Probabilistic models in biology
Predictive understanding of signaling network kinetics is essential for design of rational intervention strategies in diseases. However, inference of network parameters, such as reaction rates and species abundances, that govern intracellular signaling network dynamics is challenging due to parameter non-identifiability and cell-to-cell variability. We present a maximum entropy (ME) inference of the joint probability distribution over network parameters that reproduces snapshots of cell to cell variability in species abundances measured at multiple time points. We studied a network implicated in many diseases; phosphorylation of protein kinase B (Akt, pAkt) via the EGF/EGFR pathway. Our framework combined with experimental data on cell-to-cell variability in pAkt abundances allowed us to computationally reconstruct the experimentally inaccessible ensemble of trajectories of intracellular pAkt dynamics. Using the ensemble of trajectories, we could predict the fraction of cells that entered the cell cycle after prolonged exposure to EGF. The analysis allowed us explore sub-populations of cells that failed…