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Rational strain design with minimal phenotype perturbation.

Published version
Peer-reviewed

Repository DOI


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Authors

Narayanan, Bharath 
Weilandt, Daniel 
Hatzimanikatis, Vassily  ORCID logo  https://orcid.org/0000-0001-6432-4694

Abstract

Devising genetic interventions for desired cellular phenotypes remains challenging regarding time and resources. Kinetic models can accelerate this task by simulating metabolic responses to genetic perturbations. However, exhaustive design evaluations with kinetic models are computationally impractical, especially when targeting multiple enzymes. Here, we introduce a framework for efficiently scouting the design space while respecting cellular physiological requirements. The framework employs mixed-integer linear programming and nonlinear simulations with large-scale nonlinear kinetic models to devise genetic interventions while accounting for the network effects of these perturbations. Importantly, it ensures the engineered strain's robustness by maintaining its phenotype close to that of the reference strain. The framework, applied to improve the anthranilate production in E. coli, devises designs for experimental implementation, including eight previously experimentally validated targets. We expect this framework to play a crucial role in future design-build-test-learn cycles, significantly expediting the strain design compared to exhaustive design enumeration.

Description

Acknowledgements: This work was supported by funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 814408 (B.N. and D.W.), the Swiss National Science Foundation Synergia grant CRSII5_198543 (M.M.), the Swedish Research Council Vetenskapsradet grant 2016-06160 (B.N.), and the Ecole Polytechnique Fédérale de Lausanne (EPFL).


Funder: École Polytechnique Fédérale de Lausanne (Swiss Federal Institute of Technology Lausanne); doi: https://doi.org/10.13039/501100001703


Funder: École Polytechnique Fédérale de Lausanne (Swiss Federal Institute of Technology Lausanne)

Keywords

Escherichia coli, Genetic Engineering, Kinetics, Learning, Phenotype

Journal Title

Nat Commun

Conference Name

Journal ISSN

2041-1723
2041-1723

Volume Title

15

Publisher

Springer Science and Business Media LLC
Sponsorship
EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) (814408, 814408)
Vetenskapsrådet (Swedish Research Council) (2016-06160)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) (CRSII5_198543)