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Incorporating gene regulation networks in individual-based models

  • Full or part time

    Dr Kreft
  • Application Deadline
    Tuesday, January 31, 2017
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

All cellular processes are governed by networks of genes which detect extra-, intra- and inter-cellular signals to determine a cell’s behaviour at any particular time (genes are not active all the time, instead they are `switched on’ when the cell requires it). It is clear, therefore, that understanding the underlying network will yield important insights into the biological processes that it governs. Given the complexity and size of many of these networks, computational modelling is invaluable in the visualisation and prediction of their dynamics.

The conventional approach to model gene regulation networks is to employ ordinary and, where spatial structure is a consideration, partial differential equations. However, individual-based modelling is an alternative strategy to predict the emergent behaviour of a population of cells on a spatial domain based on the activities of the individual cells making up the population. Using computer programming, the cells’ behaviours are described by rules which they follow with assigned probabilities (e.g. if (hunger > hungerThreshold) then eat) and/or by differential equations describing intracellular dynamics such as gene regulation. This project will seek to combine the above approaches, investigating the influence of gene regulation on a cell’s decision at any particular step in time, by implementing an ODE solver embedded in each individual cell. The results will be compared to more traditional reaction-diffusion systems and also to volume-exclusion models.

The prevalence of gene regulation networks throughout biology means that any number of examples could be modelled. However, this project will focus on populations of bacteria and the influence of cell-cell signalling (quorum sensing) on sporulation (Bacillus subtilis and Clostridium difficile) and disease mechanisms (MRSA and C. difficile).

The successful applicant will join the mathematical biology research group in the School of Mathematics and the Centre for Computational Biology, thus working alongside a large range of interdisciplinary scientists in an exciting and dynamic environment.

Applicants must have (or be in the process of obtaining) a mathematics degree and a background in computer programming.

To find out more about studying for a PhD at the University of Birmingham, including full details of the research undertaken in each school, the funding opportunities for each subject, and guidance on making your application, you can now order your copy of the new Doctoral Research Prospectus, at: http://www.birmingham.ac.uk/students/drp.aspx

Funding Notes

This research project is one of a number of projects in the School of Mathematics. It is in competition for funding with one or more of our advertised PhD projects. Usually the project which receives the best applicant will be awarded supported.

Normally scholarships are only available to UK or EU citizens. Other nationals who are normally resident in the UK or those who have been resident in the UK for a period of 3 years or more are also eligible.

All students with the correct qualifications and access to independent funding are also welcome to apply.

References

[1] H. De Jong, Modeling and simulation of genetic regulatory systems: a literature review.
Journal of Computational Biology 9: 67-103 (2002).

[2] A.B. Goryachev, Understanding bacterial cell-cell communication with computational modeling.
Chemical Reviews 111: 238-250 (2011).

[3] L.A. Lardon, B.V. Merkey, S. Martins, A. Dötsch, C. Picioreanu, J.U. Kreft, B.F. Smets, iDynoMiCS: next-generation individual-based modelling of biofilms.
Environmental Microbiology 13: 2416-2434 (2011).

Related Subjects

How good is research at University of Birmingham in Mathematical Sciences?

FTE Category A staff submitted: 40.00

Research output data provided by the Research Excellence Framework (REF)

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