Predictive Genotype-Phenotype-Fitness mapping.
Lead Research Organisation:
University of Manchester
Department Name: School of Biological Sciences
Abstract
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.
This project fits 'Theme 1: Advancing the frontiers of bioscience discovery', and in particular 'Understanding the rules of life', as it is a curiosity-driven, interdisciplinary project addressing a critical question in biology, which is 'how can we predict the effects of mutations' The project also necessarily queries the rationale behind the optimal location of mutations. It is also related to 'Transformative technologies' priority area, as it combines physical and life sciences, with a clear potential to benefit the industrial synthetic biotechnology sector by providing a cheap, computational solution to one of the most labour-intensive challenges synthetic biologists face - namely, maximizing the desired output through coordination of metabolic and expression network components.
The project also fits two sub-themes of 'Theme 2: Tackling strategic challenges', as synthetic biology applications have been proposed to revolutionise both, 'sustainable food' and 'energy production'.
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.
This project fits 'Theme 1: Advancing the frontiers of bioscience discovery', and in particular 'Understanding the rules of life', as it is a curiosity-driven, interdisciplinary project addressing a critical question in biology, which is 'how can we predict the effects of mutations' The project also necessarily queries the rationale behind the optimal location of mutations. It is also related to 'Transformative technologies' priority area, as it combines physical and life sciences, with a clear potential to benefit the industrial synthetic biotechnology sector by providing a cheap, computational solution to one of the most labour-intensive challenges synthetic biologists face - namely, maximizing the desired output through coordination of metabolic and expression network components.
The project also fits two sub-themes of 'Theme 2: Tackling strategic challenges', as synthetic biology applications have been proposed to revolutionise both, 'sustainable food' and 'energy production'.
Organisations
People |
ORCID iD |
Mato Lagator (Primary Supervisor) | |
Anthi-Maria Kouvatsou (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
BB/T008725/1 | 01/10/2020 | 30/09/2028 | |||
2449211 | Studentship | BB/T008725/1 | 01/10/2020 | 30/09/2024 | Anthi-Maria Kouvatsou |