PT  - JOURNAL ARTICLE
AU  - Purushottam D. Dixit
AU  - Eugenia Lyashenko
AU  - Mario Niepel
AU  - Dennis Vitkup
TI  - Maximum entropy framework for inference of cell population heterogeneity in signaling networks
AID  - 10.1101/137513
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 137513
4099  - http://biorxiv.org/content/early/2017/05/12/137513.short
4100  - http://biorxiv.org/content/early/2017/05/12/137513.full
AB  - The dynamics of intracellular signaling networks can vary substantially among cells in a population. Predictive models of signaling networks are key to our understanding of cellular function and in design of rational interventions in disease. However, using network models to predict heterogeneity in signaling network dynamics is challenging due to cell to cell variability of network parameters, such as reaction rates and species abundances, and parameter non-identifiability. In this work, we present an inference framework based on the principle of maximum entropy (ME) to estimate the joint probability distribution over network parameters that is consistent with experimentally measured cell to cell variability in abundances of network species. We apply the framework to study the heterogeneity in the signaling network activated by the epidermal growth factor (EGF) resulting in phosphorylation of protein kinase B (Akt); a central signaling hub in mammalian cells. Notably, the inferred parameter distribution allows us to accurately predict population heterogeneity in phosphorylated Akt (pAkt) levels at early and late times after EGF stimulation as well as the heterogeneity in the levels of cell surface EGF receptors (sEGFRs) after prolonged stimulation with EGF. We discuss how the developed framework can be generalized and applied to problems beyond signaling networks.