CiteULike: Tag diktyology

Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry and Engineering

Steven Strogatz — 2005-09-06T21:00:17-00:00

(15 January 2001)

Signal transduction network motifs and biological memory

Zhangang Han — 2007-05-14T15:22:08-00:00

Journal of Theoretical Biology, Vol. 246, No. 4. (21 June 2007), pp. 755-761.

Memory is a ubiquitous phenomenon in biological systems, yet the mechanisms responsible for memory, and how to manipulate it at the subcellular level, remain poorly understood. Subjected to transient stimuli, biological systems can exhibit short early responses and/or prolonged (or permanent) late responses. Experimental evidence suggests that early responses (short-term memory) involve post-translational modification of existing proteins and/or their intracellular relocalization, whereas late responses (long-term memory) depend on new protein synthesis. Although this provides an intuitive explanation at the basic molecular level, it does little to clarify the important dynamics that actually maintain memory at the systems level. In this study, we use mathematical modeling to study dynamical mechanisms of biological memory. We first examined the response of four fundamental motifs (positive/negative feedforward and feedback) to external stimuli. Because motifs do not exist in isolation within the cell, we then combined these motifs to form signaling modules to understand how they confer biological memory. These motifs, and different combinations thereof, displayed distinct behavior in response to external stimuli. The principles described in this study have important implications for experimental approaches to identify the mechanisms for biological memory and for the development of therapeutic strategies to modulate signaling network responses in the setting of human disease.

Protein networks in disease

Trey Ideker — 2008-04-01T18:31:26-00:00

Genome Res., Vol. 18, No. 4. (1 April 2008), pp. 644-652.

During a decade of proof-of-principle analysis in model organisms, protein networks have been used to further the study of molecular evolution, to gain insight into the robustness of cells to perturbation, and for assignment of new protein functions. Following these analyses, and with the recent rise of protein interaction measurements in mammals, protein networks are increasingly serving as tools to unravel the molecular basis of disease. We review promising applications of protein networks to disease in four major areas: identifying new disease genes; the study of their network properties; identifying disease-related subnetworks; and network-based disease classification. Applications in infectious disease, personalized medicine, and pharmacology are also forthcoming as the available protein network information improves in quality and coverage. 10.1101/gr.071852.107

The Small-World Phenomenon: An Algorithmic Perspective

Jon Kleinberg — 2005-01-02T16:34:15-00:00

(# 2000)

Long a matter of folklore, the "small-world phenomenon" -- the principle that we are all linked by short chains of acquaintances -- was inaugurated as an area of experimental study in the social sciences through the pioneering work of Stanley Milgram in the 1960's. This work was among the first to make the phenomenon quantitative, allowing people to speak of the "six degrees of separation" between any two people in the United States. Since then, a number of network models have been proposed as...

Learning kernels from biological networks by maximizing entropy.

K Tsuda — 2005-11-09T11:07:31-00:00

Bioinformatics, Vol. 20 Suppl 1 (4 August 2004)

MOTIVATION: The diffusion kernel is a general method for computing pairwise distances among all nodes in a graph, based on the sum of weighted paths between each pair of nodes. This technique has been used successfully, in conjunction with kernel-based learning methods, to draw inferences from several types of biological networks. RESULTS: We show that computing the diffusion kernel is equivalent to maximizing the von Neumann entropy, subject to a global constraint on the sum of the Euclidean distances between nodes. This global constraint allows for high variance in the pairwise distances. Accordingly, we propose an alternative, locally constrained diffusion kernel, and we demonstrate that the resulting kernel allows for more accurate support vector machine prediction of protein functional classifications from metabolic and protein-protein interaction networks. AVAILABILITY: Supplementary results and data are available at noble.gs.washington.edu/proj/maxent

Navigation in a small world

JM Kleinberg — 2004-11-22T00:17:30-00:00

Nature, Vol. 406, No. 6798. (24 August 2000)

Small-world phenomena and the dynamics of information

J Kleinberg — 2005-10-14T11:59:50-00:00

(2001)

Introduction The problem of searching for information in networks like the World Wide Web can be approached in a variety of ways, ranging from centralized indexing schemes to decentralized mechanisms that navigate the underlying network without knowledge of its global structure. The decentralized approach appears in a variety of settings: in the behavior of users browsing the Web by following hyperlinks; in the design of focused crawlers [4, 5, 8] and other agents that explore the Web's links...

Network Forms of Organization

Joel Podolny — 2005-03-26T19:05:18-00:00

Initial sociological interest in network forms of organization was motivated in part by a critique of economic views of organization. Sociologists sought to highlight the prevalence and functionality of organizational forms that could not be classified as markets or hierarchies. As a result of this work, we now know that network forms of organization foster learning, represent a mechanism for the attainment of status or legitimacy, provide a variety of economic benefits, facilitate the management of resource dependencies, and provide considerable autonomy for employees. However, as sociologists move away from critiquing what are now somewhat outdated economic views, they need to balance the exclusive focus on prevalence and functionality with attention to constraint and dysfunctionality. The authors review work that has laid a foundation for this broader focus and suggest analytical concerns that should guide this literature as it moves forward.

A natural class of robust networks.

M Aldana — 2007-01-11T10:42:30-00:00

Proc Natl Acad Sci U S A, Vol. 100, No. 15. (22 July 2003), pp. 8710-8714.

As biological studies shift from molecular description to system analysis we need to identify the design principles of large intracellular networks. In particular, without knowing the molecular details, we want to determine how cells reliably perform essential intracellular tasks. Recent analyses of signaling pathways and regulatory transcription networks have revealed a common network architecture, termed scale-free topology. Although the structural properties of such networks have been thoroughly studied, their dynamical properties remain largely unexplored. We present a prototype for the study of dynamical systems to predict the functional robustness of intracellular networks against variations of their internal parameters. We demonstrate that the dynamical robustness of these complex networks is a direct consequence of their scale-free topology. By contrast, networks with homogeneous random topologies require fine-tuning of their internal parameters to sustain stable dynamical activity. Considering the ubiquity of scale-free networks in nature, we hypothesize that this topology is not only the result of aggregation processes such as preferential attachment; it may also be the result of evolutionary selective processes.

Exploration of scale-free networks

Thomas Petermann — 2006-06-15T09:20:33-00:00

(6 Jan 2004)

The increased availability of data on real networks has favoured an explosion of activity in the elaboration of models able to reproduce both qualitatively and quantitatively the measured properties. What has been less explored is the reliability of the data, and whether the measurement technique biases them. Here we show that tree-like explorations (similar in principle to traceroute) can indeed change the measured exponents of a scale-free network.

The shortest path to complex networks

S Dorogovtsev — 2004-11-22T00:17:30-00:00

(24 July 2004)

1. The birth of network science. 2. What are random networks? 3. Adjacency matrix. 4. Degree distribution. 5. What are simple networks? Classical random graphs. 6. Birth of the giant component. 7. Topology of the Web. 8.Uncorrelated networks. 9. What are small worlds? 10. Real networks are mesoscopic objects. 11. What are complex networks? 12. The configuration model. 13. The absence of degree--degree correlations. 14.Networks with correlated degrees.15.Clustering. 16. What are small-world networks? 17. `Small worlds' is not the same as `small-world networks'. 18. Fat-tailed degree distributions. 19.Reasons for the fat-tailed degree distributions. 20. Preferential linking. 21. Condensation of edges. 22. Cut-offs of degree distributions. 23. Reasons for correlations in networks. 24. Classical random graphs cannot be used for comparison with real networks. 25. How to measure degree--degree correlations. 26. Assortative and disassortative mixing. 27. Disassortative mixing does not mean that vertices of high degrees rarely connect to each other. 28. Reciprocal links in directed nets. 29. Ultra-small-world effect. 30. Tree ansatz. 31.Ultraresilience against random failures. 32. When correlated nets are ultraresilient. 33. Vulnerability of complex networks. 34. The absence of an epidemic threshold. 35. Search based on local information. 36.Ultraresilience disappears in finite nets. 37.Critical behavior of cooperative models on networks. 38. Berezinskii-Kosterlitz-Thouless phase transitions in networks. 39.Cascading failures. 40.Cliques & communities. 41. Betweenness. 42.Extracting communities. 43. Optimal paths. 44.Distributions of the shortest-path length & of the loop's length are narrow. 45. Diffusion on networks. 46. What is modularity? 47.Hierarchical organization of networks. 48. Convincing modelling of real-world networks:Is it possible? 49. The small Web..

Detecting communities in large networks

Andrea Capocci — 2005-06-09T12:05:56-00:00

(20 February 2004)

We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and links orientations. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable to the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words and in uncovering mental association patterns.

Game Theory Applications in Network Reliability

H Karaa — 2006-12-15T03:44:38-00:00

Communications, 2006 23rd Biennial Symposium on (2006), pp. 236-239.

Biological impacts and context of network theory.

E Almaas — 2007-05-13T14:17:00-00:00

J Exp Biol, Vol. 210, No. Pt 9. (1 May 2007), pp. 1548-1558.

Many complex systems can be represented and analyzed as networks, and examples that have benefited from this approach span the natural sciences. For instance, we now know that systems as disparate as the World Wide Web, the Internet, scientific collaborations, food webs, protein interactions and metabolism all have common features in their organization, the most salient of which are their scale-free connectivity distributions and their small-world behavior. The recent availability of large-scale datasets that span the proteome or metabolome of an organism have made it possible to elucidate some of the organizational principles and rules that govern their function, robustness and evolution. We expect that combining the currently separate layers of information from gene regulatory networks, signal transduction networks, protein interaction networks and metabolic networks will dramatically enhance our understanding of cellular function and dynamics.

Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life

Albert-Laszlo Barabasi — 2005-02-27T02:19:34-00:00

(01 April 2003)

A cocktail party. A terrorist cell. Ancient bacteria. An international conglomerate. <br><br> All are networks, and all are a part of a surprising scientific revolution. Albert-L&aacuteszl&oacute Barab&aacutesi, the nation's foremost expert in the new science of networks, takes us on an intellectual adventure to prove that social networks, corporations, and living organisms are more similar than previously thought. Grasping a full understanding of network science will someday allow us to design blue-chip businesses, stop the outbreak of deadly diseases, and influence the exchange of ideas and information. Just as James Gleick brought the discovery of chaos theory to the general public, Linked tells the story of the true science of the future.

Network thermodynamics and complexity: a transition to relational systems theory.

DC Mikulecky — 2006-06-19T16:24:34-00:00

Comput Chem, Vol. 25, No. 4. (July 2001), pp. 369-391.

Most systems of interest in today's world are highly structured and highly interactive. They cannot be reduced to simple components without losing a great deal of their system identity. Network thermodynamics is a marriage of classical and non-equilibrium thermodynamics along with network theory and kinetics to provide a practical framework for handling these systems. The ultimate result of any network thermodynamic model is still a set of state vector equations. But these equations are built in a new informative way so that information about the organization of the system is identifiable in the structure of the equations. The domain of network thermodynamics is all of physical systems theory. By using the powerful circuit simulator, the Simulation Program with Integrated Circuit Emphasis (SPICE), as a general systems simulator, any highly non-linear stiff system can be simulated. Furthermore, the theoretical findings of network thermodynamics are important new contributions. The contribution of a metric structure to thermodynamics compliments and goes beyond other recent work in this area. The application of topological reasoning through Tellegen's theorem shows that a mathematical structure exists into which all physical systems can be represented canonically. The old results in non-equilibrium thermodynamics due to Onsager can be reinterpreted and extended using these new, more holistic concepts about systems. Some examples are given. These are but a few of the many applications of network thermodynamics that have been proven to extend our capacity for handling the highly interactive, non-linear systems that populate both biology and chemistry. The presentation is carried out in the context of the recent growth of the field of complexity science. In particular, the context used for this discussion derives from the work of the mathematical biologist, Robert Rosen.