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Gaussian process emulation of an individual-based model simulation of microbial communities

Lookup NU author(s): Dr Oluwole Oyebamiji, Professor Darren Wilkinson, Dr Jayathilake Pahala Gedara, Professor Thomas Curtis, Dr Bowen Li

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


Abstract

The ability to make credible simulations of open engineered biological systems is an important step towards theapplication of scientific knowledge to solve real-world problems in this challenging, complex engineering domain. Animportant application of this type of knowledge is in the design and management of wastewater treatment systems.One of the crucial aspects of an engineering biology approach to wastewater treatment study is the ability to run asimulation of complex biological communities. However, the simulation of open biological systems is challengingbecause they often involve a large number of bacteria that ranges from order 10^12 (a baby’s microbiome) to 10^18 (awastewater treatment plant) individual particles, and are physically complex. Since the models are computationallyexpensive, and due to computing constraints, the consideration of only a limited set of scenarios is often possible. Asimplified approach to this problem is to use a statistical approximation of the simulation ensembles derived from thecomplex models at a fine scale which will help in reducing the computational burden. Our aim in this paper is to build acheaper surrogate of an Individual-based (IB) model simulation of microbial communities. The paper focuses on howto use an emulator as an effective tool for studying and incorporating microscale processes in a computationally efficientway into macroscale models. The main issue we address is a strategy for emulating high-level summaries from the IBmodel simulation data. We use a Gaussian process regression model for the emulation. Under cross-validation, thepercentage of variance explained for the univariate emulator ranges from 83-99% and 87-99% for the multivariateemulators, and for both biofilms and floc. Our emulators show an approximately 220-fold increase in computationalefficiency. The sensitivity analyses indicated that substrate nutrient concentration for nitrate, carbon, nitrite and oxygenas well as the maximum growth rate for heterotrophic bacteria are the most important parameters for the predictions.We observe that the performance of the single step emulator depends hugely on the initial conditions and sample sizetaken for the normal approximation. We believe that the development of an emulator for an IB model is of strategicimportance for using microscale understanding to enable macroscale problem solving.The performance of credible simulations in open engineered biological frameworks is an important step for practical application of scientific knowledge to solve real-world problems and enhance our ability to make novel discoveries. Therefore, maximising our potential to explore the range of solutions and predict the behaviour of particles at frontier level is necessary to understand how the system functions and could reduce the potential risk of failure on a large scale. One primary application of this type of knowledge is in the management of wastewater treatment systems. Ecient optimisation of wastewater treatment plant focuses on aggregate outcomes of individual particle-level processes. One of the crucial aspects of an engineering biology approach in a wastewater treatment study is to run a complex simulation of biological particles. This type of model should scale from one level to another so that they can be used to study how to manage real systems e ectively with minimal physical experimentation.Nevertheless, simulation of open biological systems is challenging because they often involve a large number of bacteria that ranges from order 1012 to 1018 individual particles and are physically complex.Since the models are computationally expensive, and due to computing constraints, the consideration of only a limited set of scenarios is often possible. A simplified approach to this problem is to use a statistical approximation of the simulation ensembles derived from the complex models at a fine scale which will help in reducing the computational burden. Our aim in this paper is to build a cheaper surrogate of the Individual-based (IB) model simulation of microbial communities. The paper focuses on how to use an emulator as an e ective tool for studying and incorporating microscale processes in a computationally efficient way into macroscale models. The main issue we address is to highlight the strategy for emulating high-level summaries from the IB model simulation data. We use a Gaussian process regression model for the emulation.


Publication metadata

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

Publication type: Article

Publication status: Published

Journal: Journal of Computational Science

Year: 2017

Volume: 22

Pages: 69-84

Print publication date: 01/09/2017

Online publication date: 14/08/2017

Acceptance date: 08/08/2017

ISSN (print): 1877-7503

ISSN (electronic): 1877-7511

Publisher: Elsevier Ltd

URL: https://doi.org/10.1016/j.jocs.2017.08.006

DOI: 10.1016/j.jocs.2017.08.006

Data Source Location: http://dx.doi.org/10.17634/123172-1


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