Testing the inferred transcription rates of a dynamic, gene network model in absolute units


The circadian clock coordinates plant physiology and development. Mathematical clock models have provided a rigorous framework to understand how the observed rhythms emerge from disparate, molecular processes. However, models of the plant clock have largely been built and tested against RNA timeseries data in arbitrary, relative units. This limits model transferability, refinement from biochemical data and applications in synthetic biology. Here, we incorporate absolute mass units into a detailed, gene circuit model of the clock in Arabidopsis thaliana. We re-interpret the established P2011 model, highlighting a transcriptional activator that overlaps the function of REVEILLE 8/LHY-CCA1-LIKE 5, and refactor dynamic equations for the Evening Complex. The U2020 model incorporates the repressive regulation of PRR genes, a key feature of the most detailed clock model F2014, without greatly increasing model complexity. We tested the experimental error distributions of qRT-PCR data calibrated for units of RNA transcripts/cell and of circadian period estimates, in order to link the models to data more appropriately. U2019 and U2020 models were constrained using these data types, recreating previously-described circadian behaviours with RNA metabolic processes in absolute units. To test their inferred rates, we estimated a distribution of observed, transcriptome-wide transcription rates (Plant Empirical Transcription Rates, PETR) in units of transcripts/cell/hour. The PETR distribution and the equivalent degradation rates indicated that the models’ predicted rates are biologically plausible, with individual exceptions. In addition to updated, explanatory models of the plant clock, this validation process represents an advance in biochemical realism for models of plant gene regulation.

SEEK ID: https://fairdomhub.org/publications/599

DOI: 10.1101/2021.03.18.436071

Projects: Millar group

Publication type: Tech report

Citation: biorxiv;2021.03.18.436071v1,[Preprint]

Date Published: 20th Mar 2021

Registered Mode: by DOI

help Submitter
Urquiza-García, U., & Millar, A. J. (2021). Testing the inferred transcription rates of a dynamic, gene network model in absolute units. In []. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2021.03.18.436071

Views: 1208

Created: 21st Mar 2021 at 22:04

Last updated: 8th Dec 2022 at 17:26

help Attributions


Powered by
Copyright © 2008 - 2023 The University of Manchester and HITS gGmbH