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PLoS One. 2015 Jun 1;10(6):e0125777. doi: 10.1371/journal.pone.0125777. eCollection 2015.

Noise-driven causal inference in biomolecular networks.

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IBM T. J. Watson Research Center, 1101 Kitchawan Road, Route 134, Yorktown Heights, N.Y. 10598, United States of America.
ImmunoDynamics Group, Program in Computational Biology and Immunology, Memorial Sloan- Kettering Cancer Center, 1275 York Avenue, Box 460, New York, N.Y. 10065, United States of America.


Single-cell RNA and protein concentrations dynamically fluctuate because of stochastic ("noisy") regulation. Consequently, biological signaling and genetic networks not only translate stimuli with functional response but also random fluctuations. Intuitively, this feature manifests as the accumulation of fluctuations from the network source to the target. Taking advantage of the fact that noise propagates directionally, we developed a method for causation prediction that does not require time-lagged observations and therefore can be applied to data generated by destructive assays such as immunohistochemistry. Our method for causation prediction, "Inference of Network Directionality Using Covariance Elements (INDUCE)," exploits the theoretical relationship between a change in the strength of a causal interaction and the associated changes in the single cell measured entries of the covariance matrix of protein concentrations. We validated our method for causation prediction in two experimental systems where causation is well established: in an E. coli synthetic gene network, and in MEK to ERK signaling in mammalian cells. We report the first analysis of covariance elements documenting noise propagation from a kinase to a phosphorylated substrate in an endogenous mammalian signaling network.

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