Biochemical reaction networks represent complex cellular regulatory mechanisms. These networks are typically analyzed using discrete stochastic simulation models. The models may involve numerous reactions involving a large number of chemical species, governed by highly uncertain parameters.

Given existing data pertaining to a biochemical reaction network, one is often interested in inferring the values of the model parameters that likely generated the data. The data itself may come from models simulated in the past, or physical experiments. Approximate Bayesian Computation (ABC) is a proven approach that effectively solves such parameter inference problems by using simulation models as a tool to find the region in the parameter space corresponding to least deviation from given data.

The rejection sampling algorithm forms the basis of the ABC framework. Samples are drawn from a specified prior distribution, and subsequently simulated. The simulated responses are compared to existing data by means of a distance function and appropriate summary statistics. Samples that result in distance function values below a specified tolerance threshold are accepted, and the rest rejected. The sampling algorithm proceeds until the desired number of accepted samples have been obtained. The inferred parameters are then reported as the mean parameter values corresponding to the accepted samples.

Design choices such as selection of distance functions, summary statistics and acquisition function for the inference process have a deep impact on the solution quality. Furthermore, increasing problem complexity often leads to impractically high inference times using rejection sampling.

Our research explores methods to accelerate high-quality parameter inference by leveraging state-of-the-art methods from the fields of computational biology, machine learning, optimization and statistics. Some of our active research topics include investigating intelligent construction of priors, methods for automated large-scale summary statistic selection, and training fast local and global approximations or surrogate models of computationally expensive simulators.

**Recent Publications:**

- P. Singh and A. Hellander (2018), Multi-objective optimization driven construction of uniform priors for likelihood-free parameter inference, Proceedings of the European Simulation Conference 2018 (to appear).
- P. Singh and A. Hellander (2018), Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits
- P. Singh and A. Hellander (2018), Hyperparameter Optimization for Approximate Bayesian Computation, Proceedings of the 2018 Winter Simulation Conference (to appear).
- P. Singh and A. Hellander (2017), ’Surrogate assisted model reduction for stochastic biochemical reaction networks’, I Proc. 49th Winter Simulation Conference, Piscataway, NJ: IEEE. 1773-1783