or
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 Ramon Grima
Dr F Menolascina
No more applications being accepted
Competition Funded PhD Project (Students Worldwide)
About the Project
Controlling gene regulatory networks can be a fundamental step for their use in the synthesis of therapeutic strategies as, for example, to correct the severe phase lag observed in the circadian rhythms of some patients. However the stochastic nature of gene expression [1] makes the effective implementation of feedback control strategies a non-trivial problem. Computer-guided control of gene expression to achieve a desired level of mean protein numbers has only recently been demonstrated in the lab [2,3]. In this project the student will combine ideas from statistical physics and control theory to design a new controller that can simultaneously control both the mean number of proteins and the size of the fluctuations about this mean. Specifically the student will use recently developed accurate approximation methods [4] to implement stochastic model predictive control of gene expression in eukaryotic cells and to test this strategy using stochastic simulations. In parallel, the student will also explore the use of artificial neural networks to achieve the same aim. The simulated controller will then be tested in the laboratory; successive rounds of modelling and experiment will be used to build a fine-tuned efficient controller.
5. Further Information
This project is ideal for a student with a Bachelors or Masters in Applied Mathematics, Physics, Computer Science, Engineering or a closely related field. Previous familiarity with mathematical modelling in biology is useful but not a necessity. The student will be given extensive training in stochastic modelling, control theory and machine learning in the context of biology in their first year to ensure a solid foundation. The student will be part of the group of Dr. Ramon Grima http://grimagroup.bio.ed.ac.uk/index.html, which is located in the Centre for Synthetic and Systems Biology (SynthSys) at the University of Edinburgh. The experiments will be conducted in the lab of Dr. Filippo Menolascina.
5. Further Information
This project is ideal for a student with a Bachelors or Masters in Applied Mathematics, Physics, Computer Science, Engineering or a closely related field. Previous familiarity with mathematical modelling in biology is useful but not a necessity. The student will be given extensive training in stochastic modelling, control theory and machine learning in the context of biology in their first year to ensure a solid foundation. The student will be part of the group of Dr. Ramon Grima http://grimagroup.bio.ed.ac.uk/index.html, which is located in the Centre for Synthetic and Systems Biology (SynthSys) at the University of Edinburgh. The experiments will be conducted in the lab of Dr. Filippo Menolascina.
Funding Notes
The “Apply online” button on this page will take you to our Online Application checklist. Please complete each step and download the checklist which will provide a list of funding options and guide you through the application process.
If you would like us to consider you for one of our scholarships you must apply by 12 noon on 13 December 2018 at the latest.
If you would like us to consider you for one of our scholarships you must apply by 12 noon on 13 December 2018 at the latest.
References
[1] Schnoerr, David, Guido Sanguinetti, and Ramon Grima. "Approximation and inference methods for stochastic biochemical kinetics—a tutorial review." Journal of Physics A: Mathematical and Theoretical 50.9 (2017): 093001.
[2] Menolascina, Filippo, Mario Di Bernardo, and Diego Di Bernardo. "Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks." Automatica 47.6 (2011): 1265-1270.
[3] Fiore, Gianfranco, et al. "In vivo real-time control of gene expression: a comparative analysis of feedback control strategies in yeast." ACS synthetic biology 5.2 (2015): 154-162.
[4] Cao, Zhixing, and Ramon Grima. "Linear mapping approximation of gene regulatory networks with stochastic dynamics." Nature communications 9.1 (2018): 3305.
[2] Menolascina, Filippo, Mario Di Bernardo, and Diego Di Bernardo. "Analysis, design and implementation of a novel scheme for in-vivo control of synthetic gene regulatory networks." Automatica 47.6 (2011): 1265-1270.
[3] Fiore, Gianfranco, et al. "In vivo real-time control of gene expression: a comparative analysis of feedback control strategies in yeast." ACS synthetic biology 5.2 (2015): 154-162.
[4] Cao, Zhixing, and Ramon Grima. "Linear mapping approximation of gene regulatory networks with stochastic dynamics." Nature communications 9.1 (2018): 3305.
How good is research at University of Edinburgh in Biological Sciences?
Research output data provided by the Research Excellence Framework (REF)
Click here to see the results for all UK universitiesProject supervisors
Career overview
Ramon Grima obtained a B.Sc (Hons) in Physics and Pure Mathematics from the University of Malta in 2000, followed by an M.A. in Physics from the University of Virginia in 2002. He completed a Ph.D. in Physics at Arizona State University in 2005. After his doctoral studies, he was a Postdoctoral Fellow at the School of Informatics, Indiana University, from 2005 to 2006. He then held the position of Mathematical Institute Fellow at Imperial College London from 2006 to 2008. Grima joined the University of Edinburgh in 2008 as a Lecturer, progressed to Reader in 2013, and was promoted to Professor in 2019. His research focuses on the chemical master equation in biochemical systems, particularly gene regulatory networks, and he has developed interests in the reaction-diffusion master equation and parameter estimation methods for gene regulatory networks.
Research interests
Ramon Grima's research focuses on the exact or approximate solution of the chemical master equation describing biochemical systems, particularly gene regulatory networks. They are also interested in the approximate solution of the reaction-diffusion master equation, considering the complex nature of the cytoplasm, including phenomena such as macromolecular crowding. A main aim is to obtain closed-form solutions for the approximate distributions of molecule numbers, which can provide insights into stochastic intracellular dynamics and how living cells have evolved to manage inherent noise. Recently, there has been a growing interest in developing efficient methods for estimating parameter values for gene regulatory networks from single cell and population snapshot data.
Career overview
Filippo Menolascina holds the Chair of Engineering Biology at the University of Edinburgh. He is trained as an Electrical Engineer and Computer Scientist, having obtained a BSc in 2006 and an MSc in 2008. Prof. Menolascina completed his PhD in 2011, defending a thesis that provided the first demonstration of in vivo real-time control of a complex synthetic gene network, pioneering the field now known as cybergenetics. Following his doctoral studies, he worked as a postdoctoral researcher at the Massachusetts Institute of Technology, where he extended his research to the control of complex traits emerging from biomolecular networks, achieving the first demonstration of real-time control of aerotaxis in Bacillus subtilis.
Research interests
Prof. Menolascina's research focuses on Engineering Biology, specifically through the cSynBioSys group, which combines in silico methods and in vivo experiments. The group aims to elucidate the design principles of living systems and to re-program cells using these principles. The overarching goal is to develop a Model-Based Biosystem Engineering framework for Engineering Biology, which seeks to automate the design of synthetic circuits, making engineered cells as easy to build and program as computers. Prof. Menolascina's work includes pioneering contributions to cybergenetics and real-time control of biomolecular networks.
Find a scholarship to fund your dream Masters
Sign in to view and filter all scholarship opportunities
or
Continue with Facebook