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Using single-cell models to predict the functionality of synthetic circuits at the population scale

View ORCID ProfileChetan Aditya, View ORCID ProfileFrançois Bertaux, View ORCID ProfileGregory Batt, View ORCID ProfileJakob Ruess
doi: https://doi.org/10.1101/2021.08.03.454887
Chetan Aditya
1Inria Paris, 2 rue Simone Iff, 75012 Paris, France
2Institut Pasteur, 28 rue du Docteur Roux, 75015 Paris, France
3Université de Paris, 85 Boulevard Saint-Germain, 75006 Paris, France
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  • ORCID record for Chetan Aditya
François Bertaux
1Inria Paris, 2 rue Simone Iff, 75012 Paris, France
2Institut Pasteur, 28 rue du Docteur Roux, 75015 Paris, France
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Gregory Batt
1Inria Paris, 2 rue Simone Iff, 75012 Paris, France
2Institut Pasteur, 28 rue du Docteur Roux, 75015 Paris, France
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Jakob Ruess
1Inria Paris, 2 rue Simone Iff, 75012 Paris, France
2Institut Pasteur, 28 rue du Docteur Roux, 75015 Paris, France
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  • For correspondence: jakob.ruess@inria.fr
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Abstract

Mathematical modeling has become a major tool to guide the characterization and synthetic construction of cellular processes. However, models typically lose their capacity to explain or predict experimental outcomes as soon as any, even minor, modification of the studied system or its operating conditions is implemented. This limits our capacity to fully comprehend the functioning of natural biological processes and is a major roadblock for the de novo design of complex synthetic circuits. Here, using a specifically constructed yeast optogenetic differentiation system as an example, we show that a simple deterministic model can explain system dynamics in given conditions but loses validity when modifications to the system are made. On the other hand, deploying theory from stochastic chemical kinetics and developing models of the system’s components that simultaneously track single-cell and population processes allows us to quantitatively predict emerging dynamics of the system without any adjustment of model parameters. We conclude that carefully characterizing the dynamics of cell-to-cell variability using appropriate modeling theory may allow one to unravel the complex interplay of stochastic single-cell and population processes and to predict the functionality of composed synthetic circuits in growing populations before the circuit is constructed.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 04, 2021.
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Using single-cell models to predict the functionality of synthetic circuits at the population scale
Chetan Aditya, François Bertaux, Gregory Batt, Jakob Ruess
bioRxiv 2021.08.03.454887; doi: https://doi.org/10.1101/2021.08.03.454887
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Using single-cell models to predict the functionality of synthetic circuits at the population scale
Chetan Aditya, François Bertaux, Gregory Batt, Jakob Ruess
bioRxiv 2021.08.03.454887; doi: https://doi.org/10.1101/2021.08.03.454887

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