Parameter discovery for stochastic computational models in systems biology using Bayesian model checking

Parameterized probabilistic complex computational (P²C²) models are being increasingly used in computational systems biology for analyzing biological systems. A key challenge is to build mechanistic P²C² models by combining prior knowledge and empirical data, given that certain system properties are unknown. These unknown components are incorporated into a model as parameters and determining their values has traditionally been a process of trial and error. We present a new algorithmic procedure for discovering parameters in agent-based models of biological systems against behavioral specifications mined from large data-sets. Our approach uses Bayesian model checking, sequential hypothesis testing, and stochastic optimization to synthesize parameters of P²C² models. We demonstrate our algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide in a clinical agent-based model of the dynamics of acute inflammation that guarantee a set of desired clinical outcomes with high probability.