Emergent decision-making in biological signal transduction networks

Authors

Tomáš Helikar, University of Nebraska‐LincolnFollow
John Konvalina, University of Nebraska at Omaha
Jack Heidel, University of Nebraska at Omaha
Jim A. Rogers, University of Nebraska Medical CenterFollow

Document Type

Article

Date of this Version

2-12-2008

Citation

PNAS (February 12, 2008) vol. 105, no. 6, pp. 1913-1918.

Comments

Copyright © 2008 by The National Academy of Sciences of the USA. Used by permission.

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.