Professor Sara Jabbari

Supervisor Details

Contact Details

s.jabbari@bham.ac.uk

School of Mathematics, University of Birmingham

Scientific Background

Research Interests

Sara Jabbari’s research group use mathematical modelling to generate experimentally-testable hypotheses that enable us to better understand microbial behaviour, often with the aim of developing or improving treatments for bacterial infections. Her group collaborates with experimentalists to combine models and data, studying bacteria from the intra-cellular level up to host-pathogen interactions.

Supervision Style

In three words or phrases: Collaborative, student-centred, inclusive.

Provision of Training

I don't expect students to start a project with all the skills required to do a PhD with me, after all a PhD is about learning. At the start of a PhD, I discuss training needs with my students who have access to a wide range of taught modules in the School of Mathematics. I have supervised several PhD students with mathematics backgrounds who have trained in molecular biology lab techniques, and supported students with a bioscience background to develop their mathematical modelling skills.

Progression Monitoring and Management

I meet my PhD students weekly in one-one meetings where we set short, medium and long-term goals. I keep an eye on progress and will discuss with you if things don't seem to be going to plan, identifying where changes are needed if appropriate. On interdisciplinary projects, we typically meet with experimental collaborators on a monthly basis.

Communication

I keep my communications to within my own working hours (9-5, Monday to Thursday) and would rarely check my email outside of these hours. My office door is always open, and I'm happy for you to discuss any issues that might be impacting your studies. I will always do my best to help you identify the appropriate support that's needed.

Meetings

PhD Students can expect scheduled meetings with me at least once per week and these meetings will be face-to-face. I am usually contactable for an instant response on every working day.

Work Patterns

The timing of work in my lab is completely flexible, and (other than attending pre-arranged meetings) I expect students to manage their own time.

Notice Period for Feedback

I need at least 2 weeks' notice to provide feedback on written work of up to 5,000 words.

MIBTP Project Details

Primary supervisor for:

Mathematical modelling of toxic-compound accumulation in E. coli: using computational models to enhance industrial bioprocessing & antibiotic efficacy

See the PhD Opportunities section to see if this project is currently open for applications via MIBTP.

Please Note: The main page lists projects via BBSRC Research Theme(s) quoted and then relevant Topic(s).

Mathematical modelling of toxic-compound accumulation in E. coli: using computational models to enhance industrial bioprocessing & antibiotic efficacy

Secondary Supervisor(s): Prof Tim Overton

University of Registration: University of Birmingham

BBSRC Research Themes:

Understanding the Rules of Life (Microbiology, Systems Biology)

Project Outline

Bacteria use a multitude of strategies to survive in toxic environments. This includes preventing intracellular accumulation of toxic substances such as antimicrobials during clinical treatment (indeed, this is a key route by which bacteria display resistance to antibiotic treatments), or solvents produced during industrial bioprocessing. Understanding the basic rules of how bacteria control accumulation of toxic substances therefore has wide ranging implications, from future improvements in antibiotic treatment (via maximising accumulation) to the production of useful chemicals (via minimising accumulation).

Bacteria adopt two fundamental strategies to prevent accumulation of toxic substances: limiting influx into cells via changes in membrane permeability, and active export (via protein complexes called efflux pumps) from the cells. Permeability and efflux are highly regulated by the bacteria via nonlinear interactions between numerous proteins and signals. The complexity of this regulatory network necessitates the use of computational tools such as mathematical models to capture the full picture of bacterial accumulation.

Using the bacterium Escherichia coli (E. coli) as a model organism, in this project we will:

develop mathematical models of the regulation of intracellular accumulation;
parameterise the model against experimental data;
generate experimentally-testable predictions that will identify key control points and, crucially, hypotheses concerning optimal ways to maximise or minimise the accumulation of toxic substances.

Computational methods to be used include differential equation modelling, parameterisation techniques for calibrating the models against experimental data, asymptotic analyses where parameterisation is infeasible, and machine learning on complex data sets. The student will work directly with experimental collaborators, but will also receive basic molecular biology training to generate their own laboratory data.