About the Project
The goal of this project is to dramatically extend our ability to predict evolutionary outcomes, by developing the ability to, for the first time, predict the effects of single point mutations 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.
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.
Training/techniques to be provided:
Systems biology, bioinformatics and biophysics: analysis of large-scale datasets; biophysical modelling of gene regulatory networks; flux-balance analysis; gene regulatory network modelling; metabolic network modelling. Molecular and synthetic biology: cloning; recombineering; construction of synthetic gene regulatory networks. Microbiology: bacterial culture growth; competition and fitness assays; flow cytometry and fluorescence-activated cell sorting; single cell analyses (optional) Evolution: genotype-phenotype-fitness mapping; experimental evolution in microbes
Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a related area / subject. MA or experience in a related discipline (e.g. formal sciences, biophysics, physics, systems engineering, systems biology, bioinformatics, computer science) is highly desirable. An ideal candidate would have experience in theoretical evolutionary biology and/or population genetics, or willingness to learn. Candidates with interest in evolutionary biology and microbiology are highly encouraged to apply. Experience in experimental biology, microbiology and/or molecular biology is desirable.
For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit http://www.internationalphd.manchester.ac.uk
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.
M Lagator, S Sarikas, H Acar, JP Bollback, CC Guet. Regulatory network structure determines patterns of intermolecular epistasis. eLife 6, e28921
C Igler, M Lagator, G Tkacik, JP Bollback, CC Guet. Evolutionary potential of transcription factors for gene regulatory rewiring. Nature Ecology and Evolution 2, 1633–1643
Herrmann HA, Dyson BC, Vass L, Johnson GN, Schwartz JM. Flux sampling is a powerful tool to study metabolism under changing environmental conditions. npj Systems Biology and Applications 5: 32 (2019).
Schwartz JM, Otokuni H, Akutsu T, Nacher JC. Probabilistic controllability approach to metabolic fluxes in normal and cancer tissues. Nature Communications 10: 2725 (2019).
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