Bayesian analysis of signaling networks governing embryonic stem cell fate decisions.
BACKGROUND
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.
Woolf
Peter J
PJ
Department of Chemical Engineering, University of Michigan, Room 3320, G. G. Brown Building, 2300 Hayward Street, Ann Arbor, MI 48109-2125, USA. pwoolf@umich.edu
Prudhomme
Wendy
W
Daheron
Laurence
L
Daley
George Q
GQ
Lauffenburger
Douglas A
DA
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D002454:Cell Differentiation / Q000502:physiology; D049109:Cell Proliferation; D002470:Cell Survival / Q000502:physiology; D002478:Cells, Cultured; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D051379:Mice; D008954:Models, Biological; D015233:Models, Statistical; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D013234:Stem Cells / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism
D020543:Proteome; D014157:Transcription Factors