Computational Approaches for Analyzing Information Flow in Biological Networks

Sci. Signal., 17 April 2012
Vol. 5, Issue 220, p. re1
DOI: 10.1126/scisignal.2002961

Computational Approaches for Analyzing Information Flow in Biological Networks

  1. Boris Kholodenko1,
  2. Michael B. Yaffe2, and
  3. Walter Kolch1,3,*
  1. 1Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland.
  2. 2Departments of Biology and Biological Engineering, David H. Koch Institute for Integrative Cancer Research at MIT, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 76-353, Cambridge, MA 02139, USA.
  3. 3Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland.
  1. *Corresponding author. E-mail: walter.kolch{at}


The advancements in “omics” (proteomics, genomics, lipidomics, and metabolomics) technologies have yielded large inventories of genes, transcripts, proteins, and metabolites. The challenge is to find out how these entities work together to regulate the processes by which cells respond to external and internal signals. Mathematical and computational modeling of signaling networks has a key role in this task, and network analysis provides insights into biological systems and has applications for medicine. Here, we review experimental and theoretical progress and future challenges toward this goal. We focus on how networks are reconstructed from data, how these networks are structured to control the flow of biological information, and how the design features of the networks specify biological decisions.


B. Kholodenko, M. B. Yaffe, and W. Kolch, Computational Approaches for Analyzing Information Flow in Biological Networks. Sci. Signal. 5, re1 (2012).

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