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Modelling the fitness landscapes of a SCRaMbLEd yeast genome

Lookup NU author(s): Dr Goksel Misirli, Professor Anil Wipat, Dr Jennifer Hallinan


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© 2022 Elsevier B.V. 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.

Publication metadata

Author(s): Yang B, Misirli G, Wipat A, Hallinan J

Publication type: Article

Publication status: Published

Journal: BioSystems

Year: 2022

Volume: 219

Print publication date: 01/09/2022

Online publication date: 27/06/2022

Acceptance date: 13/06/2022

ISSN (print): 0303-2647

ISSN (electronic): 1872-8324

Publisher: Elsevier Ireland Ltd


DOI: 10.1016/j.biosystems.2022.104730

PubMed id: 35772570


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