Edinburgh Research Explorer

Andrew Millar - Invited speaker

Argyris Zardilis - Contributor

Jose Urquiza Garcia - Contributor

Alastair Hume - Contributor

Robert Muetzelfeldt - Contributor

Gordon Plotkin - Contributor

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Forging a Causal Chain Around the Circadian Clock, from Genome Sequence to Field Traits.

Andrew J. Millar1, Argyris Zardilis2, Uriel Urquiza2, Alastair Hume3, Robert Muetzelfeldt4 and Gordon D. Plotkin5, (1)Max Born Crescent, University of Edinburgh, Edinburgh, Scotland, UNITED KINGDOM, (2)SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom, (3)EPCC and SynthSys, University of Edinburgh, Edinburgh, United Kingdom, (4)Simulistics Ltd., Edinburgh, United Kingdom, (5)School of Informatics, University of Edinburgh, Edinburgh, United Kingdom

Abstract Text:

The 24-hour circadian clock controls the sleep-wake cycle, the cell cycle and seasonal reproduction through photoperiodism. Breeders have selected alleles of clock-associated genes in multiple crops. We seek to understand how the clock gene circuit controls plant growth, biomass and life history in Arabidopsis thaliana. This work links genotype to phenotype, quantitatively and mechanistically. However, the proof of concept for a ‘crops in silico’ approach (Marshall-Colon et al. Front. Plant Sci. 2017) should ideally start from increasingly-available crop genome sequences and reach up to field traits, in a transparent and accessible fashion. I will outline our progress towards that goal in Arabidopsis.

We previously built mechanistic, mathematical models of the clock gene circuit, and of each clock-regulated process between germination and flowering, including the nightly utilisation of starch carbon stores. Most recently, we used multiple, biochemical data sets to link specific clock gene regulation to genome sequence and hence, potentially, to SNPs. We previously combined three further models to form the Arabidopsis Framework Model (FMv1; www.plasmo.ed.ac.uk/plasmo/models/model.shtml?accession=PLM_76), which predicts vegetative biomass quantitatively in simple, laboratory conditions (Chew et al. PNAS 2014). In the FMv2 (Chew et al. bioRxiv 2017, https://doi.org/10.1101/105437), we combined models of the various, clock-regulated processes in the whole-plant context from FMv1, to understand quantitatively the pleiotropic phenotypes of a ‘slow’ clock mutant, from the gene circuit dynamics to biomass. We have recently made a simpler model for vegetative growth, FM-lite, in a flexible, agent-based language Chromar (Honorato-Zimmer et al. ENTCS 2017). We then placed this model in the context of phenology models for seed dormancy and reproductive growth (Burghardt et al., Am. Nat. 2015), to simulate the whole Arabidopsis life cycle. Simulating this FM-life model for seed populations under various GxE scenarios allowed us to discriminate among previously-proposed life history strategies (Burghardt et al., Am. Nat. 2015). This suggests the potential to construct a causal chain from SNPs to eco-physiology, and to form hypotheses regarding yield and fitness advantages (Zardilis et al. bioRxiv 2018, https://doi.org/10.1101/358408).

To address the challenge of making such models accessible, I will contrast a "black-box" online simulator (http://turnip.bio.ed.ac.uk/fm/) with the open, refactoring approach we used for FMv1, in the Simile modelling environment. As such research projects must in fact share data, protocols and models, I outline the FAIRDOM data management system (www.fairdomhub.org), which now offers self-service data management to address exactly these needs. The model files for FMv2, for example, are publicly available at https://fairdomhub.org/models/248, as will the data in future.
This is an invited paper.

Forging a Causal Chain Around the Circadian Clock, from Genome Sequence to Field Traits.
Submitter's E-mail Address:
ASA Section: Climatology and Modeling
Symposium--the Modeling Road Less Travelled - Alternative Paradigms for Crop Modeling
Preferred Format:
5 Nov 2018

Event (Conference)

TitleASA, CSSA, and CSA International Annual Meeting
CountryUnited States
Degree of recognitionInternational event

ID: 74608805