Mathematical Biology and Medicine

Four-year studentship available at Leeds The project, in partnership with the Defence-science-and-technology-laboratory (Dstl), will involve the development of novel stochastic models of within-host viral infection.
A background (BSc or MSc) in Applied Mathematics, Physics or related subjects is required. The student will learn theoretical, computational and statistical methods and will undertake research visits to Dstl in Porton Down. Apply now
Enquire by email to Grant Lythe or Carmen Molina-París.

Forthcoming programs, conferences and workshops

Jobs News

Current and recent visitors

  • Mario Castro works on mathematical models of systems where fluctuations are relevant (cellular and receptor immunology) and on pattern formation in spatially extended systems (from tumor cell modeling to cauliflower morphogenesis or nano-structuring). The figure shows comparisons of different mathematical models with real experiments.
  • Madhulika Mishra (IISc Bangalore)
  • Narmada Sambaturu (IISc Bangalore)
  • Sathya Baarathi (IISc Bangalore)
  • Shamik Majumdar (IISc Bangalore)

Applications for research leading to a PhD are welcome. Please apply here, naming a potential project and supervisor. Sample projects are as follows:
  • Analysis of high-throughput genomic data applied to diseases such as cancer
    Arief Gusnanto, Charles Taylor, Jeanine Houwing-Duistermaat
    Statistical modelling of copy number alteration in cancer: using statistical methodology to discover patterns within the genomic copy number alteration profiles in cancer patients and how the pattern can be utilised for improved prediction of cancer survival and patients' clinical characteristics.
    Genetic association in complex diseases and cancer: a collaboration with research groups in the School of Medicine to perform fine mapping around a previously identified location to identify genetic variants that are associated with cancer.
  • Mathematical immunology
    Grant Lythe and Carmen Molina-París
    Development of stochastic mathematical and computational models of the immune system in health and disease, of intra-cellular signalling to understand cell fate, and development of diffusive motion models of cell-cell interactions and receptor-ligand interactions.
  • Modelling biodiversity and ecosystems
    Sandro Azaele
    In this project you will be developing mathematical and computational tools for modeling spatial and temporal patterns in ecosystems, understanding their principal drivers across different scales, at population and community level. This will also help developing methodologies for upscaling biodiversity information from fine-scale sampling.
  • Modelling evolution on molecular and macroscopic scales
    Mauro Mobilia
    Inspired by recent results in biology, and also in behavioural sciences, we will combine notions of non-linear dynamics and evolutionary game together with the theory of stochastic processes and numerical simulations to investigate how noise and mobility influences the formation of coherent patterns.
  • Modelling of biomolecules
    Oliver Harlen and Daniel Read, in collaboration with the Astbury Centre
    Simulating the motions of large biomolecules such molecular motors and also soft colloidal particles, entities that are large enough to be beyond the scope of atomistic simulations (that model the motion of individual atoms), but small enough to be affected by thermal fluctuations (Brownian motion).

Recent Mathematical Biology and Medicine seminars

  • Tuesday 6 June 2017 Kevin Burrage(Visiting Professor Oxford University and Queensland University of Technology)
    Unlocking datasets by calibrating populations of models to data density: a study in atrial electrophysiology
    The understanding of complex physical or biological systems nearly always requires a characterisation of the variability that underpins these processes. In addition, the data used to calibrate such models may also often exhibit considerable variability. A recent approach to deal with these issues has been to calibrate populations of models (POMs), that is multiple copies of a single mathematical model but with different parameter values.
    To date this calibration has been limited to selecting models that produce outputs that fall within the ranges of the dataset, ignoring any trends that might be present in the data. We present here a novel and general methodology for calibrating POMs to the distributions of a set of measured values in a dataset. We demonstrate the benefits of our technique using a dataset from a cardiac atrial electrophysiology study based on the differences in atrial action potential readings between patients exhibiting sinus rhythm (SR) or chronic atrial fibrillation (cAF) and the Courtemanche-Ramirez-Nattel model for human atrial action potentials.
    Our approach accurately captures the variability inherent in the experimental population, allows for uncertainty quantification and also allows us to identify the differences underlying stratified data as well as the effects of drug block.

Recent programs, conferences and workshops