Note to users. If you're seeing this message, it means that your browser cannot find this page's style/presentation instructions -- or possibly that you are using a browser that does not support current Web standards. Find out more about why this message is appearing, and what you can do to make your experience of our site the best it can be.

Subscribe

Logo for

PNAS 105 (6): 1913-1918

Copyright © 2008 by the National Academy of Sciences.


BIOLOGICAL SCIENCES / CELL BIOLOGY

Emergent decision-making in biological signal transduction networks

Tomás Helikar*, John Konvalina{dagger}, Jack Heidel{dagger}, and Jim A. Rogers*,{dagger},{ddagger}

*Department of Pathology and Microbiology, University of Nebraska Medical Center, 983135 Nebraska Medical Center, Omaha, NE 68198; and {dagger}Department of Mathematics, University of Nebraska, 6001 Dodge Street, Omaha, NE 68182

Edited by Eugene V. Koonin, National Institutes of Health, Bethesda, MD, and accepted by the Editorial Board December 14, 2007

Received for publication May 30, 2007.

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.

Key Words: information processing • systems biology


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.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission. E.V.K. is a guest editor invited by the Editorial Board.

This article contains supporting information online at www.pnas.org/cgi/content/full/0705088105/DC1.

{ddagger}To whom correspondence should be addressed. E-mail: jrogers{at}unmc.edu

© 2008 by The National Academy of Sciences of the USA


THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES:
Dynamical Systems Approach to Endothelial Heterogeneity.
E. R. Regan and W. C. Aird (2012)
Circ. Res. 111, 110-130
   Abstract »    Full Text »    PDF »
The effects of feedback loops on disease comorbidity in human signaling networks.
D.-H. Le and Y.-K. Kwon (2011)
Bioinformatics 27, 1113-1120
   Abstract »    Full Text »    PDF »
Beyond the wiring diagram: signalling through complex neuromodulator networks.
V. Brezina (2010)
Phil Trans R Soc B 365, 2363-2374
   Abstract »    Full Text »    PDF »
Fault Diagnosis Engineering of Digital Circuits Can Identify Vulnerable Molecules in Complex Cellular Pathways.
A. Abdi, M. B. Tahoori, and E. S. Emamian (2008)
Science Signaling 1, ra10
   Abstract »    Full Text »    PDF »

To Advertise     Find Products


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