Home > Research > Publications & Outputs > Bayesian emulation and calibration of an indivi...

Links

Text available via DOI:

View graph of relations

Bayesian emulation and calibration of an individual-based model of microbial communities

Research output: Contribution to journalJournal article

Published
Close
<mark>Journal publication date</mark>01/2019
<mark>Journal</mark>Journal of Computational Science
Volume30
Number of pages15
Pages (from-to)194-208
Publication statusPublished
Early online date19/12/18
Original languageEnglish

Abstract

Individual-based (IB) modelling has been widely used for studying the emergence of complex interactions of bacterial biofilms and their environment. We describe the emulation and calibration of an expensive dynamic simulator of an IB model of microbial communities. We used a combination of multivariate dynamic linear models (DLM) and a Gaussian process to estimate the model parameters of our dynamic emulators. The emulators incorporate a smoothly varying and nonstationary trend that is modelled as a deterministic function of explanatory variables while the Gaussian process (GP) is allowed to capture the remaining intrinsic local variations. We applied this emulation strategy for parameter calibration of a newly developed model for simulation of microbial communities against the iDynoMiCS model. The percentage of variance explained for the four outputs biomass concentration, the total number of particles, biofilm average height and surface roughness range between 84—92% and 97–99% for univariate and multivariate emulators respectively. The simulation-based sensitivity analysis identified carbon substrate, oxygen concentration and maximum specific growth rate for heterotrophic bacteria as the most critical variables for predictions. The calibration results also indicated a general reduction of uncertainty levels in most of the parameters. The study has helped us identify the tradeoff in using different types of models for microbial simulation. The approach illustrated here provides a tractable and computationally efficient technique for calibrating the parameters of an expensive computer model.