Noise-driven causal inference in biomolecular networks

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...

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Bibliographic Details
Published in:PloS one 2015-06, Vol.10 (6), p.e0125777-e0125777
Main Authors: Prill, Robert J, Vogel, Robert, Cecchi, Guillermo A, Altan-Bonnet, Grégoire, Stolovitzky, Gustavo
Format: Article
Language:English
Subjects:
Algorithms
Biology
Causality
Causation
Connectivity
Covariance matrix
E coli
Escherichia coli
Extracellular signal-regulated kinase
Fluctuations
Gene expression
Immunohistochemistry
Immunology
Inference
Kinases
Lymphocytes
Mammalian cells
Mammals
Methods
Models, Biological
Noise
Noise propagation
Phosphorylation
Physics
Protein expression
Proteins
Ribonucleic acid
RNA
Science
Signaling
Stochasticity
Substrates
Time series
Variables
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0125777