Emergent collective properties, networks and information in biology

Bibliographic Information

Emergent collective properties, networks and information in biology

J. Ricard

(New comprehensive biochemistry / general editors, A. Neuberger, L.L.M. van Deenen, v. 40)

Elsevier, 2006

1st ed

Available at  / 16 libraries

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Note

Includes bibliographical references and index

Description and Table of Contents

Description

The concept of network as a mathematical description of a set of states, or events, linked according to a certain topology has been developed recently and has led to a novel approach of real world. This approach is no doubt important in the field of biology. In fact biological systems can be considered networks. Thus, for instance, an enzyme-catalysed reaction is a network that links, according to a certain topology, the various states of the protein and of its complexes with the substrates and products of the chemical reaction. Connections between neurons, social relations in animal and human populations are also examples of networks. Hence there is little doubt that the concept of network transgresses the boundaries between traditional scientific disciplines. This book is aimed at discussing in physical terms these exciting new topics on simple protein model lattices, supramolecular protein edifices, multienzyme and gene networks.

Table of Contents

  • PrefaceMolecular stereospecific recognition and reduction in cell biology
  • Mathematical prelude: elementary set and probability theory
  • Biological networks
  • Information and communication in living systems
  • Statistical mechanics of network information, integration and emergence
  • On the mechanistic causes of network information, integration and emergence
  • Information and organization of metabolic networks
  • Functional connections in multienzyme complexes, information and generalized microscopic reversibility
  • Conformation changes and information flow in protein edifices
  • Gene networks
  • Stochastic fluctuations and network dynamicsIndex

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