Bioinformatics Advance Access first published online on October 12, 2004
This version published online on January 18, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti056
1 Biological Engineering Division, Massachusetts Institute of Technology, Cambridge, MA 02139
* 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 are able to characterize 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, previously reported by Prudhomme et al. [Proc. Natl. Acad. Sci. USA (2004)]. 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 the Bayesian networks can capture the linear, nonlinear, and multi-state 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/.
Received April 15, 2004
Revised July 3, 2004
Accepted July 20, 2004
Article
Bayesian analysis of signaling networks governing embryonic stem cell fate decisions
2 Children's Hospital, Harvard Medical School, Boston, MA 02115
Douglas A. Lauffenburger, E-mail: lauffen{at}mit.edu
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