Trajectory-matching ABC-MCMC for simulating heterogeneous dynamics in mechanistic models

Abstract

The inherent heterogeneity of complex biological systems makes it difficult to experimentally and clinically explore individual outcomes. Mechanistic mathematical models are essential tools for studying such heterogeneity. Thus, there is increasing interest in integrating newer mechanistic model-based techniques, like virtual patient cohorts and virtual clinical trials, within experimental and regulatory pipelines to probe relationships that may be difficult or impossible to ascertain through traditional wet-lab or clinical experimentation alone. Approximate Bayesian Computation (ABC) is an attractive method for generating virtual patient cohorts and running virtual clinical trials. However, ABC-based approaches can become computationally demanding when stringent acceptance criteria are imposed. In response, we developed a model-based technique called trajectory-matching random-walk feasibility sampling (TM-RWFS) that captures the variability of complex biological systems by constraining model trajectories between the upper and lower bounds of available data to generate heterogeneity. By testing the method's performance on existing mechanistic models, we show that TM-RWS generates parameter sets whose simulated trajectories reproduce the observed variability in biological systems of varying complexity while maintaining computational efficiency. Thus, TM-RWFS is a fast, new approach for generating heterogeneity in mechanistic mathematical models with implications for model-based experimental design, virtual patient cohorts, and virtual clinical trials.