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Proc Natl Acad Sci U S A. 2008 Feb 12;105(6):1913-8. doi: 10.1073/pnas.0705088105. Epub 2008 Feb 4.

Emergent decision-making in biological signal transduction networks.

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  • 1Department of Pathology and Microbiology, University of Nebraska Medical Center, 983135 Nebraska Medical Center, Omaha, NE 68198, USA.

Abstract

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.

PMID:
18250321
[PubMed - indexed for MEDLINE]
PMCID:
PMC2538858
Free PMC Article
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