Emergent decision-making in biological signal transduction networks

Tomas Helikar, John Konvalina, Jack Heidel, Jim A Rogers

Research output: Contribution to journalArticle

112 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1913-1918
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume105
Issue number6
DOIs
StatePublished - Feb 12 2008

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Information Services
Automatic Data Processing
Cluster Analysis
Noise
Signal Transduction
Decision Making

Keywords

  • Information processing
  • Systems biology

ASJC Scopus subject areas

  • General

Cite this

Emergent decision-making in biological signal transduction networks. / Helikar, Tomas; Konvalina, John; Heidel, Jack; Rogers, Jim A.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 105, No. 6, 12.02.2008, p. 1913-1918.

Research output: Contribution to journalArticle

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