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Mol Syst Biol. 2018 Jan 15;14(1):e7803. doi: 10.15252/msb.20177803.

Assigning function to natural allelic variation via dynamic modeling of gene network induction.

Author information

1
Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France magali.richard@univ-grenoble-alpes.fr daniel.jost@univ-grenoble-alpes.fr gael.yvert@ens-lyon.fr.
2
Univ. Grenoble Alpes, CNRS CHU Grenoble Alpes Grenoble INP TIMC-IMAG, Grenoble, France.
3
Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France.
4
Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
5
Univ. Grenoble Alpes, CNRS CHU Grenoble Alpes Grenoble INP TIMC-IMAG, Grenoble, France magali.richard@univ-grenoble-alpes.fr daniel.jost@univ-grenoble-alpes.fr gael.yvert@ens-lyon.fr.

Abstract

More and more natural DNA variants are being linked to physiological traits. Yet, understanding what differences they make on molecular regulations remains challenging. Important properties of gene regulatory networks can be captured by computational models. If model parameters can be "personalized" according to the genotype, their variation may then reveal how DNA variants operate in the network. Here, we combined experiments and computations to visualize natural alleles of the yeast GAL3 gene in a space of model parameters describing the galactose response network. Alleles altering the activation of Gal3p by galactose were discriminated from those affecting its activity (production/degradation or efficiency of the activated protein). The approach allowed us to correctly predict that a non-synonymous SNP would change the binding affinity of Gal3p with the Gal80p transcriptional repressor. Our results illustrate how personalizing gene regulatory models can be used for the mechanistic interpretation of genetic variants.

KEYWORDS:

SNP function; galactose; personalized medicine; stochastic model; yeast

PMID:
29335276
PMCID:
PMC5787706
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