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Bioinformatics Advance Access originally published online on October 12, 2004
Bioinformatics 2005 21(6):741-753; doi:10.1093/bioinformatics/bti056
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© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Bayesian analysis of signaling networks governing embryonic stem cell fate decisions

Peter J. Woolf 1,2,*, Wendy Prudhomme 3, Laurence Daheron 4, George Q. Daley 3 and Douglas A. Lauffenburger 2

1Department of Chemical Engineering, University of Michigan Room 3320, G. G. Brown Building, 2300 Hayward Street, Ann Arbor, MI 48109-2125, USA
2Department of Biomedical Engineering, University of Michigan Room 3320, G. G. Brown Building, 2300 Hayward Street, Ann Arbor, MI 48109-2125, USA
3Biological Engineering Division, Massachusetts Institute of Technology Cambridge, MA 02139, USA
4Children's Hospital, Harvard Medical School Boston, MA 02115, USA

*To whom correspondence should be addressed.

Motivation: Signaling events that direct mouse embryonic stem (ES) cell self-renewal and differentiation are complex and accordingly difficult to understand in an integrated manner. We address this problem by adapting a Bayesian network learning algorithm to model proteomic signaling data for ES cell fate responses to external cues. Using this model we were able to characterize the signaling pathway influences as quantitative, logic-circuit type interactions. Our experimental dataset includes measurements for 28 signaling protein phosphorylation states across 16 different factorial combinations of cytokine and matrix stimuli as reported previously.

Results: The Bayesian network modeling approach allows us to uncover previously reported signaling activities related to mouse ES cell self-renewal, such as the roles of LIF and STAT3 in maintaining undifferentiated ES cell populations. Furthermore, the network predicts novel influences such as between ERK phosphorylation and differentiation, or RAF phosphorylation and differentiated cell proliferation. Visualization of the influences detected by the Bayesian network provides intuition about the underlying physiology of the signaling pathways. We demonstrate that the Bayesian networks can capture the linear, nonlinear and multistate logic interactions that connect extracellular cues, intracellular signals and consequent cell functional responses.

Availability: Datasets and software are available online from http://sysbio.engin.umich.edu/~pwoolf/mouseES/

Contact: pwoolf{at}umich.edu

Supplementary information: http://sysbio.engin.umich.edu/~pwoolf/mouseES/


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