A Parallel Genetic Algorithm for Optimizing Multicellular Models Applied to Biofilm Wrinkling
Multiscale computational models integrating sub-cellular, cellular, and multicellular levels can be powerful tools that help researchers replicate, understand, and predict multicellular biological phenomena. To leverage their potential, these models need correct parameter values, which specify cellu...
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ndltd-UTAHS-oai-digitalcommons.usu.edu-etd-64962019-10-13T05:59:09Z A Parallel Genetic Algorithm for Optimizing Multicellular Models Applied to Biofilm Wrinkling Johnson, Christopher Douglas Multiscale computational models integrating sub-cellular, cellular, and multicellular levels can be powerful tools that help researchers replicate, understand, and predict multicellular biological phenomena. To leverage their potential, these models need correct parameter values, which specify cellular physiology and affect multicellular outcomes. This work presents a robust parameter optimization method, utilizing a parallel and distributed genetic-algorithm software package. A genetic algorithm was chosen because of its superiority in fitting complex functions for which mathematical techniques are less suited. Searching for optimal parameters proceeds by comparing the multicellular behavior of a simulated system to that of a real biological system on the basis of features extracted from each which capture high-level, emergent multicellular outcomes. The goal is to find the set of parameters which minimizes discrepancy between the two sets of features. The method is first validated by demonstrating its effectiveness on synthetic data, then it is applied to calibrating a simple mechanical model of biofilm wrinkling, a common type of morphology observed in biofilms. Spatiotemporal convergence of cellular movement derived from experimental observations of different strains of Bacillus subtilis colonies is used as the basis of comparison. 2017-05-01T07:00:00Z text application/pdf https://digitalcommons.usu.edu/etd/5442 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=6496&context=etd Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact digitalcommons@usu.edu. All Graduate Theses and Dissertations DigitalCommons@USU Computer Sciences Physical Sciences and Mathematics |
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Computer Sciences Physical Sciences and Mathematics |
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Computer Sciences Physical Sciences and Mathematics Johnson, Christopher Douglas A Parallel Genetic Algorithm for Optimizing Multicellular Models Applied to Biofilm Wrinkling |
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Multiscale computational models integrating sub-cellular, cellular, and multicellular levels can be powerful tools that help researchers replicate, understand, and predict multicellular biological phenomena. To leverage their potential, these models need correct parameter values, which specify cellular physiology and affect multicellular outcomes. This work presents a robust parameter optimization method, utilizing a parallel and distributed genetic-algorithm software package. A genetic algorithm was chosen because of its superiority in fitting complex functions for which mathematical techniques are less suited. Searching for optimal parameters proceeds by comparing the multicellular behavior of a simulated system to that of a real biological system on the basis of features extracted from each which capture high-level, emergent multicellular outcomes. The goal is to find the set of parameters which minimizes discrepancy between the two sets of features. The method is first validated by demonstrating its effectiveness on synthetic data, then it is applied to calibrating a simple mechanical model of biofilm wrinkling, a common type of morphology observed in biofilms. Spatiotemporal convergence of cellular movement derived from experimental observations of different strains of Bacillus subtilis colonies is used as the basis of comparison. |
author |
Johnson, Christopher Douglas |
author_facet |
Johnson, Christopher Douglas |
author_sort |
Johnson, Christopher Douglas |
title |
A Parallel Genetic Algorithm for Optimizing Multicellular Models Applied to Biofilm Wrinkling |
title_short |
A Parallel Genetic Algorithm for Optimizing Multicellular Models Applied to Biofilm Wrinkling |
title_full |
A Parallel Genetic Algorithm for Optimizing Multicellular Models Applied to Biofilm Wrinkling |
title_fullStr |
A Parallel Genetic Algorithm for Optimizing Multicellular Models Applied to Biofilm Wrinkling |
title_full_unstemmed |
A Parallel Genetic Algorithm for Optimizing Multicellular Models Applied to Biofilm Wrinkling |
title_sort |
parallel genetic algorithm for optimizing multicellular models applied to biofilm wrinkling |
publisher |
DigitalCommons@USU |
publishDate |
2017 |
url |
https://digitalcommons.usu.edu/etd/5442 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=6496&context=etd |
work_keys_str_mv |
AT johnsonchristopherdouglas aparallelgeneticalgorithmforoptimizingmulticellularmodelsappliedtobiofilmwrinkling AT johnsonchristopherdouglas parallelgeneticalgorithmforoptimizingmulticellularmodelsappliedtobiofilmwrinkling |
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1719267374285193216 |