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Application of Machine Learning Techniques to an Agent-Based Model of Pantoea

Lookup NU author(s): Dr Stephen McGough



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


© Copyright © 2021 Chen, Londoño-Larrea, McGough, Bible, Gunaratne, Araujo-Granda, Morrell-Falvey, Bhowmik and Fuentes-Cabrera.Agent-based modeling (ABM) is a powerful simulation technique which describes a complex dynamic system based on its interacting constituent entities. While the flexibility of ABM enables broad application, the complexity of real-world models demands intensive computing resources and computational time; however, a metamodel may be constructed to gain insight at less computational expense. Here, we developed a model in NetLogo to describe the growth of a microbial population consisting of Pantoea. We applied 13 parameters that defined the model and actively changed seven of the parameters to modulate the evolution of the population curve in response to these changes. We efficiently performed more than 3,000 simulations using a Python wrapper, NL4Py. Upon evaluation of the correlation between the active parameters and outputs by random forest regression, we found that the parameters which define the depth of medium and glucose concentration affect the population curves significantly. Subsequently, we constructed a metamodel, a dense neural network, to predict the simulation outputs from the active parameters and found that it achieves high prediction accuracy, reaching an R2 coefficient of determination value up to 0.92. Our approach of using a combination of ABM with random forest regression and neural network reduces the number of required ABM simulations. The simplified and refined metamodels may provide insights into the complex dynamic system before their transition to more sophisticated models that run on high-performance computing systems. The ultimate goal is to build a bridge between simulation and experiment, allowing model validation by comparing the simulated data to experimental data in microbiology.

Publication metadata

Author(s): Chen SH, Londono-Larrea P, McGough AS, Bible AN, Gunaratne C, Araujo-Granda PA, Morrell-Falvey JL, Bhowmik D, Fuentes-Cabrera M

Publication type: Article

Publication status: Published

Journal: Frontiers in Microbiology

Year: 2021

Volume: 12

Print publication date: 01/09/2021

Online publication date: 24/09/2021

Acceptance date: 20/08/2021

Date deposited: 29/10/2021

ISSN (electronic): 1664-302X

Publisher: Frontiers Research Foundation


DOI: 10.3389/fmicb.2021.726409


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