Two research papers on the foundation of mathematical physics after the first trustworthy comments by an expert of quantum foundation Abstract The three key points include in the two papers: The Logic foundation of quantum mechanics has constructed from the 0-1 vector logic, if two papers can pass examination and confirmation A new constructor theory supports quantum mechanics Quantum mechanics on the Clifford algebra is rigorous and reliable Highlight s This blog briefly describes the background and conclusion of two papers published as preprints. Results The Logic Foundation of quantum mechanics QM has constructed from the 0-1 vector logic, if the two papers can pass the examination and confirmation of top experts (logic, algebra, quantum information, quantum logic, quantum group theory, quantum foundation, mathematical physics, theoretical physics, etc.) . Constructor Theory Multivariable complex vector functions are generated from the 0-1 vector logic via 0-1 vector, 0-1 variables, conjugate states, conjugate feature clusters, Octonion group, conjugate transformation structure CTS, orthogonal projection, extended logic, complementary measurement, and statistical probability operators on quantization. The multivariable complex vectors support the Clifford algebra to generate the Hilbert space, the von Neumann quantum mechanics, and the discrete Hamiltonian dynamics. A new constructor theory provides the hierarchical organization of multiple levels on whole pairs of conjugate feature vectors to support quantum mechanics from microscopic variables, conjugate states, conjugate clusters to macroscopic conjugate classifications. The constructor theory supports quantum mechanics, and the results of the two papers show that quantum mechanics on the Clifford algebra is rigorous and reliable. Removing Paradoxes In relation to a series of logic problems and paradoxes faced by quantum schools and interpretations of quantum mechanics, i.e. multiple quantum measurement paradoxes, double slit interference experiment, the EPR paradox, Schrodinger cat, Bell inequality, Hardy paradox, etc., could be resolved gradually during deeper explorations of the 0-1 vector logic … Related Background The 23rd and 24th preprints, published on the Research Square in September 16th, 2020 are the first two papers ( https://www.researchsquare.com/article/rs-76545/v1 https://www.researchsquare.com/article/rs-76524/v1 ) for my activities on the 0-1 logic for four decades to explore the theoretical research on mathematical physics. First Trustworthy Comment In the first week of the two papers online (September 21-22, 2020), Professor Barry Robson (a top expert on the Dirac operators) gave a trustworthy review with excellent comments for the two papers posted online. The key parts of his comments were truncated as follows: “ … a deeper theory of semantic structure, language and thought, … to the new approach in quantum mechanics called ‘constructor theory’. … extending Dirac dualization omega = omega+ + omega- to an extended twistor theory … to the usual i-complex octonion multiplication table … split-complex (h-complex) multiplication table, … the full table … was done and analysed by Charles Arthur Muses, …”. Briefly in his review, the proposed structure - a new discrete transformation - is represented, and suggested as a constructor theory; the CTS core corresponds to Dirac bracket pair; the Octonion group linked to the quantum research of Charles Arthur Muses, and correspondence with i-complex and h-complex of the Dirac wave functions ... (Check full comments at https://www.researchquare.com/article/rs-76524/v1 , https://www.researchsquare.com/article/rs-76545/v1 ) Extraordinary Moment At this extraordinary moment of my life, it is helpful for the sequel readers to understand the research content around the two papers to summarize the occasional surprises and intrinsic difficulties that have surrounded them for four decades, and then briefly to describe the main result of the papers. Trouble in Previous Submissions The submission paths of two original research papers on mathematical physics have been suffering all the manner. The papers have been through various national and international Journals on mathematics, logic, physics, quantum information, foundation of physics, mathematical physics and other professional academic presses for many times with experiences to carry out submissions and rejections recursively. In most cases, the manuscript stands one to two weeks after the internal review of the editorial officer, and then the decision was informed that the paper was rejected. Reason for rejection: the paper does not satisfy research directions of the Journal it is recommended to submit it to other professional magazines. The latest round of rejections on the two papers were in August 2020 to take them left in the editorial office for more than eight months. Reason for rejection: The editorial office could not find a suitable reviewer, because of the global outbreak of new coronavirus pneumonia ... Draft Paper The initial idea and key development of the first paper dates back to 26 years ago in the period of my PhD study at Monash University in Australia 1990s, where the conjugate transformation structure CTS is a chapter of the doctoral thesis . Returning to this topic again was 20 years ago, and I prepared an English monograph on variant construction 2018 for Springer Nature ( https://link.springer.com/book/10.1007/978-981-13-2282-2 ), and occasionally discovered a draft paper from historical document storages. Refined Papers After carefully translating the paper into Chinese and proofreading the key formula, I realized that various nontrivial problems cannot be ignored in various formulas of the draft document. Fortunately, associated with the continuous development and advanced research ( https://www.researchgate.net/profile/Jeffrey_Zheng ) on vector measurement tools and descriptions more than 20 years, and combining the 0-1 vector logic on variant construction established 10 years ago, the significant quantitative formulas have been developed. After translation, modification and optimization, the Chinese paper has been the core content of Chapter 4 of Volume 1 in the variant construction monograph (three volumes) officially published by Chinese Science Press in November 2020. Key contents were expanded and translated as English papers with refined cases, and structured optimization, then making a series of submission activities to relevant professional journals ... Existing Results Repeated entanglements of submission activities had a go to publish such elementary results publicly. It was grown from the 2010-2013 period that a series of visual distribution results on statistical probability were obtained on simulation algorithms in asynchronous, parallel, same time and separate time, and other control conditions to take the fine simulation of two-way interference on visual interaction, simulation models, and methods. The advanced results of this series have been published in Acta Photonica Sinica, Laser and Optoelectronics Progress, International Journal of Computation and Modelling, and Journal of Modern Optics etc. Associated with the publication of these professional Journal papers, related funding applications were submitted to the Natural Science Foundation of China for four years. Through these applications could not get the sufficient supports from the majority of review experts, however, a series of academic questions and scientific queries were collected via reviewing feedbacks of both Journal submissions and funding applications from multiple reviewers of professional experts on computational simulation, visual models and methods, quantum foundation, quantum computing, quantum information, double-slit photon interference, quantum optics, optoelectronics and other fields. Scientific Questions For example, how to confirm the correctness of these simulation results from the perspective of quantum theory? How does the controllable statistical probability distribution correspond to the result of quantum optics, optoelectronics, or double-slit interference experiment? So many clustering distributions correspond to state exhaustion projections on statistical probability procedures, from where has Schrodinger equation, Dirac equation, or Heisenberg equation been involved? ... Aim to Descriptions Since the specific topics of the elementary levels were focused in the first paper, the series of academic questions and scientific queries cannot be involved. There are no primary obstacles to answer the series of questions and strict descriptions on application levels to apply the advanced researches on mathematical physics, if the core problems can be concretely agreed from the foundational levels of elementary researches. To briefly describe this new construction, the structure describes their input and output modes among core components with possible correspondences on their classical components. It is the most importance for the most readers to obtain higher levels of structural viewpoints to search targets on a pair of bird eyes from sky to grasp the main components and mapping functionalities in the complex transformation. The Main Content of the First Paper In the first paper, the constructor theory from logic, complex number to dynamics is composed of three layers: logic (vector logic), algebra (statistical probability), and transformation (discrete dynamics). Logical layer - 0-1 vector logic The vector logical layer consists of four spaces: m+1 state space, 2n feature space, N 0-1 vector space, and 2 2n vector function space (conjugate transformation structure). m+1 state space Starting from a fixed-length m+1, m ≥ 0 as a state of m+1 bits, there are 2 m+1 states. For any 0-1 state, a feature bit is nominated at a fixed position to partition its value of 0 or 1 divided into two state collections with the same number of states, 2 m+1 states of two sets are conjugated to each other in pairs. i.e., 2 m states make up a state collection, while 2 m states make up a conjugate state collection. 2n feature space n groups 1≤ n ≤ 2 m of feature classes are made of n partitions and each partition is a feature class on the 2 m state collection, and corresponding n groups of conjugate feature classes are grouped on the 2 m conjugate state collection. There is a total number of 2n groups of feature classes on the 2 m+1 states. N 0-1 vector space For any length N 0-1 vector, N ≥ 2 m+1 , its head to tail is linked together as a ring. Under a shift operation on the vector, each m+1 bits consequently linked is a state, two closed neighbor states have m bits overlapped, and the whole bit vector can be represented as a N state vector; 2n feature classes can be used to project the state vector to generate 2n distinguished 0-1 vectors with N lengths, i.e., 2n feature vectors. 2 2n conjugate vector function space Based on 2n feature vectors, 2 2n groups of vector functions are generated by four extended vector logical operators (∩, ∪, ￢, ~) : (and, or, not, conjugate). The 2 2n vector functions form a vector logic function space. Nothing likes classical vector logic functions, if there are 2n 0-1 vectors as vector variables, the number of vector logic functions is 2 2 2n super exponential power. Since vector logic operations on feature vectors represent the complete orthogonal characteristics, most of the vector functions in classical vector algebra can be significantly reduced as a few results of simple orthogonal projections in the conjugate vector function space. All the results of the operations are contained in any combination of vector logic operators with the 2n feature vectors for the input, and this original vector logical transformation structure is the conjugate transformation structure CTS. Feature Operator Under a representation on pairs of feature vectors, the logical vector operators in each CTS can be represented as a form A, B. From the vector logic viewpoint, the CTS construction layouts the logical foundation for ensuing Dirac brackets in classical quantum dynamics. Eight Forms Based on the CTS, under the action of the five extended vector logical operators (∩, ∪, ￢, ~, ’):(and, or, not, conjugate, complementary), select two feature vector sets A,B for the original representation A,B and complementary representation A’,B’, and any feature operator A, B has eight distinguishable forms as follows: {A, B, A, B’, A’, B, A’, B’, B, A, B’, A, B, A’, B’, A’} Algebra Layer - Measurement Statistics, Complex, Clifford Algebra, Hilbert Space, von Neumann Quantum Formalization From the quantitative measurement viewpoint, any feature vectors A(A’), B(B’), under the conditions of quantitative measurement, the corresponding statistical measures are a A (a A’ ), b B (b B’ ). From the quantitative representing, two complementary measures meet the opposite of each other as equivalents: a A = - a A’ , b B = - b B’ . In the algebraic layer, any feature operator A, B has eight distinguishable measuring operators, expressed as: {(a A ,b B ), (a A ,b B’ ), (a A’ ,b B ), (a A’ ,b B’ ), (b B ,a A ), (b B’ ,a A ), (b B ,a A’ ), (b B’ ,a A’ ) }. Complex Form Using the correspondence, the paper has proofed that such a measuring operator is equivalent to a complex elementary vector: (a A ,b B ) = a A + i b B ; where i = √-1 is the imaginary number. Under the condition of statistical measurement, a list of equations shows complex vector functions to be a natural correspondence from a feature operator. Based on this vector logic foundation, multiple complex vector functions correspond to the Clifford algebra under the vector algebra. A distinguishable represent of eight operators of measures is an octonion group for transforms, each element corresponds a meta generator for the transforming group. Under the corresponding support, it is natural for multivariate complex function to use the C*-algebra to produce the Hilbert space, and then to create the quantum formalization of von Neumann. Transform Layer - Dirac Operator, Dynamic Transformation, Discrete Dynamics For the representation, the last part of the first paper uses the complicated measuring operator alpha, beta as a complex bracket to represent discrete transformation tables created by the two typical operators: beta, alpha conjugate operator ~, and alpha, - beta complex conjugate operator * on discrete differential derivations. It is convenient to be compared with the traditional discrete differentials to represent a list of conjugate and complex conjugate transformations and the classic dynamic differential formulas. There are 1-1 correspondences between the discrete differential formulas and the classic differential formulas illustrated in the series of representations. From the typical comparison, these special cases show the analytical characteristics of discrete dynamics accompanied by two transformation operators associated with complicated complex time variations. The transformation corresponding to the measuring operator alpha, beta is exactly the same as the Dirac bracket with the same functions and constraints in the combinatorial operations of the composite descriptions on general feature vectors. Conclusion for the First Paper In short, the first paper introduces a vector function space in the 0-1 vector logic, where two conjugate sets of 2n feature vectors N-length generate an orthogonal vector function space in the CTS. Finally, a statistical measurement converts a feature operator to be a complex function, and two typical transformations of complex operators, conjugate and complex conjugate, show a series of the discrete dynamic formula associated with complicated complex time variations. The Main Content of the Second Paper Compared with the first paper, the goal of the second paper is much simpler. Measuring Problem Since the Einstein and Bohr controversy presented the EPR paradox in 1935, the debate over quantum measurements involving local and global variables has continued. Associated with the discovery of the AB effect by Aharonov and Bohm in 1959 on complementary properties, Bell proposed the Bell inequality in 1964 using the local conditions of the measurement, and a series of optoelectronic measurement experiments were performed since 1971, all results showed the cycle distribution of quantum measurement theory, which was significantly different from the square boundary determined by the Bell inequality on local variables, however, the boundary of another square region based on the global variables was much larger than the cycle distribution of the quantum measurement results . Hardy Paradox Due to the conditions of local and global complementarities, Hardy summed up this type of optoelectronic measuring problems as the dilemma of local and global not. In 1992, the Hardy paradox proposed to highlight this trouble measuring condition in front of the public. Two Cases for the CTS Using the CTS on 0-1 vector extension logic operation, this paper discusses the two simplest sets of vector logic, corresponding to F 1 (A, BX) - DNF and F 0 (A, BX) - CNF formal logical expressions respectively. Using the 0-1 feature vector set, it is shown that under any pair of feature selection, the expression can be made use of neither all local variables, nor all global variables, the appropriate measuring equation always contains both local and global variables in the mixed expression. Geometric Distribution From the geometric viewpoint, this result coincides with the conditions of the cycle distribution unique to quantum measurement, i.e. neither all local variables, not all global variables. From the logical viewpoint, the CTS forms a complete support to express quantum interaction on the complex complementary variables superior than other existing quantum logic systems . Conclusion of the Blog Based on the 0-1 vector logic from 0-1 variables, states, conjugate classes, feature vectors, etc., to the conjugate transformation structure CTS, the measuring system converts a feature vector into a multivariable complex vector function with imaginary variables. Since the multivariable complex vector supports the Clifford algebra, the Hilbert space and von Neumann quantum mechanics can be created. The two papers show that quantum mechanics is rigorous and reliable on the Clifford algebra. Various measuring paradoxes, such as EPR, the Schrodinger cat, Bell inequality and Hardy paradox, could be gradually resolved following the 0-1 vector logic. In other words, the 0-1 vector logic lays the logical foundation of quantum mechanics. However, for the most critical steps, the two papers need to be recognized by high-levels of top experts on logic, mathematics, geometry, quantum information, quantum mechanics, theoretical physics, and quantum foundation etc. From the perspective of elementary academic researches through multi-disciplinary, multi angle demonstrations, accurate and exact verifications, the new system structure must be rigorous, consistent, complete, satisfied full scientific condition of advanced logical reasoning to experience the most advanced tests on mathematical physics. Looking forward for the two papers to attracting attention from international professional experts and a series of institutions and organizations for elementary researches of the frontier exploration on both advanced theories and applications. Expectation ... In the current conditions, how could we use the CTS solving certain specific problems? and could the CTS be utilized to explore quantum mechanics, quantum field theory, quantum information, quantum computing, quantum measurement, quantum interaction, quantum cryptography, quantum gravity and other advanced theoretical researches and explorations, and the latest cutting-edge technology? There are clear big gaps on specific models, systematic examples, and effective results … The two papers are merely the starting point for everyone to apply the original vector logic from the 0-1 vector logic to discrete Hamiltonian dynamics, and the analysis of local and global complementary paradoxes. The advanced theoretical theory and applied research works need to be follow-up urgently to coordinate various professional experts work together on multiple levels of basic theory and frontier application researches. Further explorations and researches can make the ice on quantum foundation for a century break a gap, which has been troubled by various logical paradoxes since the begin of the quantum theory for hundreds of years. Using the 0-1 vector logic, the CTS and relevant methods, it promotes the research and exploration of advanced quantum foundation, and opens up a new path for the global application of various cutting-edge high-tech applications in near future … References Z. J. Zheng, Conjugate Transformation of Regular Plane Lattices for Binary Images , PhD Thesis , Dep. Computer Science, Monash University 1994. Jeffrey Z.J. Zheng, Variant Construction from Theoretical Foundation to Applications , Springer Nature 2019 https://link.springer.com/book/10.1007/978-981-13-2282-2 郑智捷， 变值体系理论及其应用 ，第 1 册： 理论基础及其应用 , 科学出版社 2020 年 11 月 Zheng Zhijie , Theory and Application of Variant Construction, Volume 1: The Theoretical Foundation and Its Application , Science Press , November 2020. John von Neumann, Mathematical Foundation of Quantum Mechanics , Princeton University Press 1955 A. Einstein, B. Podolsky, N. Rosen. Can Quantum-mechanical Description of Physical Realitybe Considered Complete? Phys. Rev. 47 770-780 (1935). Y. Aharonov and D. Bohm, Significance of Electromagnetic Potentials in Quantum Theory, Physical Review 115: 485-491 (1959) J.S.Bell, On theEinstein-Podolsky-Rosen paradox. Physics 1,195(1964) https://doi.org/10.1103/PhysicsPhysiqueFizika.1.195 张永德， 量子信息物理原理 ， 科学出版社 2009, 94 页 ( 图 4.2) Zhang Yongde, Principles of Quantum Information Physics , Science Press 2009, 94 pages (Figure 4.2) L.Hardy, Quantum mechanics, local realistic theories and Lorentz-invariant realistic theories.Phys. Rev. Lett. 68, 2981 (1992) E.G. Beltrametti, G. Cassinelli, The Logic of Quantum Mechanics , Addison-Wesley Publishing Company 1981
JACS: 化学家创建分子“叶片”，将 CO 2 有效 转化为 CO（附原文） 诸平 据物理学家组织网（ Phys.org ）2017年3月8日报道，美国印第安纳大学（ Indiana University ）的化学家已经创建了一种分子“叶片”，其功能像普通的植物叶片一样，利用太阳光就可以将大气中的温室效应气体CO 2 ，转化为具有广泛应用前景的CO。众所周知，CO被氧化形成CO 2 会产生大量的能力，而要想将CO 2 再转化为CO同样需要大量能量，然而，植物叶片的光合作用则轻而易举地将 CO 2 还原为CO，并释放出氧气。 印第安纳大学（ Indiana University ）的 乔潇潇（Xiaoxiao Qiao音译）等人，他们利用纳米石墨烯-铼（Re）形成的配合物 分子与二吡啶（bipyridine）连接，可以使 CO 2 的还原反应高效顺利进行,将其转换成CO 。其实， 此“叶片”主要由2部分组成，其中纳米石墨烯部分就是太阳能捕获器，吸收太阳能；而其中的 铼 原子就是产生CO的“引擎”。纳米石墨烯捕获到的太阳能来驱动 铼 原子 的 电子 流向CO 2 ,使其转化为稳定的CO。更多信息请浏览下面的相关报道或者浏览原文。 Chemists create molecular 'leaf' that collects and stores solar power without solar panels March 8, 2017 An international team of scientists led by Liang-shi Li at Indiana University has achieved a new milestone in the quest to recycle carbon dioxide in the Earth's atmosphere into carbon-neutral fuels and others materials. The chemists have engineered a molecule that uses light or electricity to convert the greenhouse gas carbon dioxide into carbon monoxide —a carbon-neutral fuel source—more efficiently than any other method of carbon reduction. The process is reported today in the Journal of the American Chemical Society . If you can create an efficient enough molecule for this reaction, it will produce energy that is free and storable in the form of fuels, said Li, associate professor in the IU Bloomington College of Arts and Sciences' Department of Chemistry. This study is a major leap in that direction. Burning fuel—such as carbon monoxide—produces carbon dioxide and releases energy. Turning carbon dioxide back into fuel requires at least the same amount of energy. A major goal among scientists has been decreasing the excess energy needed. This is exactly what Li's molecule achieves: requiring the least amount of energy reported thus far to drive the formation of carbon monoxide. The molecule—a nanographene-rhenium complex connected via an organic compound known as bipyridine—triggers a highly efficient reaction that converts carbon dioxide to carbon monoxide. The ability to efficiently and exclusively create carbon monoxide is significant due to the molecule's versatility. Carbon monoxide is an important raw material in a lot of industrial processes, Li said. It's also a way to store energy as a carbon-neutral fuel since you're not putting any more carbon back into the atmosphere than you already removed. You're simply re-releasing the solar power you used to make it. The secret to the molecule's efficiency is nanographene—a nanometer-scale piece of graphite, a common form of carbon (i.e. the black lead in pencils)—because the material's dark color absorbs a large amount of sunlight. Li said that bipyridine-metal complexes have long been studied to reduce carbon dioxide to carbon monoxide with sunlight. But these molecules can use only a tiny sliver of the light in sunlight, primarily in the ultraviolet range, which is invisible to the naked eye. In contrast, the molecule developed at IU takes advantage of the light-absorbing power of nanographene to create a reaction that uses sunlight in the wavelength up to 600 nanometers—a large portion of the visible light spectrum. Essentially, Li said, the molecule acts as a two-part system: a nanographene energy collector that absorbs energy from sunlight and an atomic rhenium engine that produces carbon monoxide. The energy collector drives a flow of electrons to the rhenium atom, which repeatedly binds and converts the normally stable carbon dioxide to carbon monoxide. The idea to link nanographene to the metal arose from Li's earlier efforts to create a more efficient solar cell with the carbon-based material. We asked ourselves: Could we cut out the middle man—solar cells—and use the light-absorbing quality of nanographene alone to drive the reaction? he said. Next, Li plans to make the molecule more powerful, including making it last longer and survive in a non-liquid form, since solid catalysts are easier to use in the real world. He is also working to replace the rhenium atom in the molecule—a rare element—with manganese, a more common and less expensive metal. Explore further: Scientists solve puzzle of converting gaseous carbon dioxide to fuel More information: Xiaoxiao Qiao, Qiqi Li, Richard N. Schaugaard, Benjamin W. Noffke, Yijun Liu , Dongping Li, Lu Liu, Krishnan Raghavachari , and Liang-shi Li. Well-Defined Nanographene–Rhenium Complex as an Efficient Electrocatalyst and Photocatalyst for Selective CO 2 Reduction （点击可以免费下载原文） . Journal of the American Chemical Society . (2017). DOI:10.1021/jacs.6b12530 Abstract Improving energy efficiency of electrocatalytic and photocatalytic CO 2 conversion to useful chemicals poses a significant scientific challenge. We report on using a colloidal nanographene to form a molecular complex with a metal ion to tackle this challenge. In this work, a well-defined nanographene–Re complex was synthesized, in which electron delocalization over the nanographene and the metal ion significantly decreases the electrical potential needed to drive the chemical reduction. We show the complex can selectively electrocatalyze CO 2 reduction to CO in tetrahydrofuran at −0.48 V vs NHE, the least negative potential reported for a molecular catalyst. In addition, the complex can absorb a significant spectrum of visible light to photocatalyze the chemical transformation without the need for a photosensitizer.
Mini-Workshop in Seoul Center for Complex Systems Studies and CTP Nov. 7, 2015 (9:30am~5:30pm) Seoul National University, 56-106 Photos From Left: B.J. Kim (SKK University)；S. Yook (KyungHee University) J. Park (KAIST)；K.-I. Goh (Korea University) H. Jeong (KAIST)；G. Chen (City University of Hong Kong) B.-H. Wang (University of Science and Technology of China) B. Kahng (Seoul National Univ)；Z. Di (Beijing Normal University) From Left: B.J. Kim (SKK University)；S. Yook (KyungHee University) J. Park (KAIST)；K.-I. Goh (Korea University) H. Jeong (KAIST)；G. Chen (City University of Hong Kong) B.-H. Wang (University of Science and Technology of China) B. Kahng (Seoul National Univ)；Z. Di (Beijing Normal University) From Left: B.J. Kim (SKK University)；S. Yook (KyungHee University) J. Park (KAIST)；K.-I. Goh (Korea University) H. Jeong (KAIST)；G. Chen (City University of Hong Kong) From Left: G. Chen (City University of Hong Kong) B.-H. Wang (University of Science and Technology of China) B. Kahng (Seoul National Univ)；Z. Di (Beijing Normal University) From Left: B.J. Kim (SKK University)；S. Yook (KyungHee University) J. Park (KAIST)；K.-I. Goh (Korea University) H. Jeong (KAIST)；G. Chen (City University of Hong Kong) B.-H. Wang (University of Science and Technology of China) B. Kahng (Seoul National Univ)；Z. Di (Beijing Normal University)
Mini-Workshop in Seoul Program Center for Complex Systems Studies and CTP Nov. 7, 2015 (9:30am~5:30pm) Seoul National University, 56-106 Time Speaker Title Chair: B. Kahng (Seoul National Univ) 9:40~10:20 G. Chen (City University of Hong Kong) Pinning control and controllability of complex networks 10:20~10:40 Coffee Break Chair: B.J. Kim (SKK University) 10:40~11:20 K.-I. Goh (Korea University) Cascade and spreading dynamics in multiplex networks 11:20~12:00 J. Park (KAIST) Narrative flow as a dynamic model of network growth and interactions 12:00~13:40 Lunch Chair: J. Park (KAIST) 13:40~14:20 B.-H. Wang (University of Science and Technology of China) Effects of degree correlations on controllability transition in complex networks 14:20~15:00 B.J. Kim (SKK University) Group intimacy and network structure 15:00~15:40 S. Yook (KyungHee University) The origin of the criticality in meme popularity distribution on networks 15:40~16:00 Coffee Break Chair: K.-I. Goh (Korea University) 16:00~16:40 Z. Di (Beijing Normal University) Locating the source of spreading in complex networks 16:40~17:20 B. Kahng (Seoul National Univ) Hybrid phase transitions in an absortbing state: percolation and avalanche
原文网址： http://www.complexnetworks.org/ COMPLEX NETWORKS 2014 Third International Workshop on Complex Networks and their Applications November 23-27, 2014 Marrakech, Morocco Collocated with: The 10th International Conference on Signal Image Technology Internet Based Systems SITIS 2014 Submission Deadline : SEPTEMBER 06, 2014 Scope of the Workshop The international workshop on Complex Networks and their Applications aims at bringing together researchers and practitioners from different science communities working on areas related to complex networks. The workshop targets two types of contributions from prospective authors: Contributions dealing of theoretical tools and methods to solve practical problems as well as applications. Both contributions should stimulate interaction between theoreticians and practitioners. Authors are encouraged to submit both theoretical and applied papers on their research in complex networks. Topics for the workshop include, but are not limited to: • Models of Complex Networks • Structural Network Properties and Analysis • Complex Networks and Epidemics • Rumor Spreading • Community Structure in Networks • Formation of Complex Networks • Generation of Complex Networks • Community Detection in Complex Networks • Motif Discovery in Complex Networks • Visualization of Complex Networks • Complex network mining • Dynamics and evolution patterns of complex networks • Community discovery in complex social networks • Visual representation of complex networks • Methodological problems in complex network studies • Applications of complex network analysis
两本复杂系统科学(complex systems science, CSSS)的好书 两本有关 CSSS的手册，一本1万多页，一本近2000页，主编都是 Meyers A. Robert : 1. Encyclopedia of Complexity and Systems Science. 10450 pages . 我把它上传到了百度云盘，随便下： http://pan.baidu.com/share/link?shareid=315982uk=2956326610 2. Mathematics of Complexity and Dynamical Systems. 1887 pages . 下载： http://pan.baidu.com/share/link?shareid=315989uk=2956326610 第二本好像是第一本的精选。 不干其它事，专门看这两本书，估计也需要1年。
前段时间发现一本经典好书，Dynamics of Complex Systems （复杂系统的动力学）， 作者是新英格兰复杂系统研究所（New England Complex Systems Institute， NECSI）的 Yaneer Bar-yam， 这本书的电子版可以在作者的主页上下载： http://necsi.edu/publications/dcs/ 由于个人爱好的原因，想把这本书翻译成简体中文，若有版权问题，请联系我。 每次翻译几页，会在本博客上连载，当然不知道会翻译到猴年马月才能翻译完，毕竟还要编程、看论文、做实验和睡觉，能抽出来的时间不多。 Yaneer Bar-yam貌似复杂系统科学（ Complex Systems Science, CSSS ）领域的一个大牛, 一直在寻求CSSS的统一理论，他还写的有其他书，比如 Complex Engineered Systems: Science Meets Technology（2010）， Unifying Themes in Complex Systems VII （2012）等，详见： http://www.amazon.com/s/ref=ntt_athr_dp_sr_1/178-7382472-4725128?_encoding=UTF8field-author=Yaneer%20Bar-yamsearch-alias=bookssort=relevancerank Dynamics of Complex Systems 这本书有10多年了，最先好像成书于1997年，在amazon上显示2003年才出版，最初应该是讲义。 作者的个人主页： http://necsi.edu/faculty/bar-yam.html
什么是教育不公？下面的一段话也许会给出一些答案，字虽少，但句句入理： “Educational disadvantage is a complex, multi-faceted problem but at its heart lies a simple truth: a child born into a less-affluent family is statistically less likely to do well at school. That sad fact will, in turn, mean that their choices and future will be limited in ways that are deep, lasting and unjust.” 我想读完这句话，大多数的人都会深有感触，不知道什么时候才能让每个人都能有机会得到公平的受教育机会和环境，也不知道是否有正开会的“代表”们想到了这件事并递交了提案？估计是指望不上他们了，因为这么多年了，这个问题依然屹立不倒。。。
Microfluidic systems can be designed to obtain and process measurements from small volumes of complex fluids with efficiency and speed,and without the need for an expert operator; this unique set of capabilities is precisely what is needed to create portable point-of-care (POC) medical diagnostic systems 1,2 . Fortunately for the microfluidics field, the military has always had a need to practise medicine in challenging and resource-limited environments, and so has long been trying to acquire robust medical technologies that add an absolute minimum to the burden of those people and machines transporting them. It was for this reason that microfluidics research in the United States was given a great boost in the 1990s by funding from the US Defense Advanced Research Projects Agency (DARPA). The technologies developed with DARPA’s support (for examples, see www.darpa.mil/MTO/mFlumes) have the characteristics needed for delivering appropriate medical diagnostics to the world’s poorest people. Today, the potential of microfluidic technologies to enhance the decentralization of medical testing is becoming accepted as one element in the next stage in the evolution of healthcare. Thanks to an upsurge in interest in (and funding of) healthcare in the developing world, combined with a slow pace of change in the developed world, this new microfluidic diagnostic technology (MDT) may be adopted first for civilian healthcare in the developing world. Some initial steps towards designing appropriate microfluidic diagnostic systems are described here. stable gas/liquid separation method with a PTFE membrane was developed by arranging a fluidic network in three dimensions to achieve almost zero dead volume at the gas/liquid extraction part
Photos of TEIA2012 International Symposium on Tsallis Entropy and Its Applications Central China Normal University, Wuhan October 16 to18, 2012 查理斯熵国际研讨会 合影 合影 康斯坦丁诺-查理斯教授作题为《复杂性科学的统计力学基础》的主题报告 康斯坦丁诺-查理斯教授 左起：王秋平教授，蔡勖教授，Professor Tsallis 华中师大会议宴会 与蔡旭教授等华中师大物理学院领导干杯 会议宴会 与查理斯教授 干杯 华中师大会议宴会 与查理斯教授干杯 会议宴会 与查理斯教授 干杯 会议宴会 与蔡旭教授及其夫人 干杯 左起：胡进锟教授（台湾中央研究院物理研究所），蔡勖教授，汪秉宏与其夫人 蔡勖教授与夫人， 及华中师范大学复杂性科学研究所团队 查理斯教授，蔡勖教授与夫人， 及华中师范大学复杂性科学研究所的女将们
Flavonoid–metal complex is reported to exhibit a higher antioxidant activity than parent flavonoid. In this paper, experimental and theoretical methods are applied to study the antioxidant properties of quercetin and quercetin–Cr(III) complex, to find out the antioxidant activity variation and the role of Cr(III) ion on the antioxidant activity of the complex. Bond dissociation energy (BDE) and ionization potential (IP) of quercetin and the complex are calculated at the B3LYP/6-311++G(2d,2p)//B3LYP/LANL2DZ level. The experimental results show that the complex has a higher DPPH radical scavenging activity than quercetin. The calculated results show that the complex displays lower BDE and IP than quercetin. The IP of the complex declines obviously, indicating that the Cr (III) ion has more impact on the electron donating ability than on the hydrogen atom transferring ability of the complex. Antioxidant property of quercetin-Cr(III) complex: the role of Cr(III) ion.pdf
The Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) in Spain offers THREE new postdoctoral positions on Complex Systems. The first position is in complex systems and urban mobility and is offered for 1+1 years. The successful candidate will work at Palma de Mallorca in collaboration with Prof. Maxi San Miguel, Dr. Victor M. Eguiluz and Dr. Jose J. Ramasco. The topics of the work will be marked by the objectives of EUNOIA, which include the analysis of electronic information regarding human mobility, the use of agent-based simulation platforms and the collaboration with the other partners of the project. Applications will be continuously received until the position is filled - starting date may be as early as October. The second postdoc opportunity is in the area of multilayer complex networks and is offered for 1+1 years. The selected candidate will join the IFISC at Palma de Mallorca, Spain and will work in collaboration with the LASAGNE-IFISC team. The aim is to develop theoretical foundations of multilayer spatiotemporal generalized complex networks and validate the theory on real world applications. The position is available starting November 2012. Applications will be continuously received until the position is filled. The third postdoc option is for 1+1+1 years in complex systems physics . The candidate will work in Palma de Mallorca, Spain within the framework of a project of the Spanish National Science Program: COMPLEX SYSTEMS PHYSICS: Information, Technology, Society and Ecology (INTENSE@COSYP). The successful candidate will participate in the transfer of basic knowledge on complex systems to one of the following areas: 1) Information processing in complex systems; 2) Collective social phenomena and socio-technical systems; 3) Collective effects in ecosystems. The position is available starting January 2013. Applications will be continuously received until the position is filled.
2012 NCTS August Workshop on Critical Phenomena and Complex Systems Date: August 14, 2012 (Tuesday) Place: Meeting Room P101, Institute of Physics, Academia Time Speaker Title 10:20-11:00 Jonathan Dushoff (Professor, Department of Mathematics Statistics, McMaster University, Canada; E-mail: dushoff@mcmaster.ca) Relationships and routes of HIV transmission 11:00-11:40 Xiaowen Li (Professor, Department of Physics, Beijing Normal University, China; E-mail: xwli@bnu.edu.cn) An Introduction to Random Number Generators 11:40-13:50 Lunch break 13:50-14:30 Gang Hu (Professor, Department of Physics, Beijing Normal University, China; E-mail: ganghu@bnu.edu.cn) Oscillation Sources in Oscillatory Gene Regulatory Networks 14:30-15:10 Ravindra E. Amritkar (Professor, Physical Research Laboratory, India; E-mail: amritkar@prl.res.in) Amplitude death in coupled dynamical systems 15:10-15:30 Tea break 15:30-16:10 Bing-Hong Wang (Professor, Department of Modern Physics, China University of Science and Technology, China; E-mail: bhwang@ustc.edu.cn) Perspectives on Human Dynamics, Social Network Analysis, Social Cooperation and Human Evolutions 16:10-16:50 Wei-Mou Zheng (Professor, Institute of Theoretical Physics, Chinese Academy of Sciences, China; E-mail: zheng@itp.ac.cn) Mean-fields, Renormalization and Dimension of Orbits
HYPE: Hybrid modelling by composition of flows Vashti Galpin, Luca Bortolussi and Jane Hillston BCS 2011 Formal Aspects of Computing Abstract. Hybrid systems are manifest in both the natural and the engineered world, and their complex nature, mixing discrete control and continuous evolution, make it difficult to predict their behaviour. In recent years several process algebras for modelling hybrid systems have appeared in the literature, aimed at addressing this problem. These all assume that continuous variables in the system are modelled monolithically, often with differential equations embedded explicitly in the syntax of the process algebra expression. In HYPE an alternative approach is taken which offers finer-grained modelling with each flow or influence affecting a variable modelled separately.The overall behaviour then emerges as the composition of flows. In this paper we give a detailed account of the HYPE process algebra, its semantics, and its use for verification of systems. We establish both syntactic conditions (well-definedness) and operational restrictions (well-behavedness) to ensure reasonable behaviour in HYPE models. Furthermore we consider how the equivalence relation defined forHYPErelates to other relations previously proposed in the literature, demonstrating that our fine-grained approach leads to a more discriminating notion of equivalence.We present the HYPE model of a standard hybrid system example, both establishing that our approach can reproduce the previously obtained results and demonstrating how our compositional approach supports variations of the problem in a straightforward and flexible way. Keywords: Hybrid systems, Process algebra, Flows, Compositionality, Bisimulation 1. Introduction a hybrid system The structure of the rest of the paper is as follows. 1) Section 2 introduce our syntax for hybrid systems, explaining its components. In the following sections,we present the operational semantics and the hybrid semantics, explaining how we go from the notion of state to the ODEs which describe the system dynamics. We then identify the subclass of HYPE models which we consider to be well-defined and discuss how to check that a HYPE model is well-behaved. We also compare composition of models in HYPE and hybrid automata. We define a notion of bisimulation in Sect. 8 and compare it with previous bisimulations for hybrid languages from the literature. To illustrate the power of HYPE, in Sect. 10 we present an example, a train gate controller. The remaining sections discuss related work, conclusions and future research. 2. HYPE definition HYPE Hybrid modelling by composition of flows.pdf
Can the complex motions in fluid, such as Brownian motion and diffusion, be described with the exact solution of the motion equations of fluid? This problem is closely related to the famous " Millennium Prize Problems " established by the Clay Mathematics Institute of Cambridge, Massachusetts，for celebrating mathematics of new millennium. One of them is about the Navier-Stokes equation. This problem was introduced shortly and vividly in the website of the Clay Institute as follows: Waves follow our boat as we meander across the lake, and turbulent air currents follow our flight in a modern jet. Mathematicians and physicists believe that an explanation for and the prediction of both the breeze and the turbulence can be found through an understanding of solutions to the Navier-Stokes equations. Although these equations were written down in 19th Century, our understanding of them remains minimal. The challenge is to make substantial progress toward a mathematical theory which will unlock the secrets hidden in the Navier-Stokes equations. Obviously, one of the possible explorations to the this problem is to try to give an exact solution to the Euler equation (which is the simplest case of Navier-Stokes equations) for describing some complex fluid motions. In the past thirty years, several exact solutions of such kind were given, such as in , , and . But these solutions usually need some complex and unnatural external force in the Euler equation, i.e., the corresponding complex motions were driven by the complex and unnatural external force (here, the "unnatural external force" means a non-potential force). So, these solutions are somehow not quite satisfactory. From 2006 to 2008, this problem was also studied by me and my graduate students Weiwei Yu and Minghui Liu. Based on the "pseudo-potential" conception proposed by Weiwei Yu, a kind of exact solution of the Euler equation was found out. This kind of exact solution contains two arbitrary given functions and three arbitrary given parameters, and the external force of the corresponding Euler equation could be zero or any given potential force. Based on the choice of the two functions and three parameters contained in the solution, and based on the KAM theory and Melnikov Method, it is proven that the Brownian motion and diffusion of the fluid can described by the chosen exact solution. The concrete exact solutions and the sketch of the related proofs are introduced in my blog paper A Series along the Nature and Beauty in Chinese. The exact solutions and the obtained second order Melnikov function are also listed on the attached pdf file Main Mathematical Formulae in English. Main Mathematical Formulae.pdf To show the complex motion (diffusion), an animation (click on the animation to watch it) was made with the software Mathematica. In this animation, 40000 fluid particles are initially distributed to four small circles, and the four groups of particles are each dyed with a different color, so that each circle has their own unified color. The animation shows how the 40000 particles move according to the chosen exact solution, and how the four colored circles develop into four different closed curves following the fluid particles on it. It is a well known fact that if infinitely many particles are continuously distributed on the four circles, following the motions of the fluid particles, the shapes of the four circles will develop into four closed curves (homotopic to the original four circles), while the areas surrounded by them are maintained respectively, and the four closed curves will never intersect each other. This means the true diffusion (or osmosis) can not really happen if the continuity of the curves is not destroyed. However, for a practical fluid, the fluid particles are always with finite number, no matter how large the number is. So, each circles are formed with only a finite number fluid particles. When the "pseudo-continuous" curves are stretched and deformed drastically, the "continuity" of these curves will be destroyed, obviously, and the diffusion (or osmosis) will really happen between the particles distributed on the four closed curves, shown this way by the animation. The velocity field described by the chosen exact solution used for the animation is periodic both in time and in the coordinates of the two dimensional plane. It is proven by calculation that the mean value of the velocity over time and over space is zero, while the mean value of the square of the velocity is a positive number if the motion exists. Clearly, the larger the mean value of the square of the velocity is, the stronger the complex motion of the fluid is. Therefore, if the period of time and period of space are small from the view point of macro-scope, then the exact solution obtained can be treated as a module of static water with temperature which is proportional the mean value of the square of the velocity. References: T.H.Solomon and J.P. Gollub, Chaotic particle transport in time-dependent Rayleigh-Benard convection , Physical Review A. Vol.38 No. 12, (1988) 6280-6286 S. Wiggins, The dynamical systems approach to Lagrangian transport in oceanic flows , Annu. Rev. Fluid Mech. 37, (2005) 295–328. N. Malhotra and S. Wiggins, Geometric Structures, Lobe Dynamics, and Lagrangian Transport in Flows with Aperiodic Time-Dependence, with Applications to Rossby Wave Flow ,J. Nonlinear Sci. Vol. 8: pp. 401–456 (1998) Author: Keying Guan (Science College, Beijing Jiaotong University) email: keying.guan@gmail.com
Robust classification of salient links in complex networks http://www.nature.com/ncomms/journal/v3/n5/pdf/ncomms1847.pdf?WT.ec_id=NCOMMS-20120529#/author-information
001-Nature2012_Stability_criteria_for_complex_ecosystems .pdf Stability criteria for complex ecosystems Stefano Allesina 1,2 Si Tang 1 Forty years ago, May proved 1,2 that sufficiently large or complex ecological networks have a probability of persisting that is close to zero, contrary to previous expectations 3 – 5 . May analysed large networks in which species interact at random 1,2,6 . However, in natural systems pairs of species have well-defined interactions (for example predator – prey, mutualistic or competitive). Here we extend May ’ s results to these relationships and find remarkable differences between predator – prey interactions, which are stabilizing, and mutualistic and competitive interactions, which are destabilizing. We provide analytic stability criteria for all cases. We use the criteria to prove that, counterintuitively, the probability of stability for predator – prey networks decreases when a realistic food web structure is imposed 7,8 or if there is a large preponderance of weak interactions 9,10 . Similarly, stability is negatively affected by nestedness 11 – 14 in bipartite mutualistic networks. These results are found by separating the contribution of network structure and interaction strengths to stability. Stable predator – prey networks can be arbitrarily large and complex, provided that predator – prey pairs are tightly coupled. The stability criteria are widely applicable, because they hold for any system of differential equations. May ’ s theorem deals with community matrices 1,2,6 M , of size S 3 S , where S is the number of species. M ij describes the effect that species j has on i around a feasible equilibrium point (that is, species have positive densities) of an unspecified dynamical system describing the species ’ densities through time. In May ’ s work 1,2 , the diagonal coefficients are 2 1, and the offdiagonal coefficients are drawn from a distribution with mean 0 and variance s 2 with probability C and are 0 otherwise. For these matrices, the probability of stability is close to 0 whenever the ‘ complexity ’ s p ffi S ffiffi C ffiffiffi w 1. Local stability measures the tendency of the system to return to equilibrium after perturbations. In unstable systems, even infinitesimal perturbations cause the system to move away from equilibrium, potentially leading to the loss of species. Thus, it should point is stable if all the eigenvalues of the community matrix have Local stability can only describe the behaviour of the system around an equilibrium point, whereas natural systems are believed to operate far from a steady state 5,15 .However, methods based on local stability are well suited to the study of large systems 1,16,17 , whose empirical parameterization would be unfeasible. Moreover, the methods are general, so that they can be applied to any system of differential equations. May ’ s matrices have random structure: each pair of species interacts with the same probability. However, this randomness translates, for large S , into fixed interaction frequencies, so that these matrices follow a precise mixture of interaction types. For example, in May ’ s matrices predator – prey interactions are twice as frequent as mutualistic ones (Supplementary Table 1). Here we extend May ’ s work to different types of interaction, starting from the random case. Suppose that two species j and i interact with probability C , and that the interaction strength is drawn from a distribution: M ij takes the value of a random variable X with mean E e X T ~ 0 and variance Var( X ) 5 s 2 . The diagonal elements of the community matrix, representing self-regulation, are set to 2 d . For large systems, the eigenvalues are contained in a circle 18 in the complex plane (Fig. 1 and Supplementary Information). The circle is centred at ( 2 d , 0) and the radius is s ffiffiffiffiffiffi p SC . In stable systems, the whole circle is contained in the left halfplane (that is, all eigenvalues have negative real parts). Thus, the system is stable when the radius is smaller than d : ffiffiffiffiffiffi p SC v h ~ d = s . In predator – prey networks, interactions come in pairs with opposite signs: whenever M ij . 0, then M ji , 0. With probability C ,we sampleone interaction strength from the distribution of j X j and the other from 2 j X j , whereas with probability (1 2 C ) both are zero. The eigenvalues of large predator – prey matrices are contained in a vertically stretched ellipse 19 , centred at ( 2 d , 0), with horizontal radius s ffiffiffiffiffiffi p SC 1 { E 2 ej X jT _ s 2 _ _ and thus the stability criterion is ffiffiffiffiffiffi p SC v h _ 1 { E 2 ej X jT = s 2 _ _ (Fig. 1 and Supplementary Information). When we constrain M ij and M ji to have the same sign, and thus impose amixture of competition andmutualismwith equal probability, the eigenvalues are enclosed in a horizontally stretched ellipse 19 and the criterion becomes ffiffiffiffiffiffi p SC v h _ 1 z E 2 ej X jT = s 2 _ _ (Fig. 1 and Supplementary Information). Take C 5 0.1, X , N (0, 1/4) (that is, X follows a normal distribution with mean 0 and variance 1/4), and d 5 1. The criterion becomes ffiffiffiffiffiffi p SC v 2 for random matrices, and is violated whenever S $ 41. For predator – prey we find ffiffiffiffiffiffi p SC v 2 p = e p { 2 T (violated for S $ 303) and for the mixture of competition and mutualism ffiffiffiffiffiffi p SC v 2 p = e p z 2 T (violated for S $ 15). Since E ej X jT = s w 0 for any distribution of X , the stability criteria forma strict hierarchy in which the mixture matrices are the least likely to be stable, the random matrices are intermediate, and the predator – prey matrices are the most likely to be stable (Fig. 2 and Table 1). Considerations based on qualitative stability 2 and numerical simulations 16 are consistent with this hierarchy. In the three cases above, the mean interaction strength is zero, and the coefficients come from the same distribution. In fact we can shuffle the interaction strengths, thereby transforming a network of one type into another: the difference in stability is driven exclusively by the arrangement of the coefficients in pairs with random, opposite and same signs, respectively. This feature allows us to further derive the stability criteria for all intermediate cases by using linear combinations of the three cases above (Supplementary Information). Two ecologically important cases, however, cannot produce a mean interaction strength of zero. In mutualistic networks all interactions are positive, whereas in competitive networks they are negative. In these cases, for large systems, all the eigenvalues except one (equal to the row sum) are contained in an ellipse (Fig. 3 and Supplementary Figs 1 and 2). In mutualistic networks in which all interaction pairs are positive and drawn from the distribution of j X j independently with probability C , the stability criterion becomes e S { 1 T C E ej X jT = s v h (that is, row sum , 0; Supplementary Information). For competitive matrices, in which interaction pairs are drawn from the distribution of 2 j X j with probability C , the criterion is ffiffiffiffiffiffi p SC 1 z e 1 { 2 C T E 2 ej X jT = s 2 _ __ ffi 1 ffiffi { ffiffiffiffiffi C ffiffiffi E ffiffi 2 ffiffi e ffiffi j ffiffi X ffiffiffi j ffi T ffiffi = ffiffi s ffiffiffiffiffi 2 q z C E ej X jT = s v h 1 Department of Ecology and Evolution, University of Chicago, 1101 East 57th Street, Chicago, Illinois 60637, USA. 2 Computation Institute, University of Chicago, 5735 South Ellis Avenue, Chicago, Illinois 60637, USA. 8 MARCH 2012 | VOL 483 | NATURE | 205 2012 Macmillan Publishers Limited. All rights reserved (Supplementary Information). In both cases, stability decreases rapidly with higher complexity, and mutualistic matrices are less likely to be stable than their competitive counterpart (Fig. 2 and Table 1). Having derived the stability criteria, we want to assess the effect of imposing realistic foodweb structurewithin the predator – prey case. It is believed that realistic food web structures should improve stability 7,8,17 . In community matrices of food webs, producers have positive columns and negative rows, with the opposite for top predators. To test whether these variations affect stability, we plotted the eigenvalues for predator – prey webs in which interactions are arranged, following the cascade 20 and niche 21 models. Imposing realistic structures results in eigenvalues with larger real parts than the corresponding unstructured predator – prey case (Supplementary Information and Supplementary Fig. 3). Thus, the cascade and nichemodels produce networks that are less likely to be stable than their unstructured predator – prey counterpart, with the niche model having a larger discrepancy: imposing realistic food web structure hampers stability. Similarly, wemeasured the effect of realistic structures onmutualistic networks. Several published mutualistic networks are bipartite 11 – 14 : there are two types of node (for example plants and pollinators), and interactions occur exclusively between different types. In addition, bipartite mutualistic networks tend to be nested 11 : the interactions of the specialists form a subset of those of the generalists. Nestedness is believed to beget stability 12 – 14 .We plotted the eigenvalues for these two types of structure and compared the results with those obtained for the unstructured mutualistic case (Fig. 3, Supplementary Information and Supplementary Fig. 4).As stated above, stability inmutualistic networks is determined by the row sum. The bipartite case yields row sums that, − 20 − 10 0 10 20 − 20 − 10 Mixture Figure 1 | Distributions of the eigenvalues and corresponding stability profiles. a , For X , N (0, s 2 ), S 5 250, C 5 0.25 and s 5 1, we plot the eigenvalues of 10 matrices (colours) with 2 d 52 1 on the diagonal and offdiagonal elements, following the random, predator – prey or mixture prescriptions. The black ellipses are derived analytically in the text. b , Numerical simulations for the corresponding stability profiles. For the random case, starting from S 5 250, C 5 0.5, s 5 0.1 and d 5 1, we systematically varied C (crosses) or s (plus signs) to obtain s ffiffiffiffiffiffi p SC spanning of the critical value for stability (indicated in red, 1 in the case of random matrices). The profiles were obtained by computing the probability of stability out of 1,000 matrices. The predator – prey case is as the random but with s 5 0.5 and critical value p /( p 2 2). The mixture case is as the random but with critical value p /( p 1 2). In all cases, the phase transition between stability and instability is accurately predicted by our derivation. that satisfies the stability criterion for each type of interactions. Combinations of S and C below each curve lead to stable matrices with a probability close to 1. The interaction types forma strict hierarchy frommutualism (most unlikely to be stable) to predator – prey (most likely to be stable). for large S , are equal to the unstructured case. Accordingly, we did not find a discrepancy in stability for the bipartite case. However, in nested structures some rows and columns have sums that are larger than average (generalist plants and animals). Consequently, nested matrices are inherently less likely to be stable than unstructured ones. These findings are confirmed by numerical simulations. Using the same method, we found that asymmetric coupling of interaction strengths (where each large M ij is coupled with a small M ji ), contrary to current expectations 22 , does not influence stability in mutualistic networks (Supplementary Information and Supplementary Fig. 5). We have considered how the arrangement of the interactions affects stability, and have found several counterintuitive results. These results can be accounted for by the fact thatwe provide a very conservative test for the effects of structure on stability (Supplementary Information). We now assess the role of the magnitude of interaction strengths. In fact, our findings extend to any distribution of coefficient strengths (Supplementary Information). Typically, ecologists have regarded s as the ‘ average interaction strength ’ 1,2 . However, s does not provide information on weak interactions 9,10,17 : we can have the same s for two distributions with distinct shapes, and thus different proportions of weak and strong interactions (Supplementary Information). We analyse how the shape of the distribution affects stability for fixed S , C , d and s . If the distribution contains many weak interactions, the expected magnitude E ej X jT 0. In contrast, if weak interactions are rare, E ej X jT s . In the predator – prey systems, lowering E ej X jT decreases h _ 1 { E 2 ej X jT = s 2 _ _ and thus hampers stability. We conclude that weak interactions, contrary to current beliefs 9,10,17 , can destabilize predator – prey systems. Weakening the interactions shifts E ej X jT closer to zero and therefore makes predator – prey systems closer to their random counterpart. With the same argument, weak interactions can stabilize the mixture of competition and mutualism case and have no effect on random networks. Variability in interaction strengths was previously found to be detrimental for stability in large food webs 23 and competitive networks 17 . For example, consider a uniform distribution X , U { s ffiffiffi p 3 , s ffiffiffi p 3 and contrast it with the normal case X , N (0, s _ _ 2 ). Both parameterizations lead to E e X T ~ 0 and Var( X ) 5 s 2 . In the uniform case, E ej X jT ~ s ffiffiffi p 3 _ 2 0 : 866 s , whereas in the normal case E ej X jT ~ s ffiffiffiffiffiffiffiffi 2 = p p 0 : 798 s . This means that the uniform distribution, on average, leads to stronger interactions than the corresponding normal case. In turn, this has a large effect on stability: the criterion for the predator – prey case becomes ffiffiffiffiffiffi p SC v 4 h for the uniform distribution, whereas it is ffiffiffiffiffiffi p SC v p = e p { 2 T h 2 : 75 h for the normal case. The random case is unaffected by the choice of the distribution ( ffiffiffiffiffiffi p SC v h ), whereas in the mixture of competition and mutualism we have ffiffiffiffiffiffi p SC v 4 h = 7 0 : 571 h for the uniform distribution and ffiffiffiffiffiffi p SC v p h = e p z 2 T 0 : 61 h for the normal case. These considerations extend to any choice of distribution for the interaction strengths (Supplementary Information and Supplementary Figs 6 and 7): weak interactions, all other things being equal, are destabilizing for food webs, stabilizing for mutualistic and competitive networks (and their mixture), and have no effect on random networks. Table 1 | Stability criteria for different types of interaction and network structure S max ( C , h ) Interaction Stability criterion (0.1, 2.0) (0.1, 4.0) (0.2, 4.0) Nested mutualism 9 28 18 Mutualism Figure 3 | Distribution of the eigenvalues for the three types of mutualism. a , Unstructured mutualism. b , Bipartite mutualism. c , Nested and bipartite mutualism. In all cases, S 5 250, s 5 0.1, C 5 0.2 and d 5 1. Note that the bipartite case does produce extreme negative real eigenvalues (green arrow) coupled with positive ones, but the rowsum(and thus the rightmost eigenvalue, red arrow) is equal to that of the unstructured mutualistic case. The nested matrices, in which generalist species yield (on average) larger row and column sums, have larger rightmost eigenvalues. Thus, highly nested matrices are less likely than the other two cases to be stable. LETTER RESEARCH 8 M A R C H 2 0 1 2 | VO L 4 8 3 | N AT U R E | 2 0 7 2012 Macmillan Publishers Limited. All rights reserved We have derived stability criteria for unstructured networks in which species interact at random, in predator – prey, mutualistic, and competitive pairs. These results hold for arbitrary diagonal values and arbitrary distribution of interaction strengths (Supplementary Information). Our analysis shows that, all other things being equal, weak interactions can be either stabilizing or destabilizing depending on the type of interactions between species. In predator – prey systems, realistic structure and weak interactions are detrimental for stability. However, in natural food webs, which seem to persist in time, weak interactions are preponderant 24 . The persistence of these networks might be explained by the interplay between their structure and weak interactions, even though each would be destabilizing if taken in isolation. For example, as suggested previously 2 , generalist predators could have weak interactions with their numerous prey, reducing the effect of the realistic structure and driving the system closer to the unstructured case. Predator – prey systems differ markedly from the other cases studied here. Suppose that a network is unstable. The system can be stabilized either by lowering C , S or s (decreasing its complexity), or by increasing the self-regulation d . This is in line with May ’ s argument: large and highly interconnected systems are difficult to stabilize. For random networks, reducing complexity is the only way to stabilize the system. However, in the other cases, networks can be stabilized by altering the distribution of interaction strengths; by modifying the parameters of the system we can typically change the distribution of the off-diagonal elements without altering the diagonal ones (Supplementary Information). For competition, mutualism and their mixture, stability is achievable by decreasing the average interaction strength E ej X jT , which is akin to lowering complexity. On the contrary, predator – prey networks can be stabilized by increasing the strength of interaction E ej X jT , and thus the coupling between predators and prey. Predator – prey systems are therefore the only ones that can potentially elude May ’ s conclusions 1,2 and support an arbitrarily large, complex and stable ecological network. Our results show that the ubiquity of consumer – resource relationships in nature could be due to their intrinsic dynamical properties. These findings are not limited to ecological networks, but instead hold for any system of differential equations resting at an equilibriumpoint. Received 18 May 2011; accepted 6 January 2012. Published online 19 February 2012. 1. May, R. M. Will a large complex system be stable? Nature 238, 413 – 414 (1972). 2. May, R. M. Stability and Complexity in Model Ecosystems (Princeton Univ. Press, 2001). 3. MacArthur, R. Fluctuations of animal populations and a measure of community stability. Ecology 36, 533 – 536 (1955). 4. Elton, C. S. Animal Ecology (Univ. of Chicago Press, 2001). 5. McCann, K. S. The diversity – stability debate. Nature 405, 228 – 233 (2000). 6. Levins, R. Evolution in Changing Environments: Some Theoretical Explorations (Princeton Univ. Press, 1968). 7. McNaughton, S. J. Stability and diversity of ecological communities. Nature 274, 251 – 253 (1978). 8. Yodzis, P. The stability of real ecosystems. Nature 289, 674 – 676 (1981). 9. McCann,K.S.,Hastings, A.Huxel,G.R.Weak trophic interactions and the balance of nature. Nature 395, 794 – 798 (1998). 10. Emmerson, M. Yearsley, J. M. Weak interactions, omnivory and emergent foodweb properties. Proc. R. Soc. Lond. B 271, 397 – 405 (2004). 11. Bascompte, J., Jordano, P., Melia ′ n, C. J. Olesen, J. M. The nested assembly of plant – animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383 – 9387 (2003). 12. Okuyama, T. Holland, J.N. Network structural propertiesmediate the stability of mutualistic communities. Ecol. Lett. 11, 208 – 216 (2008). 13. Bastolla, U. et al. The architecture ofmutualistic networks minimizes competition and increases biodiversity. Nature 458, 1018 – 1020 (2009). 14. The ′ bault, E.Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853 – 856 (2010). 15. DeAngelis, D. L. Waterhouse, J. C. Equilibrium and nonequilibrium concepts in ecological models. Ecol. Monogr. 57, 1 – 21 (1987). 16. Allesina, S. Pascual,M. Network structure, predator – prey modules, and stability in large food webs. Theor. Ecol. 1, 55 – 64 (2008). 17. Gross, T., Rudolf, L., Levin, S. A. Dieckmann, U. Generalized models reveal stabilizing factors in food webs. Science 325, 747 – 750 (2009). 18. Tao, T., Vu, V. Krishnapur, M. Random matrices: universality of ESDs and the circular law. Ann. Probab. 38, 2023 – 2065 (2010). 19. Sommers, H. J., Crisanti, A., Sompolinsky, H. Stein, Y. Spectrum of large random asymmetric matrices. Phys. Rev. Lett. 60, 1895 – 1898 (1988). 20. Cohen, J.E., Briand, F.,Newman, C. M.Palka, Z. J. Community FoodWebs: Data and Theory (Springer, 1990). 21. Williams, R. J. Martinez, N. D. Simple rules yield complex foodwebs. Nature 404, 180 – 183 (2000). 22. Bascompte, J., Jordano, P. Olesen, J. M. Asymmetric coevolutionary networks facilitate biodiversity maintenance. Science 312, 431 – 433 (2006). 23. Kokkoris, G. D., Jansen, V. A. A., Loreau,M.Troumbis, A. Y. Variability in interaction strength and implications for biodiversity. J. Anim. Ecol. 71, 362 – 371 (2002). 24. Wootton, J. T. Emmerson, M. Measurement of interaction strength in nature. Klausmeier, S.P. Lalley, R. M.May, K. S. McCann,M.Novak, P.P.A. Staniczenko and J.D. Yeakel for comments and discussion. This researchwas supported byNational Science Foundation grant EF0827493. www.nature.com/reprints . The authors declare no competing financial interests. Readers are welcome to comment on the online version of this article at www.nature.com/nature. Correspondence and requests for materials should be addressed to S.A. (sallesina@uchicago.edu). RESEARCH LETTER 2 0 8 | N AT U R E | VO L 4 8 3 | 8 M A R C H 2 0 1 2 2012 Macmillan Publishers Limited. All rights reserved
Design of a Water-Soluble Hybrid Nanocomposite of CdTe Quantum Dots and an Iridium Complex for Photoinduced Charge Transfer Yu Wang, Steve Li, StephenV. Kershaw*, Frederik Hetsch, AnthonyY.Y. Tam, Guangcun Shan, AndreiS. Susha, Chi-Chiu Ko, Vivian Wing-WahYam, KennethK.W. Lo*, AndreyL. Rogach Yu Wang, Dr. Steve Li, Dr. Stephen V. Kershaw, Frederik Hetsch, Anthony Y. Y. Tam, Guangcun Shan, Dr. Andrei S. Susha, Prof. Chi-Chiu Ko, Prof. Vivian Wing-Wah Yam, Prof. Kenneth K. W. Lo and Prof. Andrey L. Rogach Article first published online: 12 APR 2012 | DOI: 10.1002/cphc.201101005 Fast electron transfer : Picosecond charge transfer between CdTe quantum dots and a novel water-soluble organo-Ir dye is shown. This process allows hot electron transfer from the nanoparticles to the organic component before cooling is complete and before Auger relaxation processes can compete for the carrier’s excess energy. Understanding and controlling fast charge transfer is a key to making improved solar-energy conversion devices. Keywords: charge transfer; dyes; iridium; quantum dots; time-resolved spectroscopy
1 Mapping As You Go: An Effective Approach for Marker-Assisted Selection of Complex Traits Mapping As You Go.pdf 这是比较早的一篇了,前两天有朋友问起,我把它从我的文件夹中抓出来了,时间长了不看会忘记的.这篇文章说明MAS既可以针对简单性状也可以针对复杂性状.复杂性状,故名思议方法也更复杂.但不幸的是很多重要农艺性状都是复杂性状.如果复杂性状的分子标记辅助选择的理论和技术都成熟了,而且实施成本比较低,那么分子育种的黄金时代将真正到来.所以这个帖子专门搜集和复杂性状的分子标记辅助育种相关主题 2 genomic selection (GS) 这个概念是在翻译徐老师下面一篇文章时,才弄明白的.GS的关键在于利用覆盖全基因组的标记估计育种值,所用的标记既有个性状显著关联的,也有和性状不关联的.一般分子标记辅助选择的标记都是和性状显著关联的(当然背景标记除外). 3 Whole-genome strategies for marker-assisted plant breeding http://www.springerlink.com/content/a6566g4672171056/ 这是徐老师关于植物分子育种的最新综述.这里提出了全基因组策略的概念.根据我个人理解全基因组策略不是一种简单的分子标记辅助选择方法.根据文中的定义:" 全基因组策略可以定义为：植物全基因组水平分子育种需要的功能工具和方法的全面集成。 The whole-genome strategies can be defined as a full package of functional tools and methodologies required for molecular plant breeding at the level of the whole genome ".呵呵,即使新建立的方法也可以往这个框架里装.不过这篇文章中他提出了一个新概念叫E-TYPING.这个E-TYPING有点意思.常规育种只对表型进行选择,对特定的性状而言这只是个一维的线性概念,因为只有表型值的变化;而分子标记辅助育种,可以把基因型加进来,基因型和表型,这变成了两维的概念,两维可以构成一个平面,在加上E-TYPING的结果,又增加了一维,是一个立体的概念.从线性到立体反应了一种育种的趋势,或者是育种方式的革命性变化.由简单的表型选择,到基于基因型的表型选择,到适合特定环境的基于基因型的表型选择.不过,现在第2步可能做得也还不尽完美.
Modeling and mining of dynamic trust in complex service-oriented systems Florian Skopik, Daniel Schall,Schahram Dustdar Information Systems 35 (2010)735–757 a b s t r a c t The global scale and distribution of companies have changed the economyand dynamics of businesses. Web-based collaborations and cross-organizational processes typically require dynamic and context-based interactions between people and services. However,finding the right partner to work on joint tasks or to solve emerging problems in such scenarios is challenging due to scale and temporary nature of collaborations. Furthermore, actor competencies evolve over time, thus requiring dynamic approaches for their management. Web services and SOA are the ideal technical framework to automate interactions spanning people and services. To support such complex interaction scenarios, we discuss mixed service-oriented systems that are composed of both humans and software services, interacting to perform certain activities. As an example, consider a professional online support community consisting of interactions between human participants and software-based services. We argue that trust between members is essential for successful collaborations. Unlike a security perspective, we focus on the notion of social trust in collaborative networks. We show an interpretative rule-based approach to enable humans and services to establish trust based on interactions and experiences, considering their context and subjective perceptions Keywords: Collaborative environment Service-orientation Social trust Interaction patterns Flexible composition Crowdsourcing 1. Introduction Services: -- Pervasiveness -- context-awareness --adaptiveness interactions: trust the focus of this paper: how much humans or other systems can rely on services to accomplish their tasks. SOA: a centralized trust management approach -- advantages: -- shortcoming the key contributions of this paper: -- Social andbehavioraltrustmodel. -- VieTE framework. -- Evaluation anddiscussion. the organization of this paper: 1) Section 2 introduce the Expert Web case showing the need for flexible expert discovery and involvement. 2) Section 3 introduce trust concepts in collaborative environments since author trust model is based on social trust. 3) Section 4 details the concept of interaction-based behavioral trust which will be the basis for our trust inference model. 4) Section 5show author's trust model established on fuzzy set theory. 5)Section 6 formalizes the fundamental trust model relying on captured and interpreted interactions. 6)Section 7 present a balancing approach to prevent inefficient interactions. 7) Section 8 depicts the implementation details of the systemin. 8) Section 9 deals with evaluations to test the performance of the presented system as well as effectiveness of balancing algorithms. 9) Finally, Section 10 discuss related work in the area of SOA, social trust, and flexible interactions models 2. Service-orientedcollaborations a scenario showing discovery of experts and flexible interaction support as depictedin Fig. 1 trusted experts The Expert Web: 3. Communication,coordination,and composition 3.1. Social trust in collaborations social trust: trust definition: Multi-faceted trust: Compositional trust: 3.2. The cycle of trust 4. From interactions to social trust three layer approach in Figure 2: 4.1. Interaction layer 4.1.1. Collaboration data 4.1.2. Context-aware interaction observation 4.1.3. Interaction metrics and scopes 4.2. Personalized trust inference 4.3. Trust projection layer 5. Fuzzy set theory for trust inference 6. Trust model definitions 6.1. Fundamental trust model 6.2. Temporal evaluation 6.3. Trust projection 7. Towards flexible compositions 8. Architecture and implementation 9. Evaluation and discussion 10. Background and related work trust vs.quality ofservice (QoS) the cold start problem: 11. Conclusion and further work I comments: this paper invent many concepts but do not slove the real problem. Modeling and mining of dynamic trust in complex service-oriented systems.pdf
Contents 1 Subgradient 2 subgradient http://www.stanford.edu/~boyd/ the projected subgradient method Subgradient Methods.pdf subgrad_method.pdf Subgradient Methods Stephen Boyd, Lin Xiao, and Almir Mutapcic Notes for EE392o, Stanford University, Autumn, 2003, October 1, 2003 Abstract: The subgradient method is a simple algorithm for minimizing a nondifferentiable convex function. The method looks very much like the ordinary gradient method for differentiable functions, but with several notable exceptions. For example, the subgradient method uses step lengths that are ˉfixed ahead of time, instead of an exact or approximate line search as in the gradient method. Unlike the ordinary gradient method, the subgradient method is not a descent method; the function value can (and often does) increase. The subgradient method is far slower than Newton's method, but is much simpler and can be applied to a far wider variety of problems. By combining the subgradient method with primal or dual decomposition techniques, it is sometimes possible to develop a simple distributed algorithm for a problem. The subgradient method was originally developed by Shor in the Soviet Union in the 1970s. The basic reference on subgradient methods is his book . Another book on the topic is Akgul . Bertsekas is a good reference on the subgradient method, combined with primal or dual decomposition. subgrad_method.pdf *** ##凸分析 史树中 book
"Any intelligent fool can make things bigger, more complex, and more violent. It takes a touch of genius -- and a lot of courage -- to move in the opposite direction."
Bothered by recent simulations, I realize there are some complex problems about setting up parameters in stochastic interactions. So, how about changing the way of thinking and focusing on one essential point, it is to investigate the heterogeneous of the network by stochastic interactions.
i.e. 是拉丁文 id est 的缩写，它的意思就是“那就是说，换句话说”，等同于“that is，in other words” ，目的是用来 进一步 解释前面所说的观点。 e.g. 是拉丁文 exempli gratia 的缩写，它的意思是“举个例子，比如”，等同与“for example”,目的就是用几个例子来说明前面的观点。 etc.就比较好理解了，它是 etcetera 的缩写，意思是“等等”，相当于“and so on” e.g. 和 etc. 不能出现在同一句话中，因为 e.g. 是表示泛泛的举几个例子，并没有囊括所有的实例，其中就已经包含“等等”，如果再加一个 etc. 就画蛇添足了，例如下面这句话就是错的： Writing instructors focus on a number of complex skills that require extensive practice (e.g., organization, clear expression, logical thinking, etc.)
Dynamic Days Asia Pacific 7 (DDAP7) The 7th International Conference on Nonlinear Science Academia Sinica, Taipei, Taiwan, 6 August (Monday)-9 August 2012 General Information: Dynamic Days Asia Pacific (DDAP) is a regular series of international conferences rotating among Asia-Pacific countries every two years in recent years. Its purpose is to bring together researchers world-wide to discuss the most recent developments in nonlinear science. It also serves as a forum to promote regional as well as international scientific exchange and collaboration. The conference covers a variety of topics in nonlinear physics, biological physics, nonequilibrium physics, complex networks, econophysics, and quantum/classical chaos, etc. DDAP1 started in 1999 in Hong Kong, then continued in Hangzhou (DDAP2, 2002), Singapore (DDAP3, 2004), Pohang (DDAP4, 2006), Nara (DDAP5, 2008) and Sydney (DDAP6, 2010). DDAP7 will take place at Academia Sinica in Taipei, Taiwan on 6-9 August 2012. Plans for the 8th to the 9th DDAP are scheduled for India (2014) and Hong Kong (2016). Information for some former conferences: DDAP6: University of New South Wales, Sydney, Australia, 12-14 July 2010 http://conferences.science.unsw.edu.au/DDAP6/DDAP6.html DDAP5: Nara Prefectural New Public Hall, Nara, Japan, 9-12 September 2008 http://minnie.disney.phys.nara-wu.ac.jp/~toda/ddap5/ DDAP4: Pohang University of Science and Technology, Pohang, Korea, 12-14 July 2006 http://www.apctp.org/topical/ddap4/ DDAP3: National University of Singapore, Singapore, 30 June-2 July 2004 http://www.cse.nus.edu.sg/com_science/story/body.html DDAP2: Zhejian University, HangZhou, China, 8-12 August 2002 http://physics.zju.edu.cn/note/dispArticle.Asp?ID=132 DDAP1: Hong Kong Baptist University, Hong Kong, 13-16 July 1999 http://www.hkbu.edu.hk/~ddap/ Topics of the conference Chaos Pattern formation Econophysics Complex networks Protein folding and aggregation etc Organization Committee (OC) Chin Kun Hu* (huck@phys.sinica.edu.tw ) Academia Sinica: Chairperson Ming-Chya Wu* (mcwu@phys.sinica.edu.tw) National Central University: Secretary Chi Keung Chan* (ckchan@gate.sinica.edu.tw) Academia Sinica Cheng-Hung Chang (chchang@mail.nctu.edu.tw) National Chiao Tung University Chi-Ming Chen (cchen@phy.ntnu.edu.tw) National Taiwan Normal University Chi-Ning Chen (cnchen@mail.ndhu.edu.tw) National Dong Hwa University Hsuan-Yi Chen* (hschen@phy.ncu.edu.tw) National Central University Yeng-Long Chen* (yenglong@phys.sinica.edu.tw) Academia Sinica Yih-Yuh Chen (yychen@phys.ntu.edu.tw) National Taiwan University Chung-I Chou (cichou@faculty.pccu.edu.tw ) Chinese Culture University Lin-Ni Hau (lnhau@jupiter.ss.ncu.edu.tw) National Central University Ming-Chung Ho (t1603@nknucc.nknu.edu.tw) National Kaohsiung Normal University Tzay-Ming Hong (ming@phys.nthu.edu.tw) National Tsing Hua University Ding-wei Huang (dwhuang@cycu.edu.tw) Chung-Yuan Christian University Ming-Chang Huang (ming@phys.cycu.edu.tw) Chung-Yuan Christian University Kwan-Tai Leung* (leungkt@phys.sinica.edu.tw) Academia Sinica Sai-Ping Li* (spli@phys.sinica.edu.tw) Academia Sinica Sy-Sang Liaw (liaw@phys.nchu.edu.tw) National Chung Hsing University Chai-Yu Lin (lincy@phy.ccu.edu.tw) National Chung Cheng University Hsiu-Hau Lin (hsiuhau@phys.nthu.edu.tw) National Tsing Hua University Chun-Yi David Lu (cydlu@ntu.edu.tw) National Taiwan University Wen-Jong Ma* (mwj@nccu.edu.tw) National Chengchi University Ning-Ning Pang (nnp@phys.ntu.edu.tw) National Taiwan University Yuo-Hsien Shiau (yhshiau@nccu.edu.tw) National Chengchi University Chi-Tin Shih (ctshih@thu.edu.tw ) Tunghai University Hsen-Che Tseng (tseng@phys.nchu.edu.tw) National Chung Hsing University Wen-Jer Tzeng (wjtzeng@mail.tku.edu.tw) Tamkang University Zicong Zhou (zzhou@mail.tku.edu.tw ) Tamkang University *Members of Local Organization Committee International Advisory Committee (IAC) Asia-Pacific Moo Young Choi (Seoul National University, mychoi@snu.ac.kr) Robert Dewar (The Australian National University, robert.dewar@anu.edu.au) Bruce Henry (University of New South Wales, b.henry@unsw.edu.au) Gang Hu (Beijing Normal University, ganghu@bnu.edu.cn) Pak Ming Hui (The Chinese University of Hong Kong, pmhui@phy.cuhk.edu.hk) Byungnam Kahng (Seoul National University, bkahng@snu.ac.kr) Kunihiko Kaneko (The University of Tokyo, kaneko@complex.c.u-tokyo.ac.jp) Seunghwan Kim (APCTP, Pohang, swan@postech.ac.kr) Yuri S. Kivshar (The Australian National University, ysk124@physics.anu.edu.au) Takahisa Harayama (ATR Wave Engineering Laboratories, harayama@atr.jp) Yoshiki Kuramoto (Kyoto University, kuramoto@kurims.kyoto-u.ac.jp) Choy-Heng Lai (National University of Singapore, phylaich@nus.edu.sg) Baowen Li (National University of Singapore, phylibw@nus.edu.sg) Bing Hong Wang (China Univ of Science Technology, bhwang@ustc.edu.cn) Po Zheng (Zhejiang University, bozheng@zju.edu.cn) Zhigang Zheng (Beijing Normal University, zgzheng@bnu.edu.cn) Changsong Zhou (Hong Kong Baptist University, cszhou@hkbu.edu.hk) Ravindra E. Amritkar (Physical Research Laboratory, amritkar@prl.ernet.in) Mustansir Barma (Tata Institute of Fundamental Research, Mumbai, barma@theory.tifr.res.in) Abhishek Dhar (Raman Research Institute in Bangalore, dabhi@rri.res.in) Ramakrishna Ramaswamy (Jawaharlal Nehru University, New Delhi, r.ramaswamy@mail.jnu.ac.in) Europe Giulio Casati (Center for Nonlinear and Complex Systems, Via Vallegio, Giulio.Casati@uninsubria.it) Michel Peyrard (ENS de Lyon, Michel.Peyrard@ens-lyon.fr) Mogens Jensen (University of Copenhagen, mhjensen@nbi.dk) Celso Grebogi (University of Aberdeen, grebogi@abdn.ac.uk) Stefano Ruffo (University of Florence, stefano.ruffo@unifi.it) Tamas Vicsek (Etvs Loránd University (ELTE), vicsek@hal.elte.hu) America Predrag Cvitanovic (Georgia Tech., predrag@gatech.edu) Ying-Cheng Lai (Arizona State University, Ying-Cheng.Lai@asu.edu) Edward Ott (University of Maryland, edott@umd.edu) Rajarshi Roy (University of Maryland, rroy@umd.edu) Gene Stanley (Boston University, hes@bu.edu ) Host Institute Institute of Physics of Academia Sinica Sponsors: APCTP (Pohang, South Korea) Physical Society of the Republic of China (Taipei, Taiwan) National Science Council (Taipei, Taiwan) National Center for Theoretical Sciences (Taipei, Taiwan) Ministry of Education (Taipei, Taiwan) Lectures: 12 plenary lectures 12-18 invited talks in 3 parallel sessions Some contributed talks and posters * 1-2 mins short report for each poster will be arranged during poster session. 10-15 mins talk will be arranged on Aug 9 for the reporter who wins the best poster award. Important dates: 30 November 2011: collecting responses from international advisory committee 2 December 2011: preparing a list of plenary lectures and invited talks January 2012: applying NSC grant DDAP7schedule
1: Klinge S, Voigts-Hoffmann F, Leibundgut M, Arpagaus S, Ban N. Crystal Structure of the Eukaryotic 60S Ribosomal Subunit in Complex with Initiation Factor 6. Science. 2011 Nov 3. PubMed PMID: 22052974. 2: Ataide SF, Schmitz N, Shen K, Ke A, Shan SO, Doudna JA, Ban N. The crystal structure of the signal recognition particle in complex with its receptor. Science. 2011 Feb 18;331(6019):881-6. PubMed PMID: 21330537. 3: Rabl J, Leibundgut M, Ataide SF, Haag A, Ban N. Crystal structure of the eukaryotic 40S ribosomal subunit in complex with initiation factor 1. Science. 2011 Feb 11;331(6018):730-6. Epub 2010 Dec 23. PubMed PMID: 21205638. 4: Maier T, Leibundgut M, Ban N. The crystal structure of a mammalian fatty acid synthase. Science. 2008 Sep 5;321(5894):1315-22. PubMed PMID: 18772430. 5: Leibundgut M, Jenni S, Frick C, Ban N. Structural basis for substrate delivery by acyl carrier protein in the yeast fatty acid synthase. Science. 2007 Apr 13;316(5822):288-90. PubMed PMID: 17431182. 6: Jenni S, Leibundgut M, Boehringer D, Frick C, Mikolásek B, Ban N. Structure of fungal fatty acid synthase and implications for iterative substrate shuttling. Science. 2007 Apr 13;316(5822):254-61. PubMed PMID: 17431175. 7: Thore S, Leibundgut M, Ban N. Structure of the eukaryotic thiamine pyrophosphate riboswitch with its regulatory ligand. Science. 2006 May 26;312(5777):1208-11. Epub 2006 May 4. PubMed PMID: 16675665. 8: Jenni S, Leibundgut M, Maier T, Ban N. Architecture of a fungal fatty acid synthase at 5 A resolution. Science. 2006 Mar 3;311(5765):1263-7. PubMed PMID: 16513976. 9: Maier T, Jenni S, Ban N. Architecture of mammalian fatty acid synthase at 4.5 A resolution. Science. 2006 Mar 3;311(5765):1258-62. PubMed PMID: 16513975. 10: Nissen P, Hansen J, Ban N, Moore PB, Steitz TA. The structural basis of ribosome activity in peptide bond synthesis. Science. 2000 Aug11;289(5481):920-30. PubMed PMID: 10937990. 11: Ban N, Nissen P, Hansen J, Moore PB, Steitz TA. The complete atomic structure of the large ribosomal subunit at 2.4 A resolution. Science. 2000 Aug 11;289(5481):905-20. PubMed PMID: 10937989.
王晓钢教授（科学网博主--等离子体科学）刚刚当选2011年美国物理学会会士(APS Fellow)，已肯定他在磁重联和复杂等离子体方面的贡献。这是继蔡诗东院士我国第二个等离子体物理学家在大陆获得的APS Fellow。证书上的嘉奖词（citation）是： “For seminal contributions to the theory of magnetic reconnection with broad applications to fusion and space plasmas, and to studies of waves and instabilities in complex plasmas.” 当选APS Fellow 还是很不容易的。 以前看过APS DPP的Fellow名单，发现只有L.CHEN, G.FU等几位华人在，大陆本土竟然一个都没有，太悲催了。9.16号刚刚在杭州CPS秋季会议上见过王老师。再次祝贺王老师，望大陆有更多人成为APS Fellow。
亮哥近日整理了他的计算MSF的程序（fortran），科学网的各位有需要的话可以email我 ryang8@asu.edu 为第一时间给您回复，请尽量有英文。。谢谢理解了。。 其中文献1可以是一个非常好的工具，上面研究了几乎所有的非线性系统，研究对于不同类型的coupling，不同大小的coupling strength，其对主稳定函数以及同步性能的影响。 Master-stability functions MSFs are fundamental to the study of synchronization in complex dynamical systems. For example, for a coupled oscillator network, a necessary condition for synchronization to occur is that the MSF at the corresponding normalized coupling parameters be negative. To understand the typical behaviors of the MSF for various chaotic oscillators is key to predicting the collective dynamics of a network of these oscillators. We address this issue by examining, systematically, MSFs for known chaotic oscillators. Our computations and analysis indicate that it is generic for MSFs being negative in a finite interval of a normalized coupling parameter. A general scheme is proposed to classify the typical behaviors of MSFs into four categories. These results are verified by direct simulations of synchronous dynamics on networks of actual coupled oscillators. References: L. Huang, Q.-F. Chen, Y.-C. Lai , and L. M . Pecora, ``Generic behavior of master -stability functions in coupled nonlinear dyna m ical syste m s,'' Physical Review E 80 , 036204 (2009). R. Yang, L. Huang, and Y.-C. Lai , ``Transient disorder in dyna m ically growing networks,'' Physical Review E 79 , 046101 (2009).
无意中发现了 这个站点 ，觉得很有意思，所以记载下来。 The Human Connectome Project Understanding the human brain is one of the great scientific challenges of the 21st century. The Human Connectome Project (HCP) represents a concerted attack on a key aspect of this challenge: elucidating the neural pathways that underlie brain function. Deciphering this amazingly complex wiring diagram will reveal much about what makes us uniquely human and what makes every person different from all others. The HCP will comprehensively map human brain circuitry in 1,200 healthy adults using cutting-edge methods of noninvasive neuroimaging. It will yield invaluable information about brain connectivity, its relationship to behavior, and the contributions of genetic and environmental factors to individual differences in brain circuitry. Results will be made freely available to the scientific community via the ConnectomeDB database and the Connectome Workbench visualization platform. This will include unprecedented ‘fly-through’ capabilities for navigating brain pathways and identifying neural circuits associated with different behavioral capacities. Successful charting of the human connectome in normal adults will be enormously informative. Even more importantly, it will pave the way for studies that reveal how brain circuitry changes during development and aging and how it differs in numerous neurological and psychiatric disorders. In short, it will transform our understanding of the human brain in health and disease.
一些时候人们会把systems biology误写为system biology。systems，是带s的。再次特别提示注意。 --------------- Systems biology From Wikipedia, the free encyclopedia Systems biology is a term used to describe a number of trends in bioscience research, and a movement which draws on those trends. Proponents describe systems biology as a biology-based inter-disciplinary study field that focuses on complex interactions in biological systems, claiming that it uses a new perspective (holism instead of reduction). Particularly from year 2000 onwards, the term is used widely in the biosciences, and in a variety of contexts. An often stated ambition of systems biology is the modeling and discovery of emergent properties, properties of a system whose theoretical description is only possible using techniques which fall under the remit of systems biology. Contents 1 Overview 2 History 3 Techniques associated with systems biology 4 See also 5 References 6 Further reading 6.1 Books 6.2 Journals 6.3 Articles 7 External links Overview Systems biology can be considered from a number of different aspects: As a field of study, particularly, the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway). As a paradigm, usually defined in antithesis to the so-called reductionist paradigm (biological organisation), although fully consistent with the scientific method. The distinction between the two paradigms is referred to in these quotations: "The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge...the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models" Science "Systems biology...is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different....It means changing our philosophy, in the full sense of the term" Denis Noble As a series operational protocols used for performing research, namely a cycle composed of theory, analytic or computational modelling to propose specific testable hypotheses about a biological system, experimental validation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory. Since the objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete as possible. Therefore, transcriptomics, metabolomics, proteomics and high-throughput techniques are used to collect quantitative data for the construction and validation of models. As the application of dynamical systems theory to molecular biology. As a socioscientific phenomenon defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel. This variety of viewpoints is illustrative of the fact that systems biology refers to a cluster of peripherally overlapping concepts rather than a single well-delineated field. However the term has widespread currency and popularity as of 2007, with chairs and institutes of systems biology proliferating worldwide (Such as the Institute for Systems Biology). History Systems biology finds its roots in: the quantitative modeling of enzyme kinetics, a discipline that flourished between 1900 and 1970, the mathematical modeling of population growth, the simulations developed to study neurophysiology, and control theory and cybernetics. One of the theorists who can be seen as a precursor of systems biology is Ludwig von Bertalanffy with his general systems theory, "organism biology" (he defined "organism" as the concept of "system") and his book titled "General Systems Theory in Physics and Biology" was published in 1950. One of the first numerical simulations in biology was published in 1952 by the British neurophysiologists and Nobel prize winners Alan Lloyd Hodgkin and Andrew Fielding Huxley, who constructed a mathematical model that explained the action potential propagating along the axon of a neuronal cell. Their model described a cellular function emerging from the interaction between two different molecular components, a potassium and a sodium channels, and can therefore be seen as the beginning of computational systems biology. In 1960, Denis Noble developed the first computer model of the heart pacemaker. The formal study of systems biology, as a distinct discipline, was launched by systems theorist Mihajlo Mesarovic in 1966 with an international symposium at the Case Institute of Technology in Cleveland, Ohio entitled "Systems Theory and Biology." The 1960s and 1970s saw the development of several approaches to study complex molecular systems, such as the Metabolic Control Analysis and the biochemical systems theory. The successes of molecular biology throughout the 1980s, coupled with a skepticism toward theoretical biology, that then promised more than it achieved, caused the quantitative modelling of biological processes to become a somewhat minor field. However the birth of functional genomics in the 1990s meant that large quantities of high quality data became available, while the computing power exploded, making more realistic models possible. In 1997, the group of Masaru Tomita published the first quantitative model of the metabolism of a whole (hypothetical) cell. The term of "systems biology" can be found in the article of Zieglgansberger W. and Tolle TR, 1993 (Pub-Med, NIH). During the 1990s years, Zeng B.J. created the concept, model and terms of "system medicine" (April, 1992), "system bio-engineering" (June, 1994) and "systems genetics"(Nov. 1994)" in China, and established the Associates for Biosystem Science and Engineering in 1999, Germany. Around the year 2000, when Institutes of Systems Biology were established in Seattle and Tokyo, systems biology emerged as a movement in its own right, spurred on by the completion of various genome projects, the large increase in data from the omics (e.g. genomics and proteomics) and the accompanying advances in high-throughput experiments and bioinformatics. Since then, various research institutes dedicated to systems biology have been developed. As of summer 2006, due to a shortage of people in systems biology several doctoral training centres in systems biology have been established in many parts of the world. Techniques associated with systems biology According to the interpretation of System Biology as the ability to obtain, integrate and analyze complex data from multiple experimental sources using interdisciplinary tools, some typical technology platforms are: Genomics: Organismal deoxyribonucleic acid(DNA) sequence, including intra-organisamal cell specific variation. (i.e. Telomere length variation etc). Epigenomics / Epigenetics: Organismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (i.e. DNA methylation, Histone Acetelation etc). Transcriptomics: Organismal, tissue or whole cell gene expression measurements by DNA microarrays or serial analysis of gene expression Interferomics: Organismal, tissue, or cell level transcript correcting factors (i.e. RNA interference) Translatomics / Proteomics: Organismal, tissue, or cell level measurements of proteins and peptides via two-dimensional gel electrophoresis, mass spectrometry or multi-dimensional protein identification techniques (advanced HPLC systems coupled with mass spectrometry). Sub disciplines include phosphoproteomics, glycoproteomics and other methods to detect chemically modified proteins. Metabolomics: Organismal, tissue, or cell level measurements of all small-molecules known as metabolites. Glycomics: Organismal, tissue, or cell level measurements of carbohydrates. Lipidomics: Organismal, tissue, or cell level measurements of lipids. In addition to the identification and quantification of the above given molecules further techniques analyze the dynamics and interactions within a cell. This includes: Interactomics: Organismal, tissue, or cell level study of interactions between molecules. Currently the authoratative molecular discipline in this field of study is protein-protein interactions (PPI), although the working definition does not pre-clude inclusion of other molecular disciplines such as those defined here. Fluxomics: Organismal, tissue, or cell level measurements of molecular dynamic changes over time. Biomics: systems analysis of the biome. The investigations are frequently combined with large scale perturbation methods, including gene-based (RNAi, mis-expression of wild type and mutant genes) and chemical approaches using small molecule libraries. Robots and automated sensors enable such large-scale experimentation and data acquisition. These technologies are still emerging and many face problems that the larger the quantity of data produced, the lower the quality. A wide variety of quantitative scientists (computational biologists, statisticians, mathematicians, computer scientists, engineers, and physicists) are working to improve the quality of these approaches and to create, refine, and retest the models to accurately reflect observations. The systems biology approach often involves the development of mechanistic models, such as the reconstruction of dynamic systems from the quantitative properties of their elementary building blocks. For instance, a cellular network can be modelled mathematically using methods coming from chemical kinetics and control theory. Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used. Other aspects of computer science and informatics are also used in systems biology. These include new forms of computational model, such as the use of process calculi to model biological processes, the integration of information from the literature, using techniques of information extraction and text mining, the development of online databases and repositories for sharing data and models (such as BioModels Database), approaches to database integration and software interoperability via loose coupling of software, websites and databases such as Gaggle, SBW, or commercial suits, and the development of syntactically and semantically sound ways of representing biological models, such as the Systems Biology Markup Language (SBML). See also Related fields Complex systems Complex systems biology Bioinformatics Biological network inference Biological systems engineering Biomedical cybernetics Biostatistics Extrapolation based molecular systems biology Theoretical Biophysics Relational Biology Translational Research Computational biology Computational systems biology Scotobiology Synthetic biology Systems biology modeling Systems ecology Systems immunology Related terms Life Biological organisation Artificial life Gene regulatory network Metabolic network modelling Living systems theory Network Theory of Aging Regulome Systems Biology Markup Language (SBML) SBO Viable System Model Antireductionism Systems biologists Category:Systems biologists Lists Category:Systems biologists List of systems biology conferences List of omics topics in biology List of publications in systems biology List of systems biology research groups References ^ Snoep J.L. and Westerhoff H.V.; Alberghina L. and Westerhoff H.V. (Eds.) (2005.). "From isolation to integration, a systems biology approach for building the Silicon Cell". Systems Biology: Definitions and Perspectives. Springer-Verlag. p. 7. ^ "Systems Biology - the 21st Century Science". http://www.systemsbiology.org/Intro_to_ISB_and_Systems_Biology/Systems_Biology_--_the_21st_Century_Science . ^ Sauer, U. et al. (27 April 2007). . Science 316: 550. doi:10.1126/science.1142502. PMID 17463274. ^ Denis Noble (2006). The Music of Life: Biology beyond the genome. Oxford University Press. ISBN 978-0199295739. p21 ^ "Systems Biology: Modelling, Simulation and Experimental Validation". http://www.bbsrc.ac.uk/science/areas/ebs/themes/main_sysbio.html . ^ Kholodenko B.N., Bruggeman F.J., Sauro H.M.; Alberghina L. and Westerhoff H.V.(Eds.) (2005.). "Mechanistic and modular approaches to modeling and inference of cellular regulatory networks". Systems Biology: Definitions and Perspectives. Springer-Verlag. p. 143. ^ Hodgkin AL, Huxley AF (1952). . J Physiol 117 (4): 500–544. PMID 12991237. ^ Le Novère, N (2007). . BMC Systems Biology 1: 28. doi:10.1186/1752-0509-1-28. PMID 17567903. ^ Noble D (1960). . Nature 188: 495–497. doi:10.1038/188495b0. PMID 13729365. ^ Mesarovic, M. D. (1968). Systems Theory and Biology. Springer-Verlag. ^ "A Means Toward a New Holism". Science 161 (3836): 34–35. doi:10.1126/science.161.3836.34. http://www.jstor.org/view/00368075/ap004022/00a00220/0 . ^ "Working the Systems". http://sciencecareers.sciencemag.org/career_development/previous_issues/articles/2006_03_03/working_the_systems/(parent)/158 . ^ Gardner, TS; di Bernardo D, Lorenz D and Collins JJ (4 July 2003). . Science 301: 102–1005. doi:10.1126/science.1081900. PMID 12843395. ^ di Bernardo, D; Thompson MJ, Gardner TS, Chobot SE, Eastwood EL, Wojtovich AP, Elliot SJ, Schaus SE and Collins JJ (March 2005). . Nature Biotechnology 23: 377–383. doi:10.1038/nbt1075. PMID 15765094. Further reading Books Zeng BJ. Structurity - Pan-evolution theory of biosystems, Hunan Changsha Xinghai, May, 1994. Hiroaki Kitano (editor). Foundations of Systems Biology. MIT Press: 2001. ISBN 0-262-11266-3 CP Fall, E Marland, J Wagner and JJ Tyson (Editors). "Computational Cell Biology." Springer Verlag: 2002 ISBN 0-387-95369-8 G Bock and JA Goode (eds).In Silico" Simulation of Biological Processes, Novartis Foundation Symposium 247. John Wiley Sons: 2002. ISBN 0-470-84480-9 E Klipp, R Herwig, A Kowald, C Wierling, and H Lehrach. Systems Biology in Practice. Wiley-VCH: 2005. ISBN 3-527-31078-9 L. Alberghina and H. Westerhoff (Editors) – Systems Biology: Definitions and Perspectives, Topics in Current Genetics 13, Springer Verlag (2005), ISBN 978-3540229681 A Kriete, R Eils. Computational Systems Biology., Elsevier - Academic Press: 2005. ISBN 0-12-088786-X K. Sneppen and G. Zocchi, (2005) Physics in Molecular Biology, Cambridge University Press, ISBN 0-521-84419-3 D. Noble, The Music of life. Biology beyond the genome Oxford University Press 2006. ISBN 0199295735, ISBN 978-0199295739 Z. Szallasi, J. Stelling, and V.Periwal (eds.) System Modeling in Cellular Biology: From Concepts to Nuts and Bolts (Hardcover), MIT Press: 2006, ISBN 0-262-19548-8 B Palsson, Systems Biology - Properties of Reconstructed Networks. Cambridge University Press: 2006. ISBN 978-0-521-85903-5 K Kaneko. Life: An Introduction to Complex Systems Biology. Springer: 2006. ISBN 3540326669 U Alon. An Introduction to Systems Biology: Design Principles of Biological Circuits. CRC Press: 2006. ISBN 1-58488-642-0 - emphasis on Network Biology (For a comparative review of Alon, Kaneko and Palsson see Werner, E. (March 29, 2007). "All systems go" (PDF). Nature 446: 493–494. doi:10.1038/446493a. http://www.nature.com/nature/journal/v446/n7135/pdf/446493a.pdf .) Andriani Daskalaki (editor) "Handbook of Research on Systems Biology Applications in Medicine" Medical Information Science Reference, October 2008 ISBN 978-1-60566-076-9 Journals BMC Systems Biology - open access journal on systems biology Molecular Systems Biology - open access journal on systems biology IET Systems Biology - not open access journal on systems biology WIRES Systems Biology and Medicine - open access journal on systems biology and medicine Articles Zeng BJ., On the concept of system biological engineering, Communication on Transgenic Animals, CAS, June, 1994. Zeng BJ., Transgenic expression system - goldegg plan (termed system genetics as the third wave of genetics), Communication on Transgenic Animals, CAS, Nov. 1994. Zeng BJ., From positive to synthetic medical science, Communication on Transgenic Animals, CAS, Nov. 1995. Binnewies, Tim Terence, Miller, WG, Wang, G. The complete genome sequence and analysis of the human pathogen Campylobacter lari. Published in journal: Foodborne Pathog Disease (ISSN 1535-3141) , vol: 5, issue: 4, pages: 371-386, 2008, Mary Ann Liebert, Inc. Publishers. M. Tomita, Hashimoto K, Takahashi K, Shimizu T, Matsuzaki Y, Miyoshi F, Saito K, Tanida S, Yugi K, Venter JC, Hutchison CA. E-CELL: Software Environment for Whole Cell Simulation. Genome Inform Ser Workshop Genome Inform. 1997;8:147-155. ScienceMag.org - Special Issue: Systems Biology, Science, Vol 295, No 5560, March 1, 2002 Marc Vidal and Eileen E. M. Furlong. Nature Reviews Genetics 2004 From OMICS to systems biology Marc Facciotti, Richard Bonneau, Leroy Hood and Nitin Baliga. Current Genomics 2004 Systems Biology Experimental Design - Considerations for Building Predictive Gene Regulatory Network Models for Prokaryotic Systems Katia Basso, Adam A Margolin, Gustavo Stolovitzky, Ulf Klein, Riccardo Dalla-Favera, Andrea Califano, (2005) "Reverse engineering of regulatory networks in human B cells". Nat Genet;37(4):382-90 Mario Jardon Systems Biology: An Overview - a review from the Science Creative Quarterly, 2005 Johnjoe McFadden, Guardian.co.uk - 'The unselfish gene: The new biology is reasserting the primacy of the whole organism - the individual - over the behaviour of isolated genes', The Guardian (May 6, 2005) Pharoah, M.C. (online). Looking to systems theory for a reductive explanation of phenomenal experience and evolutionary foundations for higher order thought Retrieved Jan, 15 2008. WTEC Panel Report on International Research and Development in Systems Biology (2005) E. Werner, "The Future and Limits of Systems Biology", Science STKE 2005, pe16 (2005). Francis J. Doyle and J?rg Stelling, "Systems interface biology" J. R. Soc. Interface Vol 3, No 10 2006 Kahlem, P. and Birney E. (2006). "Dry work in a wet world: computation in systems biology." Mol Syst Biol 2: 40. E. Werner, "All systems go", "Nature" vol 446, pp 493–494, March 29, 2007. (Review of three books (Alon, Kaneko, and Palsson) on systems biology.) Santiago Schnell, Ramon Grima, Philip K. Maini, "Multiscale Modeling in Biology", American Scientist, Vol 95, pages 134-142, March-April 2007. TS Gardner, D di Bernardo, D Lorenz and JJ Collins. "Inferring genetic networks and identifying compound of action via expression profiling." Science 301: 102-105 (2003). Jeffery C. Way and Pamela A. Silver, Why We Need Systems Biology H.S. Wiley, "Systems Biology - Beyond the Buzz." The Scientist. June 2006. Nina Flanagan, "Systems Biology Alters Drug Development." Genetic Engineering Biotechnology News, January 2008
Announcement: Focus Issue on “Synchronization and Cascading Processes in Complex Networks” T. Nishikawa Department of Mathematics, Clarkson University, Potsdam, New York 13699, USA (Received 22 December 2010; published online 4 February 2011) Chaos announces a Focus Issue on Synchronization and Cascading Processes in Complex Networks. The study of dynamical processes on complex networks has emerged as a next frontier in the science of networks, and has been attracting rapidly growing number of researchers, particularly from the Nonlinear Science community. This Focus Issue aims to provide the community with the latest developments on this front, focusing on synchronous and cascading dynamics, as well as how they arise and interact with the structure of the substrate network, analyzed in particular from the dynamical systems viewpoint. This Focus Issue will cover the following aspects of this important topic: • Types of dynamical processes—perfectly or imperfectly synchronized oscillations, phase synchronization, as well as other types of generalization and variations; cascading of information, dynamical states, and other node attributes; any other dynamical processes that can be formulated as synchronization or cascading problem. • Contexts—any context in which collective dynamics of a network is relevant, including, but not limited to, biological, social, and technological networks. • Methods—local and global stability analysis, computer simulations, mean-field analysis and its variations, and any other innovative approach that reveals the dynamics-structure relationship. • Questions—relation between the dynamical properties and the structure of the substrate network, transition to and from synchronized motion, relation to the functionalities of the network, real-world applications. Individuals wishing to submit contributed papers for consideration for publication in this issue should submit them to the journal at http://chaos.peerx-press.org prior to the submission deadline of 1 February 2011. The authors should indicate in their cover letter their desire to have this article considered for the Focus Issue. 1054-1500/2011/21(1)/010201/1/$30.00 21, 010201-1 VC 2011 American Institute of Physics CHAOS 21, 010201 (2011) Downloaded 18 May 2011 to 144.214.2.47. Redistribution subject to AIP license or copyright; see http://chaos.aip.org/about/rights_and_permissions
By Megan Scudellari Power Failure Does mitochondrial dysfunction lie at the heart of common, complex diseases like cancer and autism? Kevin Hand Mitochondria are tiny. A single human cell can contain hundreds to thousands of these potato-shaped organelles, depending on the tissue type. They power the biochemical reactions in our cells through the production of adenosine triphosphate (ATP). These oft-overlooked furnaces, not studied in earnest until the 1970s, are now the subject of intense scrutiny for their potentially central role in common, complex diseases. They may be, scientists say, pivotal to the etiology of diseases such as cancer and Alzheimer’s, epidemics against which researchers and companies have spent billions of dollars but made arguably little progress. But not everyone agrees with the mitochondrial hypothesis. Complex diseases are simply that, some researchers argue—complex. While mitochondria are essential to human physiology, there has not been sufficient evidence to prove that mitochondrial dysfunction plays a causative role in complex diseases. When it is implicated, debate ensues over whether errors in energy production contribute to disease pathology or are simply a consequence of it. “The question remains, as it should, how often a major player?” asks Marvin Natowicz, a clinician specializing in autism and mitochondrial disease at the Cleveland Clinic in Ohio. It doesn’t help that studies of human mitochondrial function are invasive, costly, and lengthy. But over the last five years, a growing number of papers by researchers around the world have implicated dysfunctional mitochondria in many elusive diseases, including Parkinson’s, autism, and aging. And leading the charge is an unlikely champion, a respected and renowned member of the National Academy of Sciences who is simultaneously a self-proclaimed radical and zealot: a man about whom colleagues hesitate to comment, a maverick known for mounting a soapbox to hold forth on the “vital force,” Eastern medicine, and E=mc 2 . Douglas Wallace analyzes frozen patient cell lines to identify molecular defects affecting mitochondrial function that lead to disease. Robert Neroni On a brisk February morning, Douglas Wallace walks through the halls of the Center for Mitochondrial and Epigenomic Medicine, a new research center at the Children’s Hospital of Philadelphia, spouting philosophy. “Every one of the diseases we can’t solve is absolutely logical if we put energy at the center,” he says. “I believed that in 1970 and I believe it now.” A short, cheery man with gold-rimmed glasses, a yellow and green paisley tie and oversize pants held up by blue suspenders, Wallace is a founder of the field of human mitochondrial genetics. As a researcher he has published over 230 papers and is consulted by clinicians about some of the world’s trickiest diseases. But he is also a man on a mission to convince the scientific establishment that they’ve got it all wrong. Medicine fails to solve many of today’s common, complex diseases, Wallace asserts, because the fundamental paradigm is wrong: the medical establishment has spent far too long focusing on anatomy and ignoring energy—specifically, mitochondria. It has been his tune for more than 30 years, though it’s often fallen on deaf ears in the scientific community. But today, the idea that energy deficiency plays a major role in human disease appears to be gaining momentum, as more and more papers link mitochondrial dysfunction to disease. The shift has prompted Children’s Hospital to put their money behind his research, and has caused many in the community to wonder: Is Doug Wallace crazy? Or is he right? A Pandora’s box of mutations Mitochondria generate energy in the form of ATP by combining nutrients and oxygen in a chemical reaction called oxidative phosphorylation (OXPHOS). The mitochondrion is hypothesized to have originated as a bacterium engulfed by another cell some two billion years ago. Mitochondrial DNA (mtDNA) is circular, with 37 genes, 13 of which encode subunits of enzymes involved in OXPHOS and so are analogous to the wiring diagram for a power plant. (See “Mitochondria at Work” below.) More than a thousand additional genes in the nucleus of the cell (nDNA) are involved in the maintenance, growth, and replication of mitochondria, and around 80 of those nuclear genes code for proteins involved directly in OXPHOS. While nDNA is inherited from both the mother and the father, in 1980 Wallace demonstrated that human mtDNA is inherited only from the mother. In 1988, Wallace took our understanding of mtDNA a step further: He discovered, for the first time, that mutations in mtDNA cause disease. He identified a point mutation in a protein subunit that results in Leber’s hereditary optic neuropathy, a form of midlife blindness. 1 Shortly after, Wallace identified an mtDNA mutation associated with a form of progressive epilepsy accompanied by muscle weakness. 2 It was his first glimpse into a Pandora’s box of diseases caused by mutations in that small, circular DNA. Today more than 400 point mutations, as well as innumerable mtDNA rearrangements, are linked to heart and muscle disease, epilepsy, deafness, blindness, anemia, and more. 3 In addition, mutations in nDNA genes can cause mitochondrial disease, as can combinations of nDNA and mtDNA mutations. 4 “At the moment, there are about 120 different genetic disorders, and there are probably as many again to be discovered,” says David Thorburn, head of mitochondrial research at the Murdoch Childrens Research Institute in Victoria, Australia. A primary mitochondrial disease—one caused by a mutation in mtDNA—is not easy to diagnose and often involves many organ systems, including heart, brain, muscle, and gastrointestinal tract. “We used to say, if three or more systems are involved, think mitochondria,” says Marni Falk, a pediatrician at Children’s Hospital of Philadelphia and a leading mitochondrial researcher. In addition to the production of ATP, mitochondria regulate calcium control in the cell and guide cell death. “They’re like the conductor of the orchestra,” says Falk. “When they’re not working, all is disrupted.” Sadly, there is a dearth of therapies for well over 95 percent of primary mitochondrial disease cases. “The treatments we hoped would prove effective have been really disappointing,” says Marc Yudkoff, chief of child development and rehabilitation medicine at Children’s Hospital. “The area of mitochondrial disease has become our most pressing concern.” Children’s Hospital is a hub of research into primary mitochondrial diseases and treatments for their victims, with hundreds of cases referred each year—and “the pace is increasing,” says Falk. In 2007, she established a mitochondrial research group at the hospital, bringing together over 175 specialists in numerous fields—from endocrinology to anesthesiology to hematology to surgery—to spark collaborations to identify new biomarkers and treatments for such diseases. But beyond the need for therapies and research into primary mitochondrial diseases, Wallace believes there is an even larger, unrecognized chasm in the medical community. Over the last fifty years, despite billions of dollars in funding, the medical community has failed to discover causes or treatments for many common, complex diseases: heart disease, Alzheimer’s, autism, and more. Wallace attributes that continuing failure to the fact that clinicians and researchers base medical training and treatments on anatomy. If someone has a headache, for example, doctors look to the head. If the patient has chest pain, a clinician examines the heart or lungs. Doctors are taught organ-specific medicine in school, and the NIH still organizes its research centers based largely on organ systems: the National Eye Institute, the National Heart, Blood, and Lung Institute, and the National Institute of Diabetes and Digestive and Kidney Diseases, for example. But life is structure plus energy, argues Wallace, and we’ve been missing the second half of that equation. It is “self-evident” in some ways, says Yudkoff. Disease is caused by a loss of organization—by entropy—which is essentially a loss of energy. “On a basic physical and chemical level, it’s not arguable,” he says. Thorburn adds, “The whole area is fascinating and plausible. has done some pioneering work. He sells it very hard, but people are very much interested in the ideas and following up on them.” To Wallace, looking at complex diseases through the lens of mitochondria makes everything clearer. “I’m not saying anything done before isn’t good. It’s just not complete,” says Wallace. Especially, he believes, when trying to understand the elusive link between disease, genetics, and the environment. Infographic: Mitochondria At Work View full size JPG (443 KB) | PDF (1.66 MB) Andrew Swift Genome-wide association studies (GWAS), a popular tool to find genetic variations associated with a particular disease, have for the most part had limited success. Their failures are often blamed on confounding environmental factors. Mitochondria, notes Wallace, are that missing factor: they act as a direct link between our genes and the environment, taking in calories and oxygen (products of the environment), and producing ATP and acetyl coenzyme A, two molecules involved in the regulation of most biochemical reactions, including gene expression. 5 In addition, during OXPHOS, mitochondria generate reactive oxygen species (ROS), the smoke from the furnace. At low levels, the ROS provide a critical signaling system from the mitochondrion to the cytosol and nucleus. However, at high levels, these free radicals cause significant damage to the cell and organelles, especially to the mitochondria themselves. Consequently, mtDNA has a much higher mutation rate than nuclear DNA. Thus, the most common genetic changes caused by the environment are mutations in mtDNA, says Wallace. These inheritable changes, plus mitochondrial regulation of nuclear DNA gene expression by ATP and acetyl coenzyme A, are the major factors contributing to predisposition to the common diseases, argues Wallace. 6 Yet GWAS only analyze nDNA, not mtDNA. “GWAS are wonderful,” says Wallace. “The problem is they don’t include energetics.” Today, Wallace finally has the backing of a major research hospital to explore these ideas and more. In 2009, while visiting Children’s Hospital, Wallace spoke with Yudkoff about his desire to start a center focused on the role of mitochondria in common, complex diseases. “It was a bit like Einstein asking if he’s welcome in a physics department,” says Yudkoff. “There’s arguably no one alive with more impeccable credentials in the field.” The Center for Mitochondrial and Epigenomic Medicine (CMEM) opened July 2010, with 19 of Wallace’s staff from his previous post at the University of California, Irvine, joining him. In addition to staff, Wallace brought his mice. Over decades, he has created numerous mouse models in an effort to prove a direct cause-and-effect relationship between mitochondrial defects and common diseases. In 1997, for example, he created a mouse deficient in Ant1, a nuclear-encoded protein involved in ATP synthesis, loss of which produces debilitating heart and muscle disease. 7 He has also created mice harboring mtDNAs with a single base change in the mtDNA COI gene. These animals develop heart and muscle disease as well as other symptoms, demonstrating that a single mtDNA mutation is sufficient to cause degenerative disease. Today, Wallace has over 3,000 mice with different mitochondrial defects serving as models for diseases including diabetes, hypertension, blindness, and neurological problems. The Center, now stocked with mice and staff, could not have opened at a more opportune time. Today there is a “renaissance” of researchers considering the role of energy and mitochondria in common disease, says Falk. “In the last decade, there’s been an explosion of research,” she says. Still, while studies of nuclear DNA implicate new genes in complex diseases every day, mtDNA studies are far fewer and more difficult to perform, typically requiring an invasive muscle biopsy and an analysis of the percentage of mutated mitochondria within a cell or population of cells. To catch the attention of the medical community, every last scrap of research will be needed. Extraordinary claims, as they say, require extraordinary evidence. Altered metabolism in complex diseases In 1982, an illicit chemist in Northern California synthesized and sold a bad batch of a narcotic called “China White.” Four drug addicts injected the contaminated dope and subsequently developed tremors, rigidity, and loss of balance—it appeared as if they had Parkinson’s disease. The drug, researchers later discovered, was actually MPTP, a neurotoxin that inhibits the first step of OXPHOS. “Ever since then, the role of mitochondria in Parkinson’s has been hotly debated,” says Doug Turnbull, a member of the mitochondrial research group at Newcastle University in the United Kingdom. Today, more than 800 papers have analyzed the role of mitochondria in Parkinson’s disease, with intriguing conclusions. The most frequently known genetic cause of Parkinson’s to date is a mutation in LRRK2 , a nuclear gene encoding an enzyme that associates with the mitochondrial outer membrane. (See “ The Genes of Parkinson’s Disease ,” The Scientist, February 2011.) Studies have shown the mutation is connected to impaired mitochondrial function. 8 In 2006, Turnbull and colleagues at Newcastle found significant mitochondrial DNA deletions in the substantia nigra, a brain region damaged in patients with Parkinson’s disease. 9 “Neurobiologists are too shy to accept that it is a mitochondrial disease, but it is a mitochondrial disease,” says Prasanth Potluri, a self-proclaimed “mitochondriac” and research scientist at CMEM. Yet, for others, the idea is not so cut-and-dried. It’s “quite controversial,” said Thomas Gasser, director of the department of neurodegenerative diseases at the Hertie-Institute for Clinical Brain Research at the University of Tübingen, Germany. There are rare, recessively inherited forms of Parkinson’s in which defective mitochondria certainly play a role, says Gasser, but for the more common, sporadic cases of Parkinson’s, “the primary defect lies somewhere else.” There is also budding evidence for the role of mitochondrial dysfunction in other common neurodegenerative diseases. Recent studies suggest that amyloid-beta, the chief component of the characteristic plaque of Alzheimer’s disease, progressively accumulates within mitochondria, acting as a direct toxin. In addition, defects in OXPHOS, including mutations in mtDNA, have been frequently associated with the disease. 10,11 And a recent study from Newcastle University found that mtDNA deletions may also be an important contributor to multiple sclerosis. 12 In 2005, a population-based study at a school in Portugal demonstrated that seven percent of autistic children studied had disturbances in mitochondrial energy metabolism. 13 “It raised the question that disturbances of mitochondrial function might be a reasonably common finding in persons with autism,” says Natowicz of the Cleveland Clinic. Yet researchers are divided over the degree to which mitochondrial dysfunction actually contributes to the autistic phenotype, and over whether people with OXPHOS disorders are a clinically distinct population of autistic individuals or no different from most who suffer from autism. More large population-based studies might answer that question, says Natowicz, but they have yet to be done. “This is a central question that needs much more attention,” he concludes. Genome-wide association studies are wonderful. The problem is they don’t include energetics.—Douglas Wallace But nowhere is the study of altered metabolism more popular than in cancer research. Researchers have long observed that metabolism in tumors is different from metabolism in noncancerous cells, possibly because cancer cells must accommodate the increased metabolic demands of rapid cell proliferation. The study of metabolism in cancer cells has “exploded” over the last five years, says Eyal Gottlieb, a researcher at the Beatson Institute for Cancer Research in Glasgow, Scotland. There are hundreds of papers describing mitochondrial DNA mutations in cancer, including Wallace’s own work identifying mtDNA mutations in prostate cancer. 14 But alterations in mitochondrial DNA and function could be a consequence of a cancerous phenotype, rather than the cause. There are some instances where scientists have demonstrated a direct causal role of mitochondria dysfunction in cancer, 15 but such cases are, at the moment, “the exception to the rule,” says Gottlieb. Still, he adds, “There is a link there, even if we don’t fully understand it.” For now, the role of mitochondria in common diseases continues to be investigated in numerous studies. Still, the majority of clinicians in all of these fields—even in Parkinson’s, where the evidence seems strongest—have not embraced Wallace’s paradigm-shifting theory. A new concept of medicine Wallace walks down a long, empty corridor. To the right, row after row of sparkling lab benches stand empty and waiting. Today, only twenty-one people fill one of the four lab bays that will make up the new center, but already the team is tackling projects in metabolic syndrome, cancer, heart disease, and aging. Wallace has plans to hire more new faculty plus support staff this year. He reaches the end of the corridor and turns, walking into one of the center’s new conference rooms, which appropriately overlooks a silent power plant, silhouetted against the cold Philadelphia sunset. His voice has grown hoarse. He settles back into a chair. In the end, Wallace, whose mother had Alzheimer’s and whose son is autistic, isn’t out to criticize his colleagues, but to save lives. “I don’t know how long it’s going to take for people to see this is relevant,” he says with a sigh, looking out at the quiet plant below. “We now have a mitochondrial, energy-based concept of medicine, which beautifully explains in a simple way all the previous inexplicable problems. Things are only complex when we don’t understand them.” Methods: ID’ing Disease-Related Mutations in Mitochondrial DNA In the effort to identify mitochondrial DNA (mtDNA) mutations associated with human disease, a major hurdle has been the fact that there is no “normal” mtDNA sequence. As Wallace and colleagues discovered beginning in the 1980s, human populations around the world have high levels of variation in mtDNA, which can be sorted into distinct haplogroups, or branches, reflecting their geographic origins. Lucy Reading-Ikkanda (map); Source: Copyright 2002 Mitomap.org mtDNA in modern humans dates back to Africa some 150,000 to 200,000 years ago. Based on samples of mtDNA collected around the world over decades, Wallace’s team has mapped this remarkable correlation between mtDNA variation and place of origin: as humans spread around the globe out of Africa, populations acquired adaptive mutations allowing them to thrive in different climates. (See map at left: letters denote mtDNA lineages.) In cold regions, for example, lineages acquired mtDNA mutations that resulted in a less-efficient oxidative phosphorylation system, with decreased ATP production, but increased heat production. This high degree of mtDNA variation puts clinicians in a quandary. How can one identify which variations cause disease and which are simply the result of a person’s geographic origins? For example, sequencing the mtDNA of more than 500 patients known to suffer from mitochondrial cardiomyopathy resulted in over 200 different sequence variants—far too many to identify a culprit. To resolve the issue, Wallace and his team recently designed an automated analysis system they call MITOMASTER to compare the mtDNA sequences of patients with a database of thousands of mtDNA haplogroups. Analyzing the mtDNA sequences of 29 Italian patients with mitochondrial heart disease, the researchers identified 593 mtDNA variants, but found that 98 percent of them were haplogroup-associated. Six mutations, however, were novel and not associated with a haplogroup, suggesting they were possible disease contributors ( Eur J Hum Genet . 19:200-07, 2011). The approach demonstrates that clinicians shouldn’t be analyzing individual mtDNA sequences in isolation, and that automated systems can help researchers ferret out links between mtDNA mutations and disease pathology. References: 1. D.C. Wallace et al., “Mitochondrial DNA mutation associated with Leber’s hereditary optic neuropathy,” Science , 242:1427-30, 1988. 2. J.M. Shoffner et al., “Myoclonic epilepsy and ragged-red fiber disease (MERRF) is associated with a mitochondrial DNA tRNA(Lys) mutation,” Cell , 61:931-37, 1990. 3. D.C. Wallace, “Mitochondrial DNA Mutations in Disease and Aging,” Environ Mol Mutagen , 51:440-50, 2010. 4. A. Spinazzola, M. Zeviani, “Disorders from perturbations of nuclear-mitochondrial intergenomic cross-talk,” J Intern Med , 265:174-92, 2009. 5. D.C. Wallace, Colloquium paper: “Bioenergetics, the origins of complexity, and the ascent of man,” PNAS , 107:8947-53, 2010. 6. D.C. Wallace, “Bioenergetics and the epigenome: interface between the environment and genes in common diseases,” Dev Disabil Res Rev , 16:114-19, 2010. 7. B.H. Graham et al., “A mouse model for mitochondrial myopathy and cardiomyopathy resulting from a deficiency in the heart/muscle isoform of the adenine nucleotide translocator,” Nat Genetics , 16:226-34, 1997. 8. H. Mortiboys et al., “Mitochondrial impairment in patients with Parkinson disease with the G2019S mutation in LRRK2 ,” Neurology , 75:2017-20, 2010. 9. A. Bender et al., “High levels of mitochondrial DNA deletions in substantia nigra neurons in aging and Parkinson disease,” Nat Genet , 38:515-17, 2006. 10. P.E. Coskun et al., “Alzheimer’s brains harbor somatic mtDNA control-region mutations that suppress mitochondrial transcription and replication,” PNAS , 101:10726–31, 2004. 11. P.E. Coskun, “Systemic mitochondrial dysfunction and the etiology of Alzheimer’s disease and Down syndrome dementia,” J Alzheimers Dis , 20 Suppl 2:S293-310, 2010. Free F1000 Evaluation 12. G.R. Campbell et al., “Mitochondrial DNA deletions and neurodegeneration in multiple sclerosis,” Ann Neurol , 69:481-492, 2011. Free F1000 Evaluation 13. G. Oliveria et al., “Mitochondrial dysfunction in autism spectrum disorders: a population-based study,” Dev Med Child Neurol , 47:185-59, 2005. 14. J.A. Petros et al., “mtDNA mutations increase tumorigenicity in prostate cancer,” PNAS , 102:719-24, 2005. 15. D.C. Wallace, “A Mitochondrial Paradigm of Metabolic and Degenerative Diseases, Aging, and Cancer: A Dawn for Evolutionary Medicine,” Ann Rev Genet, 39:359-407, 2005. Rating: 3.94 /5 (53 votes ) Comment on this article ROS issues may also involve immune system by judith luber-narod Just an observation regarding Alzheimer's disease. Immune cells can also contribute high levels of ROS and respond to them. Perhaps there is a link between the mitochondrial systems and immune responses seen in many of the neurologic diseases associated with mitochondrial abnormalities. Mighty Mitochondria by anonymous poster I think they are onto something here. What about the effects of environmental estrogens on mito? Growth hormone in beef. Everybody likes it rare. an excellent science writing by anonymous poster The complicated material on mitochondria is presented with clarity and supported by a quality illustration. Very well written and engaging. Nice article... by anonymous poster ... and good to see mitochondria back in the mainstream news. For anyone interested in increasing the visibility of mitochondrial research, there are currently a couple of bills... H.R. 3502 and S. 2858 in need of support (i.e., call your senator). One goal of this campaign is the establishment of an office of mitochondrial disease/research, within the NIH. mitochondria mutation and disease by Robert Fujimura I agree but mitochondrial research has been intense from the time I became a graduate student in biochemistry in 1956. The most famous scientists in mitochondria around that period was David Green of U. Wisconsin and Albert Lehninger of the Johns Hopkins. My question may have been answered by the first comment. There are many copies of mitochondria per cell. Therefore, how could a mutation in a mitochondria genome affect the whole cell and whole organism. As the first comment says, is it because the critical mutation that affects mitochondria function is nuclear genomic? electronic supplementation by Keith Wendell We have been working on this from the supplementation of electrons position since 1986. (A large antioxident dose) www.thrisoint.com contains the various published articles. By just offering a free flow of electrons at the same level as the body generates for the meridian (chi) flow seem to help the body heal most disease. (wounds, pain, edema, diabetes type 2, hypertension, dengy fever, lyme, MS and cancer all seem to inprove) If there is anyone out there who is actually interested we are happy to discuss. Devices Bodihealth, Bodicharger and Bodisong. sincerely Keith not just m-DNA -- other causes of mitochondrial dysfunction by anonymous poster I hope that the focus of this lab is not merely on mitochondrial DNA mutations. Surely these folks know that most of the polypeptides in the mitochondria are nuclear-encoded. It is quite likely that many "mitochondrial" dysfunctions are due to problems with nuclear-encoded gene products, and mutations in those genes should therefore also be sought out in conjunction with various hereditary medical conditions. Perhaps even more importantly I want to point out that there is sometimes a pharmacological (rather than genetic) basis for some mitochondrial dysfunctions. For example, the mechanism of action of statins is to inhibit the enzyme that synthesizes mevalonic acid, which is a key metabolic precursor to cholesterol. However, mevalonate is also a key metabolic precursor of two other critical products: dolichol and coenzyme Q. Statins are therefore used at dosages that only partially inhibit total enzyme activity. However, the enzyme that takes mevalonate down the "dolichol" pathway has much higher affinity than the enzyme that takes it down the "CoQ" pathway, and as a result it is quite likely that some people will develop CoQ insufficiency(but probably not dolichol deficiency) due to statins. This is the likeliest cause of statin-associated leg muscle pain (since exogenous CoQ administration usually relieves that pain), and CoQ insufficiency should also be suspected as a possible cause for other side effects (liver, kidney, heart muscle) that are associated with statins. There are certainly other pharmacological agents in therapeutic use today that impact either directly or indirectly on mitochondrial function. There's a wealth of exploration that needs to be done here. All of which is to say that this is a stimulating article, and there's a lot of work that should be done in this area. Autism: disorder not disease by Neil Toner According to DSM IV, autism is a disorder, not a disease. If you are going to write about autism please get it right. We do not need any more confusion in the area than we already have. Read more: Power Failure - The Scientist - Magazine of the Life Sciences http://www.the-scientist.com/article/display/58132/#ixzz1Mg8cbPIz
Controllability of complex networks .pdf ARTICLE doi:10.1038/nature10011 Controllability of complex networks Yang-Yu Liu1,2, Jean-Jacques Slotine3,4 Albert-Laszlo Barabasi1,2,5 1:Center for Complex Network Research and Departments of Physics, Computer Science and Biology, Northeastern University, Boston, Massachusetts 02115, USA. 2:Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. 3:Nonlinear Systems Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. 4:Department of Mechanical Engineering and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139,USA. 5Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. 1, 2: M AY 2 0 1 1 | VO L 4 7 3 | N AT U R E | 1 6 7 2011 Macmillan The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them.Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, aframework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.
还热着呢 刚出炉 Nature 473 , 167–173 (12 May 2011) doi:10.1038/nature10011 Received 18 November 2010 Accepted 16 March 2011 Published online 11 May 2011 有人认识一作吗？ 是不是已经有人开始follow了。。 呵呵 pap er
SIGN Signum function. For each element of X, SIGN(X) returns 1 if the element is greater than zero, 0 if it equals zero and -1 if it is less than zero.For the nonzero elements of complex X, SIGN(X) = X ./ ABS(X).
Cytoscape is an open source bioinformatics software platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data. Although Cytoscape was originally designed for biological research, now it is a general platform for complex network analysis and visualization. Cytoscape core distribution provides a basic set of features for data integration and visualization. Additional features are available as plugins . Plugins are available for network and molecular profiling analyses, new layouts, additional file format support, scripting, and connection with databases. Plugins may be developed by anyone using the Cytoscape open API based on Java™ technology and plugin community development is encouraged. Most of the plugins are freely available . 信息来自：http://www.cytoscape.org/
ISCNS 2011 nApril 23-24 Nanjing · Home · Organizing Committee · Invited Speakers · Program Schedule · Registration · Location and Sightseeing · Sponsors · Contact The 2011 International Symposium on Complex Networks and Systems (ISCNS'11) will be held on April 23-24 2011 in Nanjing, China, officially organized by the Department of Mathematics , Southeast University , China. The ISCNS'11 focuses on complex systems and networks, multi-agent systems, nonlinear dynamics and control, systems biology, sensor networks, communication networks, networked control systems, and their applications. The symposium will be sponsored by the Department of Mathematics , Southeast University , China. Latest News Due the limit budget, we cannot afford your travel fees in this workshop. What we can afford are the following: 1. your living cost (including 2 buffet lunch 自助餐 +1 banquet) 2. your accommodation fees in the reserved hotel 3. tour fee in Zhongshan Ling （中山陵） on 25 April after the workshop Please send me the filled accommodation form with some detailed information by 11 April so that we can reserve the accommodation for you. Program Schedule 23-24 April, 2011: Two-day Workshop Registration Apr. 22, 2011 （ Friday ） 9:00-21:00 Registration : Nanjing Shijiyuan Hotel (jinxianghe road 20) 南京世纪缘大酒店 ( 进香河路 20 号 ) 东南大学四牌楼校区 西门向南大约 800 米 Apr. 23, 2011 （ Saturday ） 9:00-17:40 Registration : Chunhui Hall ( 东南大学四牌楼校区春晖堂 ) 南京市四牌楼 2 号东南大学 Apr. 23, 2011(Saturday) Chunhui Hall(春晖堂) 09:00-09:30 OpenCeremony Session1 Chair:JindeCao 09:30- 10:20 Guanrong Chen LaplacianSpectra ofComplexNetworks andTheirEffects onSynchronization Performances 10:20- 10:40 TeaBreak 10:40- 11:30 Yusheng Xue TBD 12:00- 14:00 Lunch Session2 Chair:JinqingFang 14:00-14:50 Xiaofan Wang SocialLearningin ComplexNetworks 14:50-15:40 Binghong Wang TransportationDynamics onMobileNodeNetwork 15:40-16:00 TeaBreak 16:00-16:50 Jinqing Fang AFrameworkof Non-Equilibriumand EquilibriumStatistical EnsembleFormalism forComplexQuantumSystems 16:50-17:40 Fuchun Sun UniversalApproximationand ControlofMulti-Time-Scale DynamicalSystems 18:00 Banquet Apr.24,2011(Sunday) ChunhuiHall( 春晖堂 ) Session3 Chair:YuanqingXia 09:00-09:50 Jun-an Lu Synchronization-based ScalabilityofRingsand ChainsofDenseLumps 09:50-10:10 TeaBreak 10:10-11:00 Zhihong Guan ImpulsiveAlgorithms forConsensusof Multi-agentSystems 11:00-11:50 Yuanqing Xia AnalysisandSynthesisof NetworkedControlSystems 12:00- 14:00 Lunch Session4 Chair:WenwuYu 14:00-14:50 Zhisheng Duan Synchronizationof ComplexNetworksand ConsensusofMulti-AgentSystems: AUnifiedViewpoint 14:50-15:40 Daniel Ho APinningSchemeand AggregationApproach toComplexNetworks 15:40-16:00 TeaBreak 16:00-16:50 Guangming Xie ControlTheory forSmartSwarms 16:50-17:40 Wei Ren DistributedControl ofNetworkedMulti-agent Systems:Algorithms andApplications 17:40-18:00 ConclusionRemarks Apr. 25, 2011(Monday) Tourism in Zhongshan Ling（中山陵） All Rights Reserved 2011 - By ISCNS'2011
The 5-th Week Schedule Program Schedule "Recent Progresses in Complex Networks Research" The Fifth Week (Mar 28-April 1) ====================================== Mar 28 Monday 10:00-11:30 Chairperson ： Bing-Hong Wang B. Kahng （ Seoul National University ） : Explosive percolation transition of complex networks Lunch Break 15 ： 00-16:00 Chairperson ： Tao Zhou Wei Li (Max-Planck-Institute for Mathematics in the Sciences): Statistical Learning in an evolutionary game Mar 29 Tue 10:00-11:30 Chairperson ： Bing-Hong Wang Hawoong Jeong (Korea Advanced Institute of Science and Technology) ： S tructure and dynamics of the directed complex networks Mar 30 Wed 10:00-11:30 Chairperson ： Bing-Hong Wang Zhou, Changsong （ Hong Kong Baptist University ） ： Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations Mar 31 Thursday 10:00-11:30 Chairperson ： Bing-Hong Wang David Saad （ The Non-linearity and ComplexityResearch Group, Aston University ， UK ） Dynamics of Boolean networks - a Generating Functional Analysis Lunch Break 15 ： 00-16:00 Chairperson ： Tao Zhou You-Gui Wang ( Beijing Normal University): Self-Organized Criticality in Market Economies April 1 Friday 10:00-11:30 Chairperson ： Bing-Hong Wang Tao Zhou (University of Electronic Science and Technology of China) : 1, Link prediction in complex networks; 2, Information extracting from networks: ranking and recommending; Lunch Break 15:00-17:00 Chairperson ： Tao Zhou Discussion about recent progresses and future directions in complex networks research : All the participants: Chin-Kun Hu ， B.Kahng ， Beom Jun Kim, Bin Jiang ， Renaud Lambiotte ， Jing Zhao ， Hyunggyu Park, Hawoong Jeong, Sumiyoshi Abe, Jae Dong Noh, David Saad ， Changsong Zhou, Chenping Zhu, Wei Li, Zi-Ke Zhang, Lin-Yuan Lv ， Mingsheng Shang, Tao Zhou ， Bing-Hong Wang
The Fourth Week Schedule Interdisciplinary Applications of Statistical Physics Complex Networks (KITPC/ITP-CAS, Feb 28 - Apr 1, 2011) Week 4, Mar 21 – Mar 25, 2011 Monday 21 / 3 /201 1 Room: 6620 Chairperson: Bing-Hong Wang 10:00-11:00 Chin-Kun Hu （ Institute of Physics, Academia Sinica ， Taipei ） Some recent results for complex networks of nonlinear and biological systems' 11:00-11:30 Discussion Lunch Break Room: 6620 Chairperson: Tao Zhou 15:00-16:00 Bin Jiang (University of G?vle, Dept of Technology and Built Environment, Sweden) Scaling of geographic space with data-intensive geospatial computing. 16:00-16:20 Break 16:20-17:00 Jing Zhao (Department of Mathematics, Logistical Engineering University) Network based TCM pharmacology Tuesday 22 / 3 /201 1 Room: 6620 Chairperson: Bing-Hong Wang 10:00-11:00 Sumiyoshi Abe (Department of Physical Engineering, Mie University ) Aftershocks 11:00-11:30 Discussion Lunch Break Room: 6620 Chairperson: Tao Zhou 15:00-15:50 Renaud Lambiotte (Imperial College London; Institute for Mathematical Sciences ) Beyond Space For Spatial Networks 15:50-16:10 Break 16:10-17:00 Wen-Xu Wang (Arisona University, USA) TBA Wednesday 23 / 3 /201 1 Room: 6620 Chairperson: Bing-Hong Wang 10:00-11:00 Hyunggyu Park (School of Physics, Korea Institute for Advanced Study, Korea) Phase transitions on networks: Annealed verses quenched 11:00-11:30 Discussion Lunch Break Room: 6620 Chairperson: Tao Zhou 15:00-16:00 Renaud Lambiotte (Imperial College London; Institute for Mathematical Sciences) Multi-scale Modularity in Complex Networks 16:00-16:20 Break 16:20-17:00 Yueheng Lan (Department of Physics, Tsinghua University) Novel computation of the growth rate of generalized random Fibonacci sequences Thursday 24 / 3 /201 1 Room: 6620 Chairperson: Bing-Hong Wang 10:00-11:00 Beom Jun Kim (Department of Physics, Sungkyunkwan University, Korea) Synchronization in two coupled networks 11:00-11:30 Discussion Lunch Break Room: 6620 Chairperson: Tao Zhou 15:00-16:00 Renaud Lambiotte (Imperial College London; Institute for Mathematical Sciences) Multirelational Organization of Large-scale Social Networks in an Online World 16:00-16:30 Lin-Yun Lv ( ) TBA Friday 25 / 3 /201 1 Room: 6620 Chairperson: Bing-Hong Wang 10:00-11:00 Jae Don Noh (Department of Physics, University of Seoul, Korea ) Scaling behavior of random walk centrality in general graphs 11:00-11:30 Discussion Lunch Break Room: 6620 Chairperson: Tao Zhou 15:00-16:00
应Lambert Academic Publishing之邀，我写的书 Modularity: The Principle of Evolution in Complex Systems今天出版了。 此书总结了我数年来在复杂网络，金融物理，和免疫系统进化方面的工作。主题围绕着“模块化” (modularity)展开。此书的核心是提出了生物进化中的模块化定律。 Modularity, understanding systems as the combination of separated components, is a prevalent concept in biology, social science and engineering design. This book offers a general theoretical model of emergence of modularity during evolution in changing environments. Evidences from protein interaction network and protein domain network support this theory as a general law of evolution in complex systems. Applying it to animal development, this theory appears to provide an explanation for the occurrence and timing of the Cambrian explosion. Applying it to trade networks, this theory predicts that globalized economy is more sensitive to recessions. This book also introduces a quantitative measure of hierarchy, a statistical method to predict the dominant flu strain and the first theoretical model to explain the bacterial acquired immunity (CRISPR). The information in the book should help shed some light in the study of complex systems, flu vaccine, and bacterial antibiotic resistance. 购买链接: http://www.amazon.com/Modularity-principle-evolution-complex-systems/dp/3844311416 前后封面
The 2011 International Symposium on Complex Networks and Systems (ISCNS'11) http://www.ee.cityu.edu.hk/~wwyu/ISCNS11/index.html Welcome The 2011 International Symposium on Complex Networks and Systems (ISCNS'11) will be held on April 23-24 2011 in Nanjing, China, officially organized by the Department of Mathematics , Southeast University , China. The ISCNS'11 focuses on complex systems and networks, multi-agent systems, nonlinear dynamics and control, systems biology, sensor networks, communication networks, networked control systems, and their applications. The symposium will be sponsored by the Department of Mathematics , Southeast University , China. Latest News We will cover the travel (hard-seat tickets if by trains) and accommodation fees (in the reserved hotel) for some students and young teachers. Note that there are limited quota. Please send your registration form to us as soon as possible. All Rights Reserved 2011 - By ISCNS'2011 General Chairs Jinde Cao (SEU, China) Jijun Liu (SEU, China) Organizing Committees Jinling Liang (SEU, China) Qingshan Liu (SEU, China) Jianquan Lu (SEU, China) Xingmei Xue (SEU, China) Wenwu Yu (SEU, China) Invited Speakers (listed not in any particular order) Confirmed Guanrong Chen (CityU, HK) Zhisheng Duan (Peking University, China) Jinhu Lv (Chinese Academy of Sciences, China) Daniel W. C. Ho (CityU, HK) Yuanqing Xia (Beijing Institute of Technology, China) Unconfirmed Program Schedule 23-24 April, 2011: Two-day Workshop Related Download under construction Send Registration Form To Wenwu Yu Email: wenwuyu@gmail.com ; wwyu@seu.edu.cn Telephone: 0086-15051861330 Registration Form Download Sponsors Department of Mathematics , Southeast University , China Contact Dr. Wenwu Yu Department of Mathematics , Southeast University Nanjing 210096, P. R. China Tel: +86-25-52090596-8531 Fax: +86-25-83792316 Email: wenwuyu@gmail.com ; wwyu@seu.edu.cn
110221-Complex_systems_A_survey.pdf Complex Systems: A Survey M. E. J. Newman Department of Physics, University of Michigan, Ann Arbor, MI 48109 and Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109 A complex system is a system composed of many interacting parts, often called agents, which displays collective behavior that does not follow trivially from the behaviors of the individual parts. Examples include condensed matter systems, ecosystems, stock markets and economies, biological evolution, and indeed the whole of human society. Substantial progress has been made in the quantitative understanding of complex systems, particularly since the 1980s, using a combination of basic theory, much of it derived from physics, and computer simulation. The subject is a broad one, drawing on techniques and ideas from a wide range of areas. Here I give a short survey and an annotated bibliography of resources for those interested in learning about complex systems. I. INTRODUCTION Complex systems is a relatively new and broadly interdisciplinary field that deals with systems composed of many interacting units, often called “agents.” The foundational elements of the field predate the current surge of interest in it, which started in the 1980s, but substantial recent advances in the area coupled with increasing interest both in academia and industry have created new momentum for the study and teaching of the science of complex systems. There is no precise technical definition of a “complex system,” but most researchers in the field would probably agree that it is a system composed of many interacting parts, such that the collective behavior of those parts together is more than the sum of their individual behaviors. The collective behaviors are sometimes also called “emergent” behaviors, and a complex system can thus be said to be a system of interacting parts that displays emergent behavior. Classic examples of complex systems include condensed matter systems, ecosystems, the economy and financial markets, the brain, the immune system, granular materials, road traffic, insect colonies, flocking or schooling behavior in birds or fish, the Internet, and even entire human societies. Unfortunately, complex systems are, as their name makes clear, complex, which makes them difficult to treat scientifically. Experimental observations are of course possible, though these fall largely within the realm of the traditional scientific disciplines and are usually not considered a part of the field of complex systems itself, which is primarily devoted to theoretical developments. Complex systems theory is divided between two contrasting approaches. The first involves the creation and study of simplified mathematical models that do not claim to mimic quantitatively the behavior of real systems but instead try to abstract their most important qualitative elements into a solvable framework from which we can gain scientific insight. The tools used in such studies include dynamical systems theory, information theory, cellular automata, networks, computational complexity theory, and numerical methods. The second approach is to create systematic computer simulations of the interacting parts of a complex system, often down to minute details, and then watch and measure the emergent behaviors that appear. The primary tool for this approach is agent-based simulation, around which a community of computer scientist scientists and software developers has grown up to create software tools for sophisticated computational research in complex systems. This resource letter focuses on the methods and theoretical tools of complex systems, including both the modeling and simulation approaches above, though I also include a short section of references to individual specific complex systems, such as economies or ecosystems, which can serve as a concrete foundation motivating the theoretical studies.
Copyright by SIAM. Unauthorized reproduction of this article is prohibited. SIAM R EVIEW _ c 2010 Society for Industrial and Applied Mathematics Vol. 52, No. 2, pp. 294314 Complex Singularities and the Lorenz Attractor Divakar Viswanath Sonmez Sahuto?glu Abstract. The Lorenz attractor is one of the best-known examples of applied mathematics. However, much of what is known about it is a result of numerical calculations and not of mathematical analysis. As a step toward mathematical analysis, we allow the time variable in the three-dimensional Lorenz system to be complex, hoping that solutions that have resisted analysis on the real line will give up their secrets in the complex plane. Knowledge of singularities being fundamental to any investigation in the complex plane, we build upon earlier work and give a complete and consistent formal development of complex singularities of the Lorenz system using the psi series . The psi series contain two undetermined constants. In addition, the location of the singularity is undetermined as a consequence of the autonomous nature of the Lorenz system. We prove that the psi series converge, using a technique that is simpler and more powerful than that of Hille, thus implying a two-parameter family of singular solutions of the Lorenz system. We pose three questions, answers to which may bring us closer to understanding the connection of complex singularities to Lorenz dynamics. Key words. Lorenz attractor, psi series, complex singularities AMS subject classifications. 34M 35, 37D45 DOI. 10.1137/090753474 Complex Singularities and the Lorenz Attractor
Some Recent Advances in Complex Networks Synchronization _ Guanrong Chen, Xiaofan Wang, Xiang Li, and Jinhu Lv (陈关荣,汪小帆,李翔,吕金虎) 按语: 我把陈关荣等四位合作的文章推荐到这个专栏,完全是为了推动博文比赛. 因为 他们当中有三位是评委,自然将不参加评选. （方锦清2010、3、27） Abstract. The current study of complex dynamical networks is pervading almost all kinds of science, engineering and technology, ranging from mathematics to computers, physics to biology, even to sociology. Its impacts on the modern high-tech industries, financial markets and human life are prominent and will be far-reaching. Research on fundamental properties and dynamical features of such complex networks has indeed become overwhelming. This Chapter presents a brief overview of some past and current studies on the subject of complex dynamical network synchronization, particularly from an engineering and technological perspective. Some commonly concerned issues in the current research of network synchronization, mainly on Some recent advances in complex network synchroniz
Chaos 16, 015104 (2006) Changsong Zhou and Jurgen Kurths 这是一篇我看到的极出色的文章，所研究的复杂网络上的分层同步（我就直译了）问题对识别网络的拓扑结构具有启发性。PRE80，016116（2009）也正是基于此，明确提出了用来网络探测。在没有看到更早的文献之前，我先当它是利用动力学来探测网络度分布的第一文了。如此说，EPL82，68001（2008）可能要觉得有点冤，因为那里宣称是首次。但在仔细阅读这三文章之后，我还是认为这里闪烁的原创性更明亮！ 文章的摘要：We study synchronization behavior in networks of coupled chaotic oscillators with heterogeneous connection degrees. Our focus is on regimes away from the complete synchronization state, when the coupling is not strong enough, when the oscillators are under the influence of noise or when the oscillators are nonidentical. We have found a hierarchical organization of the synchronization behavior with respect to the collective dynamics of the network. Oscillators with more connections (hubs) are synchronized more closely by the collective dynamics and constitute the dynamical core of the network. The numerical observation of this hierarchical synchronization is supported with an analysis based on a mean field approximation and the master stability function. 1.在简介里隐约给出了复杂网络的研究对象主要就是指联接，或称为复杂的拓扑结构。而对于结点的研究则是动力学的范围。所以是否可以将我们研究的对象分为这么两层次：复杂系统是第一层、复杂网络与动力系统（微分动力系统）是第二层，这一层是拓扑结构与单一结点动力学。在第二层次上的单独研究都已经开展得很多了，而第一层的研究则包括常见的什么传播动力学、网络同步等。 2.MSF分析的是网络的完全同步（CS），但是网络的最自然状态往往是非完全同步的。在这种情况下，局部群体行为的分析也是十分令人感兴趣的。这对应以往斑图研究中的发达湍流与全局同步运动（规则斑图之一）之间的状态。当然以前斑图研究的也可以说成是简单连接的网络上的动力学研究。 3.Interestingly, the stability analysis of the CS state can be adopted to provide an understanding of the hierarchical synchronization.这是在简介结尾时说的一句话，可能是对应II（E）部分的，平均场分析。因为这一部分的分析方法与MSF相似。不同的是normalized耦合强度是结点度的显函数，且由之可知度大的结点耦合强度也大。所以可想而知的是有权连接对所讨论的对象也有很大的影响。 4.文章最后一句Our present interest is on self-organization of structures and dynamics due to the interplay between them令人浮想连翩。 5.总体还有个感觉就是工作量很大，做得很细，值得学习。
Fig. 1 The more connection and species, the more unstable Recently my former group published one paper about the stability of complex food network in the journal 'Science'. This is not my direction. But I am near enough to understand their ideas of research. The basic question came from Robert May in the 70s. He found that there is a dilemma between the theory and the observation. People generally believe that the diversity in an ecological system makes it stable. But May showed it mathematically that with increasing connection links and interactive nodes, an ecological system will become more and more unstable. People with the experience of solving large nonlinear differential equations know that the system becomes more and more difficult to have steady states if the number of equations increase. So Which is correct: our intuition or our model? In this papery, they used a small trick before the simulation. If there is a steady state in a dynamic system, you can always re-scale the time for different variables to normalize one steady state to an unit vector = (1, 1, ..., 1). Then you can do usual stability analysis around this point. Based on Monte Carlo sampling of different parameters, you will know how they effect the stability of the system around this steady state locally. Using this half-analytical-half-numeric method, they generated 100 million food webs randomly and made a statistic how different factors effect the stability. They found that more connections and more species really de-stabilize the ecological system. That is, in a forest, if trees, birds, insects etc. form more connections and there are more species, the ecological system in this forest will be more inclined to collapse! (Really unintuitive?) The other conclusion is that the food web will be more stable if top predators like lions, tigers die fast, or they can eat more deer when deer population increases. This kind of idea is actually not new. In 1960s, when Kauffman tried to study the gene regulatory network of the living organism, he did the same thing by creating millions of random networks since the real genetic networks would come decades later (He simply can not wait). Based on his statistics on all these random gene networks, Kauffman concluded that if we need an evolution in Life, we have to have our gene network at the edge of Chaos. That is, it is not either in deterministic system with the fixed number of steady states, nor in totally chaos without any steady states at all. It is just at this delicate zone in between. Could this complexity analysis with large number of simulations will reveal more 'emergent properties' in Life? Let's just wait and see. Ref: Thilo Gross, Lars Rudolf, Simon A. Levin, Ulf Dieckmann, Generalized Models Reveal Stabilizing Factors in Food Webs, Science 7 August 2009, Vol. 325. no. 5941, pp. 747 - 750,DOI: 10.1126/science.1173536 END
Complex09 Call For Participation Welcome to Complex2009 The First International Conference on Complex Sciences: Theory and Applications 发件人Xiao Shi XiaoShi@ntu.edu.sg 回复Xiao Shi XiaoShi@ntu.edu.sg 发送至 COMPLEX_2009@mlist.ntu.edu.sg 日期2009年2月17日 下午7:03 主题Complex09 Call For Participation 邮送域mlist.ntu.edu.sg 隐藏详细信息 2月17日 (3天前) 回复 The interdisciplinary studies on complex systems have gained extensive research interests. Significant impacts have been made by such studies on a wide range of different areas including physics, biology, economics, social sciences, etc., and penetrating into various engineering applications. In the long-term future, the way we understand and cope with the world may all be revolutionized by such studies. Complex'2009, the First International Conference on Complex Sciences: Theory and Applications, aims to provide a unique and convenient platform for people working on theory and applications of complex systems to exchange their ideas and their latest research results. Topics of interests address, but not limited to, the following areas: - Structure and Dynamics of Complex Networks - Complex Biological Systems - Econophysics - Sociophysics - Complex Systems Methods - Complex Systems in Engineering We look forward to your participation in COMPLEX'2009 to make this conference a success. CONFERENCE DATE AND LOCATION February 23-25, 2009, Shanghai, China CONFERENCE PROGRAM The detailed conference program is available at http://www.complex-sys.org/ . The conference features: - Seventeen technical sessions consisting of more than 190 high-quality technical papers - Five collated workshops. - Four Distinguished Keynote Speakers and Fifteen Invited Speakers. COLLATED WORKSHOPS Causality in Complex Systems (ComplexCCS) http://www.complexsystems.net.au/wiki/Complex_%2709_Workshop_on_Causalit y_in_Complex_Systems Complex Engineering Networks (ComplexEN) http://complexen.icstweb.org/ Complexity Theory of Arts and Music (COART) http://coart.icstweb.org/ Modelling and Analysis of Human Dynamics (MANDYN) http://pil.phys.uniroma1.it/~gcalda/Complex2009Satellite/ Social Physics and its Applications (SPA) http://socialphysics.ac.cn/NewsView.Asp?id=46 For further information and registration: http://www.complex-sys.org/ 回复 全部回复 转发
The Complexity Science Group in the Department of Physics Astronomy at the University of Calgary invites applications for a PhD position in the field of complex network theory with applications to neurosciences. The successful applicant is expected to work closely with other members of the Complexity Science Group and members of the Hotchkiss Brain Institute in an interdisciplinary environment. A background in statistical physics, computational physics, time series analysis, complex network theory and/or information theory is beneficial. Applicants should send an email to davidsen(at)phas.ucalgary.ca that includes a CV with a list of publications and a brief statement of research interests. Review of applications will begin on January 30, 2009 and continue until the position is filled. More information about the group is available at www.ucalgary.ca/complexity More detalis about the job in PDF fie: PhD Complex in neuroscience
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