Continue with FacebookLooking to list your PhD opportunities? Log in here.
This project is no longer listed on FindAPhD.com and may not be available.
Click here to search FindAPhD.com for PhD studentship opportunities
Prof D Coca
Applications accepted all year round
Self-Funded PhD Students Only
About the Project
The ability to build genetic circuits with a reproducible response to external stimuli requires the development of both experimental techniques as well as rigorous theoretical design methodologies.
The genes and gene products involved in the response to a signal make up a genetic regulatory network. The development of robust, reliable and efficient novel gene regulatory circuits requires a mathematical description of this system. Until now, hundreds or even thousands of diverse GRN have been characterized experimentally, providing a wealth of data that can be used to infer empirical models of genetic circuits using model inference techniques akin to those employed in control engineering.
Changes in bacterial gene expression in response to a signal are often mediated by the products of regulator genes. Regulator genes encode signal-responsive proteins that act as activators or repressors of the expression level of effector genes. Therefore a gene regulatory network can be viewed as a complex assembly of interconnected regulatory units that can be analysed or manipulated using rigorous methodologies analogous to those in control engineering.
The aim of this project is to develop a novel framework for modelling, analysis and design of basic genetic circuits that is rooted in theoretical control theory. Such an approach promises to offer great insight into the underlying design principles of GRN, better understanding of cellular functions and in the engineering of novel gene regulatory circuits.
Funding Notes
This is a self-funded research project.
We require applicants to have either an undergraduate honours degree (1st) or MSc (Merit or Distinction) in a relevant science or engineering subject from a reputable institution. Prospective candidates for this project should have either an undergraduate honours degree (2:1) or MSc (Merit or Distinction) in Control Engineering, Applied Mathematics, Physics or other related disciplines.
Full details of how to apply can be found at the following link:
https://www.sheffield.ac.uk/acse/research-degrees/applyphd
Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive: https://www.sheffield.ac.uk/postgraduate/phd/scholarships
We require applicants to have either an undergraduate honours degree (1st) or MSc (Merit or Distinction) in a relevant science or engineering subject from a reputable institution. Prospective candidates for this project should have either an undergraduate honours degree (2:1) or MSc (Merit or Distinction) in Control Engineering, Applied Mathematics, Physics or other related disciplines.
Full details of how to apply can be found at the following link:
https://www.sheffield.ac.uk/acse/research-degrees/applyphd
Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive: https://www.sheffield.ac.uk/postgraduate/phd/scholarships
Search suggestions
Based on your current searches we recommend the following search filters.
Check out our other PhDs in Sheffield, United Kingdom
Check out our other PhDs in United Kingdom
Start a New search with our database of over 4,000 PhDs
PhD suggestions
Based on your current search criteria we thought you might be interested in these.
Using markers of gene evolutionary age to identify function within gene regulatory networks related to embryo implantation
The University of Manchester
Multi-scale machine learning approaches to composite materials modelling and design
The University of Manchester
Noise-induced transitions in gene regulatory networks
University of Surrey