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Calibration of Rule-Based Stochastic Biochemical Models using Statistical Model Checking

Computational modeling of biochemical networks assists in understanding the functioning of a biochemical process as well as enables in silico experimentation. However, such models are often stochastic in nature and contain unknown parameters that must be estimated against expert behavioral specifications. In this paper, we present a new statistical model checking approach for calibrating rule-based stochastic biochemical models such that the calibrated model satisfies a given behavioral temporal logic specification. Our approach computes a distribution of a quantitative tightness metric describing how well a specification is satisfied by a parameterized rule-based stochastic biochemical model. Then, sequential selection of the larger of the means of two such distributions is used to guide a simulated annealing based search for the correctly calibrated model that satisfies the given behavioral temporal logic specification. Our experimental results demonstrate the successful calibration of a rule-based T-cell receptor model while achieving a significant speedup in time.