research-article

Optimisation and landscape analysis of computational biology models: a case study

Authors:

Khulood Alyahya,

Ozgur E. Akman,

Jonathan E. FieldsendAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1644 - 1651

https://doi.org/10.1145/3067695.3084609

Published: 15 July 2017 Publication History

Abstract

The parameter explosion problem is a crucial bottleneck in modelling gene regulatory networks (GRNs), limiting the size of models that can be optimised to experimental data. By discretising state, but not time, Boolean delay equations (BDEs) provide a significant reduction in parameter numbers, whilst still providing dynamical complexity comparable to more biochemically detailed models, such as those based on differential equations. Here, we explore several approaches to optimising BDEs to timeseries data, using a simple circadian clock model as a case study. We compare the effectiveness of two optimisers on our problem: a genetic algorithm (GA) and an elite accumulative sampling (EAS) algorithm that provides robustness to data discretisation. Our results show that both methods are able to distinguish effectively between alternative architectures, yielding excellent fits to data. We also perform a landscape analysis, providing insights into the properties that determine optimiser performance (e.g. number of local optima and basin sizes). Our results provide a promising platform for the analysis of more complex GRNs, and suggest the possibility of leveraging cost landscapes to devise more efficient optimisation schemes.

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Cited By

Akman ODoherty KWareham B(2023)BDEtools: A MATLAB Package for Boolean Delay Equation ModelingJournal of Computational Biology10.1089/cmb.2021.065830:1(52-69)Online publication date: 1-Jan-2023
https://doi.org/10.1089/cmb.2021.0658
Berg MPlöntzke JSiebert HRöblitz S(2021)Modelling Oscillatory Patterns in the Bovine Estrous Cycle with Boolean Delay EquationsBulletin of Mathematical Biology10.1007/s11538-021-00942-z83:12Online publication date: 2-Nov-2021
https://doi.org/10.1007/s11538-021-00942-z
Alyahya KDoherty KAkman OFieldsend J(2021)Reduced Models of Gene Regulatory Networks: Visualising Multi-modal LandscapesMetaheuristics for Finding Multiple Solutions10.1007/978-3-030-79553-5_10(229-258)Online publication date: 23-Oct-2021
https://doi.org/10.1007/978-3-030-79553-5_10
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Optimisation and landscape analysis of computational biology models: a case study

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cover image ACM Conferences

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2017

1934 pages

ISBN:9781450349390

DOI:10.1145/3067695

Copyright © 2017 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 15 July 2017

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University of Exeter HPC Strategy
Engineering and Physical Sciences Research Council

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GECCO '17

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SIGEVO

GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Akman ODoherty KWareham B(2023)BDEtools: A MATLAB Package for Boolean Delay Equation ModelingJournal of Computational Biology10.1089/cmb.2021.065830:1(52-69)Online publication date: 1-Jan-2023
https://doi.org/10.1089/cmb.2021.0658
Berg MPlöntzke JSiebert HRöblitz S(2021)Modelling Oscillatory Patterns in the Bovine Estrous Cycle with Boolean Delay EquationsBulletin of Mathematical Biology10.1007/s11538-021-00942-z83:12Online publication date: 2-Nov-2021
https://doi.org/10.1007/s11538-021-00942-z
Alyahya KDoherty KAkman OFieldsend J(2021)Reduced Models of Gene Regulatory Networks: Visualising Multi-modal LandscapesMetaheuristics for Finding Multiple Solutions10.1007/978-3-030-79553-5_10(229-258)Online publication date: 23-Oct-2021
https://doi.org/10.1007/978-3-030-79553-5_10
Parreaux L(2020)The simple essence of algebraic subtyping: principal type inference with subtyping made easy (functional pearl)Proceedings of the ACM on Programming Languages10.1145/34090064:ICFP(1-28)Online publication date: 3-Aug-2020
https://dl.acm.org/doi/10.1145/3409006
Goli MDrechsler R(2020)PREASCACM Transactions on Design Automation of Electronic Systems10.1145/338814025:5(1-28)Online publication date: 5-Jul-2020
https://dl.acm.org/doi/10.1145/3388140
Avramidis ELalik MAkman O(2020)SODECLACM Transactions on Mathematical Software10.1145/338507646:3(1-21)Online publication date: 13-Jul-2020
https://dl.acm.org/doi/10.1145/3385076
Ezer DWhitaker K(2019)Data science for the scientific life cycleeLife10.7554/eLife.439798Online publication date: 6-Mar-2019
https://doi.org/10.7554/eLife.43979
Alyahya KAkman OFieldsend JAuger AStützle T(2019)Landscape analysis under measurement errorProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326858(1415-1418)Online publication date: 13-Jul-2019
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