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}ucd.ie

Abstract

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.

Citation:

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

Signaling networks in MS: A systems-based approach to developing new pharmacological therapies
E. Kotelnikova, M. Bernardo-Faura, G. Silberberg, N. A. Kiani, D. Messinis, I. N. Melas, L. Artigas, E. Schwartz, I. Mazo, M. Masso et al.
Mult Scler 21, 138-146 (1 February 2015)

Network quantification of EGFR signaling unveils potential for targeted combination therapy
B. Klinger, A. Sieber, R. Fritsche-Guenther, F. Witzel, L. Berry, D. Schumacher, Y. Yan, P. Durek, M. Merchant, R. Schafer et al.
Mol Syst Biol 9, 673-673 (21 July 2014)

Computational Modeling of ERBB2-Amplified Breast Cancer Identifies Combined ErbB2/3 Blockade as Superior to the Combination of MEK and AKT Inhibitors
D. C. Kirouac, J. Y. Du, J. Lahdenranta, R. Overland, D. Yarar, V. Paragas, E. Pace, C. F. McDonagh, U. B. Nielsen, M. D. Onsum et al.
Sci Signal 6, ra68-ra68 (13 August 2013)

Automatic Generation of Predictive Dynamic Models Reveals Nuclear Phosphorylation as the Key Msn2 Control Mechanism
M. Sunnaker, E. Zamora-Sillero, R. Dechant, C. Ludwig, A. G. Busetto, A. Wagner, and J. Stelling
Sci Signal 6, ra41-ra41 (28 May 2013)

Hypoxia induces a phase transition within a kinase signaling network in cancer cells
W. Wei, Q. Shi, F. Remacle, L. Qin, D. B. Shackelford, Y. S. Shin, P. S. Mischel, R. D. Levine, and J. R. Heath
Proc. Natl. Acad. Sci. USA 110, E1352-E1360 (9 April 2013)

Modeling Regulatory Networks to Understand Plant Development: Small Is Beautiful
A. M. Middleton, E. Farcot, M. R. Owen, and T. Vernoux
Plant Cell 24, 3876-3891 (1 October 2012)

Science Signaling. ISSN 1937-9145 (online), 1945-0877 (print). Pre-2008: Science's STKE. ISSN 1525-8882