Emergent decision-making in biological signal transduction networks

The complexity of biochemical intracellular signal transduction networks has led to speculation that the high degree of interconnectivity that exists in these networks transforms them into an information processing network. To test this hypothesis directly, a large scale model was created with the l...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 105; no. 6; pp. 1913 - 1918
Main Authors Helikar, Tomáš, Konvalina, John, Heidel, Jack, Rogers, Jim A
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 12.02.2008
National Acad Sciences
Subjects
Biochemistry
Biological Sciences
Boolean data
Calcium
Decision Making
Information processing
Input output
Model theory
Models, Biological
Parametric models
Physiological regulation
Principal components analysis
Receptors
Robust control
Signal noise
Signal Transduction
Systems Biology
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Summary:The complexity of biochemical intracellular signal transduction networks has led to speculation that the high degree of interconnectivity that exists in these networks transforms them into an information processing network. To test this hypothesis directly, a large scale model was created with the logical mechanism of each node described completely to allow simulation and dynamical analysis. Exposing the network to tens of thousands of random combinations of inputs and analyzing the combined dynamics of multiple outputs revealed a robust system capable of clustering widely varying input combinations into equivalence classes of biologically relevant cellular responses. This capability was nontrivial in that the network performed sharp, nonfuzzy classifications even in the face of added noise, a hallmark of real-world decision-making.
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Author contributions: J.K., J.H., and J.A.R. designed research; T.H., J.K., and J.A.R. performed research; T.H., J.K., and J.A.R. contributed new reagents/analytic tools; T.H., J.K., and J.A.R. analyzed data; and J.A.R. wrote the paper.
Edited by Eugene V. Koonin, National Institutes of Health, Bethesda, MD, and accepted by the Editorial Board December 14, 2007
ISSN:0027-8424
1091-6490
1091-6490
DOI:10.1073/pnas.0705088105