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}


How do cells perceive their environment and produce appropriate responses? The sensing and coordination of proper responses is coordinated by intricate biochemical networks that integrate signals and compute biological responses with high fidelity and reproducibility. Modern “omics” (proteomics, genomics, lipidomics, and metabolomics) techniques have identified many of the components of these networks, but researchers are now faced with the challenge of unraveling how the components are connected and how they compute. To elucidate these network properties, various mathematical and computational modeling techniques are used because their complexity defeats human intuition. The results indicate that signaling networks show a wide diversity of behaviors encoded in their design, but that they are also very flexible, with cells adapting their networks in response to different contexts by changing connectivities. The remaining challenges include integration of data generated with methods for the analysis of different types of networks (gene regulation, protein modification, and metabolic networks), and generation of coherent network interpretations that are widely accessible and can generate new hypotheses that are experimentally testable and that will push the knowledge frontier in biology and biomedicine.


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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