Optimal Evolutionary Control for Artificial Selection on Molecular Phenotypes

Controlling an evolving population is an important task in modern molecular genetics, including directed evolution for improving the activity of molecules and enzymes, in breeding experiments in animals and in plants, and in devising public health strategies to suppress evolving pathogens. An optima...

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Autores principales:	Armita Nourmohammad, Ceyhun Eksin
Formato:	article
Lenguaje:	EN
Publicado:	American Physical Society 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/8015c8ab11214365b2da802e27dc3382
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spelling	oai:doaj.org-article:8015c8ab11214365b2da802e27dc33822021-12-02T17:57:32ZOptimal Evolutionary Control for Artificial Selection on Molecular Phenotypes10.1103/PhysRevX.11.0110442160-3308https://doaj.org/article/8015c8ab11214365b2da802e27dc33822021-03-01T00:00:00Zhttp://doi.org/10.1103/PhysRevX.11.011044http://doi.org/10.1103/PhysRevX.11.011044https://doaj.org/toc/2160-3308Controlling an evolving population is an important task in modern molecular genetics, including directed evolution for improving the activity of molecules and enzymes, in breeding experiments in animals and in plants, and in devising public health strategies to suppress evolving pathogens. An optimal intervention to direct evolution should be designed by considering its impact over an entire stochastic evolutionary trajectory that follows. As a result, a seemingly suboptimal intervention at a given time can be globally optimal as it can open opportunities for desirable actions in the future. Here, we propose a feedback control formalism to devise globally optimal artificial selection protocol to direct the evolution of molecular phenotypes. We show that artificial selection should be designed to counter evolutionary trade-offs among multivariate phenotypes to avoid undesirable outcomes in one phenotype by imposing selection on another. Control by artificial selection is challenged by our ability to predict molecular evolution. We develop an information theoretical framework and show that molecular timescales for evolution under natural selection can inform how to monitor a population in order to acquire sufficient predictive information for an effective intervention with artificial selection. Our formalism opens a new avenue for devising artificial selection methods for directed evolution of molecular functions.Armita NourmohammadCeyhun EksinAmerican Physical SocietyarticlePhysicsQC1-999ENPhysical Review X, Vol 11, Iss 1, p 011044 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Armita Nourmohammad Ceyhun Eksin Optimal Evolutionary Control for Artificial Selection on Molecular Phenotypes
description	Controlling an evolving population is an important task in modern molecular genetics, including directed evolution for improving the activity of molecules and enzymes, in breeding experiments in animals and in plants, and in devising public health strategies to suppress evolving pathogens. An optimal intervention to direct evolution should be designed by considering its impact over an entire stochastic evolutionary trajectory that follows. As a result, a seemingly suboptimal intervention at a given time can be globally optimal as it can open opportunities for desirable actions in the future. Here, we propose a feedback control formalism to devise globally optimal artificial selection protocol to direct the evolution of molecular phenotypes. We show that artificial selection should be designed to counter evolutionary trade-offs among multivariate phenotypes to avoid undesirable outcomes in one phenotype by imposing selection on another. Control by artificial selection is challenged by our ability to predict molecular evolution. We develop an information theoretical framework and show that molecular timescales for evolution under natural selection can inform how to monitor a population in order to acquire sufficient predictive information for an effective intervention with artificial selection. Our formalism opens a new avenue for devising artificial selection methods for directed evolution of molecular functions.
format	article
author	Armita Nourmohammad Ceyhun Eksin
author_facet	Armita Nourmohammad Ceyhun Eksin
author_sort	Armita Nourmohammad
title	Optimal Evolutionary Control for Artificial Selection on Molecular Phenotypes
title_short	Optimal Evolutionary Control for Artificial Selection on Molecular Phenotypes
title_full	Optimal Evolutionary Control for Artificial Selection on Molecular Phenotypes
title_fullStr	Optimal Evolutionary Control for Artificial Selection on Molecular Phenotypes
title_full_unstemmed	Optimal Evolutionary Control for Artificial Selection on Molecular Phenotypes
title_sort	optimal evolutionary control for artificial selection on molecular phenotypes
publisher	American Physical Society
publishDate	2021
url	https://doaj.org/article/8015c8ab11214365b2da802e27dc3382
work_keys_str_mv	AT armitanourmohammad optimalevolutionarycontrolforartificialselectiononmolecularphenotypes AT ceyhuneksin optimalevolutionarycontrolforartificialselectiononmolecularphenotypes
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Optimal Evolutionary Control for Artificial Selection on Molecular Phenotypes

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