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Modelling the fitness landscapes of a SCRaMbLEd yeast genome.

Authors
  • Yang, Bill1
  • Misirli, Goksel2
  • Wipat, Anil1
  • Hallinan, Jennifer3
  • 1 ICOS School of Computing Newcastle University 1, Urban Sciences Building Science Square, Newcastle Upon Tyne, UK.
  • 2 School of Computing and Mathematics Keele University, UK.
  • 3 BioThink Brisbane, Australia. Electronic address: [email protected] , (Australia)
Type
Published Article
Journal
Bio Systems
Publication Date
Jun 27, 2022
Volume
219
Pages
104730–104730
Identifiers
DOI: 10.1016/j.biosystems.2022.104730
PMID: 35772570
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The use of microorganisms for the production of industrially important compounds and enzymes is becoming increasingly important. Eukaryotes have been less widely used than prokaryotes in biotechnology, because of the complexity of their genomic structure and biology. The Yeast2.0 project is an international effort to engineer the yeast Saccharomyces cerevisiae to make it easy to manipulate, and to generate random variants using a system called SCRaMbLE. SCRaMbLE relies on artificial evolution in vitro to identify useful variants, an approach which is time consuming and expensive. We developed an in silico simulator for the SCRaMbLE system, using an evolutionary computing approach, which can be used to investigate and optimize the fitness landscape of the system. We applied the system to the investigation of the fitness landscape of one of the S. saccharomyces chromosomes, and found that our results fitted well with those previously published. We then simulated directed evolution with or without manipulation of SCRaMbLE, and revealed that controlling the SCRaMbLE process could effectively impact directed evolution. Our simulator can be applied to the analysis of the fitness landscapes of any organism for which SCRaMbLE has been implemented. Copyright © 2022 Elsevier B.V. All rights reserved.

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