Maximum entropy framework for inference of cell population heterogeneity in signaling networks

Predictive models of signaling networks are essential tools for understanding cell population heterogeneity and designing rational interventions in disease. However, using network models to predict signaling dynamics heterogeneity is often challenging due to the extensive variability of signaling parameters across cell populations. Here, we describe a Maximum Entropy-based fRamework for Inference of heterogeneity in Dynamics of sIgAling Networks (MERIDIAN). MERIDIAN allows us to estimate the joint probability distribution over signaling parameters that is consistent with experimentally observed cell-to-cell variability in abundances of network species. We apply the developed approach to investigate the heterogeneity in the signaling network activated by the epidermal growth factor (EGF) and leading to phosphorylation of protein kinase B (Akt). Using the inferred parameter distribution, we also predict heterogeneity of phosphorylated Akt levels and the distribution of EGF receptor abundance hours after EGF stimulation. We discuss how MERIDIAN can be generalized and applied to problems beyond modeling of heterogeneous signaling dynamics.