NOTICE: Citizendium is still being set up on its newer server, treat as a beta for now; please see here for more.
Citizendium - a community developing a quality comprehensive compendium of knowledge, online and free. Click here to join and contribute—free
CZ thanks our previous donors. Donate here. Treasurer's Financial Report -- Thanks to our content contributors. --

Biological networks/Bibliography

From Citizendium, the Citizens' Compendium
Jump to: navigation, search
This article is developing and not approved.
Main Article
Related Articles  [?]
Bibliography  [?]
External Links  [?]
Citable Version  [?]
A list of key readings about Biological networks.
Please sort and annotate in a user-friendly manner. For formatting, consider using automated reference wikification.


  • Képès F. (editor) (2007) Biological networks. Volume 3 of Complex Systems and Interdisciplinary Science. World Scientific. ISBN 9789812706959. | Google Books preview.
    • From Preface: In network models, the relevant components in a system are identified as nodes. The interactions between these components are represented as links between nodes. Following this abstraction step, it becomes possible to study the topological properties of the network thus obtained. The generality and uniformity of the network representation make it possible to compare systems of very different types. At present, pure and combined network-based approaches still present fascinating challenges with respect to topological properties, and to temporal and spatial development.
  • Newman MEJ, Barabâasi AL, Watts DJ. (2006) The structure and dynamics of networks. Princeton, N.J: Princeton University Press.
  • Watts DJ. (1999) Small worlds: the dynamics of networks between order and randomness. Princeton, N.J: Princeton University Press, ISBN 0691005419

Book Chapters

Journal articles

  • Weitz JS, Benfey PN, Wingreen NS. (2007) Evolution, Interactions, and Biological Networks. PLoS Biol 5(1): e11.
    • Excerpt: As [Theodosius] Dobzhansky famously noted, nothing in biology makes sense except in the light of evolution...This is particularly true of biological networks, and we believe that the lens of evolution provides an exciting opportunity to link disciplines in ways that address fundamental challenges in biology.
  • Nitschke JR. (2009) Systems chemistry: Molecular networks come of age. Nature 462:736-738.
    • Excerpt: There are two main questions [systems chemists are asking]. The first is how the complex networks of molecules found on the prebiotic Earth might have crossed the threshold of life...The second question is how collections of molecules self-assemble into complex structures, and how secondary interactions between molecules and competition for molecular building blocks lead to complex behaviour within self-assembling systems.
  • Bray D. (2003) Molecular Networks: The Top-Down View. Science 301:1864-1865.
    • Excerpt: Everything in a living cell is, of course, connected to everything else, and interactions between macromolecules through multiple noncovalent bonds are the very fabric of life. It is therefore an attractive notion that, by taking a top-down view of protein-protein interactions, enzymatic pathways, signaling pathways, and gene regulatory pathways, we will gain a better perspective of how they work.
  • Alon U. (2003) Biological Networks: The Tinkerer as an Engineer. Science 301:1866-1867.
    • Excerpt: This viewpoint [article] comments on recent advances in understanding the design principles of biological networks. It highlights the surprising discovery of "good-engineering" principles in biochemical circuitry that evolved by random tinkering.
  • Barabási A-L, Albert R. (1999) Emergence of Scaling in Random Networks. Science 286:509-511.
    • Excerpt: Here we report on the existence of a high degree of self-organization characterizing the large-scale properties of complex networks. Exploring several large databases describing the topology of large networks that span fields as diverse as the WWW or citation patterns in science, we show that, independent of the system and the identity of its constituents, the probability P(k) that a vertex in the network interacts with k other vertices decays as a power law, following P(k) ~k-gamma- . This result indicates that large networks self-organize into a scale-free state, a feature unpredicted by all existing random network models.