Application of Machine Learning Techniques to an Agent-Based Model of Pantoea
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 ti...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-09-01
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| Series: | Frontiers in Microbiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2021.726409/full |
| _version_ | 1819135150977449984 |
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| author | Serena H. Chen Pablo Londoño-Larrea Andrew Stephen McGough Amber N. Bible Chathika Gunaratne Pablo A. Araujo-Granda Jennifer L. Morrell-Falvey Debsindhu Bhowmik Miguel Fuentes-Cabrera |
| author_facet | Serena H. Chen Pablo Londoño-Larrea Andrew Stephen McGough Amber N. Bible Chathika Gunaratne Pablo A. Araujo-Granda Jennifer L. Morrell-Falvey Debsindhu Bhowmik Miguel Fuentes-Cabrera |
| author_sort | Serena H. Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T10:14:31Z |
| format | Article |
| id | doaj.art-2273a916e7f545bdb940e9b11b80a7cc |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-12-22T10:14:31Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-2273a916e7f545bdb940e9b11b80a7cc2022-12-21T18:29:45ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2021-09-011210.3389/fmicb.2021.726409726409Application of Machine Learning Techniques to an Agent-Based Model of PantoeaSerena H. Chen0Pablo Londoño-Larrea1Andrew Stephen McGough2Amber N. Bible3Chathika Gunaratne4Pablo A. Araujo-Granda5Jennifer L. Morrell-Falvey6Debsindhu Bhowmik7Miguel Fuentes-Cabrera8Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesChemical Engineering Faculty, Universidad Central del Ecuador, Quito, EcuadorSchool of Computing, Newcastle University, Newcastle upon Tyne, United KingdomBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesComputer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesChemical Engineering Faculty, Universidad Central del Ecuador, Quito, EcuadorBiosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesCenter for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesAgent-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.https://www.frontiersin.org/articles/10.3389/fmicb.2021.726409/fullagent-based modelmachine learningrandom forest regressionneural networkPantoea |
| spellingShingle | Serena H. Chen Pablo Londoño-Larrea Andrew Stephen McGough Amber N. Bible Chathika Gunaratne Pablo A. Araujo-Granda Jennifer L. Morrell-Falvey Debsindhu Bhowmik Miguel Fuentes-Cabrera Application of Machine Learning Techniques to an Agent-Based Model of Pantoea Frontiers in Microbiology agent-based model machine learning random forest regression neural network Pantoea |
| title | Application of Machine Learning Techniques to an Agent-Based Model of Pantoea |
| title_full | Application of Machine Learning Techniques to an Agent-Based Model of Pantoea |
| title_fullStr | Application of Machine Learning Techniques to an Agent-Based Model of Pantoea |
| title_full_unstemmed | Application of Machine Learning Techniques to an Agent-Based Model of Pantoea |
| title_short | Application of Machine Learning Techniques to an Agent-Based Model of Pantoea |
| title_sort | application of machine learning techniques to an agent based model of pantoea |
| topic | agent-based model machine learning random forest regression neural network Pantoea |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2021.726409/full |
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