Understanding the evolution of gene regulatory networks through biophysical modelling and machine learning
About the Project
The ability to coordinate the expression of genes within a cell is at the heart of life. When and how much of a specific gene is turned into a protein is essential for organisms to respond to their environment and to manage resources. The need to carefully regulate genes has resulted in the evolution of gene regulatory networks – complex networks of interactions, where the protein product of one gene controls the expression of one or more other genes.
While the structures of numerous gene regulatory networks have been studied intensively, we know very little about how those networks came to be. In particular, we do not know how the basic building blocks of gene regulatory networks determine network structures.
In this project we will study this key question in evolutionary biology. You (together with your supervisors) will explore how the building blocks of gene regulatory networks in bacteria impact network structure. Bacterial networks consist of two components: short stretches of DNA situated upstream of the gene (called promoters) and proteins that bind to those promoters (transcription factors). The binding of transcription factors to a promoter is integrated into a signal, which determines the expression levels of the controlled gene. The basic mechanism of gene regulation is, therefore, the binding of transcription factor(s) to a promoter. The first goal of the project is to model these basic mechanisms using statistical thermodynamics in order to predict how mutations change TF-promoter interactions and, hence, how regulation evolves.
Once we can model how mutations affect the regulation at individual promoters, you will explore how the structure of entire networks impacts their evolution. In other words, does the number of promoters, the number of TFs binding to each promoter and the regulatory relationship between promoter units impact how they evolve and change under mutational pressure.
This interdisciplinary project will rely on machine learning and theoretical evolutionary modelling, with the option of backing up the theoretical findings with experiments on bacterial gene regulatory networks. Ultimately, understanding how network structure shapes network evolution will allow us to better predict the evolution of gene regulatory networks, and to study important processes like the emergence of multiple drug resistance in pathogenic bacteria and horizontal gene transfer.
Training/techniques to be provided
This project will provide multidisciplinary training in evolutionary and systems biology, biophysics and machine learning, with the goal of using machine learning and computational modelling to understand essential aspects of organismal evolution. The project offers an opportunity to also engage in experimental lab work, depending on the interests of the student and how the project develops. The student will gain a broad skillset highly relevant to industry/academia and will be integrated into MERMan, the UK’s largest cluster of microbial evolution research groups, providing a supportive and stimulating research environment.
Entry requirements
Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a related subject area.
We are looking for a student with genuine interest in interdisciplinary work. The successful candidate can either be a student from physics, mathematics or computer science with a genuine interest in evolutionary biology, or have a biology background with profound interest and experience in theoretical work. Strong programming skills are required, ideally in Python, C++ or Fortran. Excellent written and oral communication skills are essential.
Before you Apply
Applicants must make direct contact with preferred supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.
How To Apply
For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor. On the online application form select PhD Genetics.
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 https://www.bmh.manchester.ac.uk/study/research/programmes/integrated-teaching/
Your application form must be accompanied by a number of supporting documents by the advertised deadlines. Without all the required documents submitted at the time of application, your application will not be processed and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.
If you have any queries regarding making an application please contact our admissions team FBMH.doctoralacademy.admissions@manchester.ac.uk.
Equality, Diversity and Inclusion
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/
Funding Notes
References
Lagator, M., Sarikas, S., Steinrück, M., Toledo-Aparicio, D., Bollback, J. P., Tkacik, G., & Guet, C. C. (2022). Predicting bacterial promoter function and evolution from random sequences. eLife 11
Acar Kirit, H, JP Bollback, and M Lagator. "The Role of the Environment in Horizontal Gene Transfer." Molecular Biology and Evolution 39.11 (2022): msac220.
Nuha BinTayyash, Sokratia Georgaka, S T John, Sumon Ahmed, Alexis Boukouvalas, James Hensman, Magnus Rattray, Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments, Bioinformatics, 37, 21, (2021), 3788–3795
Forbes Beadle L, Love JC, Shapovalova Y, Artemev A, Rattray M, Ashe HL (2023) Combined modelling of mRNA decay dynamics and single-molecule imaging in the Drosophila embryo uncovers a role for P-bodies in 5′ to 3′ degradation. PLoS Biol 21(1): e3001956
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