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

Lookup NU author(s): Dr Oluwole Oyebamiji, Professor Darren Wilkinson, Dr Bowen Li, Dr Paolo Zuliani, Professor Thomas Curtis

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2018 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.


Publication metadata

Author(s): Oyebamiji OK, Wilkinson DJ, Li B, Jayathilake PG, Zuliani P, Curtis TP

Publication type: Article

Publication status: Published

Journal: Journal of Computational Science

Year: 2019

Volume: 30

Pages: 194-208

Print publication date: 01/01/2019

Online publication date: 19/12/2018

Acceptance date: 12/12/2018

Date deposited: 10/01/2019

ISSN (print): 1877-7503

Publisher: Elsevier B.V.

URL: https://doi.org/10.1016/j.jocs.2018.12.007

DOI: 10.1016/j.jocs.2018.12.007

Data Source Location: https://doi.org/10.17634/123172-4


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