Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems. (August 2018)

• Record Type: Journal Article
• Authors: Pienaar, Elsje
• Is Part Of: Biomedical engineering and computational biology. Volume 9(2018)
• Publication Date: 2018-08
• Subjects: Monte Carlo -- Markov chain -- stochastic models -- multifidelity -- rare events; Biomedical engineering -- Periodicals; Computational biology -- Periodicals; Biomedical Engineering; Computational Biology; Biomedical engineering; Computational biology; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; Periodicals; 610.2805
• DOI: 10.1177/1179597218790253
• Languages: English
• ISSNs: 1179-5972

Abstract

Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that uses medium-fidelity analysis (Monte Carlo simulations) and/or low-fidelity analysis (Markov chain models) to analyze high-fidelity stochastic model results. Medium-fidelity analysis can produce large numbers of possible rare event trajectories for a single high-fidelity model simulation. This allows prediction of both rare event dynamics and probability distributions at much lower frequencies than high-fidelity models. Low-fidelity analysis can calculate probability distributions for rare events over time for any frequency by updating the probabilities of the rare event state space after each discrete event of the high-fidelity model. To validate the approach, we apply multifidelity analysis to a high-fidelity model of tuberculosis disease. We validate the method against high-fidelity model results and illustrate the application of multifidelity analysis in predicting rare event trajectories, performing sensitivity analyses and extrapolating predictions to very low frequencies in complex systems. We believe that our approach will complement ongoing efforts to enable accurate prediction of rare event dynamics in high-fidelity computational models.