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(BBSRC DTP) Predictive Genotype-Phenotype-Fitness Mapping in E.coli


Project Description

Genotype-phenotype-fitness mapping, or the connection between genetic mutations and their effects on organismal fitness, lies at the heart of understanding and predicting evolution. Because of its central role in determining evolutionary outcomes, much experimental and theoretical effort has been put into describing how genotype maps onto fitness. And yet, in spite of extensive descriptions of genotype-phenotype-fitness mappings, we entirely lack the ability to predict that relationship. This, in turn, limits our ability to predict evolution – a task that is becoming increasingly important in the face of the impending antimicrobial resistance crisis and for establishing the engineering ground rules to best utilize synthetic biology for the benefit of mankind.

The goal of this project is to dramatically extend our ability to predict evolutionary outcomes, by developing a predictive genotype-phenotype-fitness map that would connect single point mutations to their effect on fitness of E.coli. The project will focus on mutations in gene regulatory elements (promoters), first determining how they map onto phenotype (gene expression levels), and subsequently onto organismal fitness.

The project will involve a combination of experimental and theoretical work. The core of the project will focus on developing a model that combines two existing frameworks: thermodynamic modelling of gene regulatory networks with flux-balance analysis of metabolic networks. Experimentally, the student will develop synthetic gene regulatory networks (using well-understood regulators LacI, TetR, and Lambda cI) in order to understand the fundamental structural and architectural features of promoters, which will form the foundation of the thermodynamic model. Secondly, the student will also introduce mutations into the existing promoters of metabolic genes, quantitatively measuring the effects of those mutations on fitness, and using that data to develop an accurate flux-balance analysis of the network.

Successfully combining these approaches will result in the first predictive, single nucleotide-resolution genotype-phenotype-fitness map. Such a map would dramatically improve our ability to predict evolution. On a fundamental level, this project will demonstrate how interdisciplinary approaches can help overcome long-standing, fundamental questions in biology. From an applied perspective, the last stage of the project will evaluate how such a map can be applied to improve synthetic biology and its applications. The ability to predict optimal metabolic network design on a single nucleotide level would help overcome the need for extensive experimental ‘tweaking’ of networks – a costly and laborious obstacle for synthetic biology.

Entry Requirements:
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

Funding Notes

This project is to be funded under the BBSRC Doctoral Training Partnership. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the BBSRC DTP website View Website

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

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