BioMETA: A multiple specification parameter estimation system for stochastic biochemical models

The inherent behavioral variability exhibited by stochastic biochemical systems makes it a challenging task for human experts to manually analyze them. Computational modeling of stochastic biochemical systems helps in predicting the behaviors of the underlying biochemical processes but at the same time introduces the presence of several unknown parameters. A key challenge faced in this scenario is to determine the values of these unknown parameters against known behavioral specifications. We p resent a new method, B ioMETA, to address this problem such that a single set of parameter values causes a parameterized stochastic biochemical model to satisfy all the given probabilistic temporal logic behavioral specifications simultaneously. Our method is based on combining a multiple hypothesis testing based statistical model checking technique with simulated annealing search to look for a single set of parameter values so that the given parameterized model satisfies multiple probabilistic behavioral specifications. W e s tudy two stochastic rule-based models of biochemical receptors, namely, FcεRI and T-cell as our benchmarks to evaluate the usefulness of the presented method. Our experimental results successfully estimate 26 parameters of FcεRI and 29 parameters of T-cell receptor model against three probabilistic temporal logic behavioral specifications each.