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A multi-model Framework for the Arabidopsis life cycle

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    Rights statement: © The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Experimental Biology. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Original languageEnglish
Pages (from-to)2463–2477
Number of pages16
JournalJournal of Experimental Botany
Volume70
Issue number9
Early online date19 Feb 2019
DOIs
Publication statusPublished - 19 Feb 2019

Abstract

Linking our understanding of biological processes at different scales is a major conceptual challenge in biology, which is aggravated by differences in research methods. Modelling can be a useful approach to consolidating our understanding across traditional research domains. The laboratory model species Arabidopsis thaliana is very widely used to study plant growth processes and has also been tested more recently in eco-physiology and population genetics. However, approaches from crop modelling that might link these domains are rarely applied to Arabidopsis. Here, we combine plant growth models with phenology models from eco-physiology, using the agent-based modelling language Chromar. We introduce a simpler Framework Model of vegetative growth for Arabidopsis, FM-lite. By extending this model to include inflorescence and fruit growth and seed dormancy, we present a whole-life-cycle, multi-model FM-life, which allows us to simulate at the population level in various genotype x environment scenarios. Environmental effects on plant growth distinguish between the simulated life history strategies that were compatible with previously-described Arabidopsis phenology. Our results simulate reproductive success that is founded on the broad range of physiological processes familiar from crop models and suggest an approach to simulate evolution directly in future.

    Research areas

  • Systems biology, Arabidopsis thaliana, computational modelling, agent-based modelling, eco-physiology, life history, growth model, population ecology, Arabidopsis, Ecophysiology

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