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Data-driven dynamical modelling of a pathogen-infected plant gene regulatory network : a comparative analysis
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Foo, Mathias, Dony, Leander and He, Fei (2022) Data-driven dynamical modelling of a pathogen-infected plant gene regulatory network : a comparative analysis. Biosystems, 219 . 104732. doi:10.1016/j.biosystems.2022.104732 ISSN 0303-2647.
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WRAP-data-driven-dynamical-modelling-pathogen-infected-plant-gene-regulatory-network-comparative-analysis-Foo-2022.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (2093Kb) | Preview |
Official URL: https://doi.org/10.1016/j.biosystems.2022.104732
Abstract
Recent advances in synthetic biology have enabled the design of genetic feedback control circuits that could be implemented to build resilient plants against pathogen attacks. To facilitate the proper design of these genetic feedback control circuits, an accurate model that is able to capture the vital dynamical behaviour of the pathogen-infected plant is required. In this study, using a data-driven modelling approach, we develop and compare four dynamical models (i.e. linear, Michaelis-Menten with Hill coefficient (Hill Function), standard S-System and extended S-System) of a pathogen-infected plant gene regulatory network (GRN). These models are then assessed across several criteria, i.e. ease of identifying the type of gene regulation, the predictive capability, Akaike Information Criterion (AIC) and the robustness to parameter uncertainty to determine its viability of balancing between biological complexity and accuracy when modelling the pathogen-infected plant GRN. Using our defined ranking score, we obtain the following insights to the modelling of GRN. Our analyses show that despite commonly used and provide biological relevance, the Hill Function model ranks the lowest while the extended S-System model ranks highest in the overall comparison. Interestingly, the performance of the linear model is more consistent throughout the comparison, making it the preferred model for this pathogen-infected plant GRN when considering data-driven modelling approach.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QH Natural history S Agriculture > SB Plant culture T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
Library of Congress Subject Headings (LCSH): | Gene regulatory networks, Gene regulatory networks -- Mathematical models, Synthetic biology -- Data processing, Plant-pathogen relationships -- Simulation methods , Computational biology | |||||||||
Journal or Publication Title: | Biosystems | |||||||||
Publisher: | Elsevier Science Ltd. | |||||||||
ISSN: | 0303-2647 | |||||||||
Official Date: | September 2022 | |||||||||
Dates: |
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Volume: | 219 | |||||||||
Article Number: | 104732 | |||||||||
DOI: | 10.1016/j.biosystems.2022.104732 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 4 July 2022 | |||||||||
Date of first compliant Open Access: | 1 July 2023 | |||||||||
RIOXX Funder/Project Grant: |
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