Model Parameterisation in Healthcare and Life Sciences: Extracting Knowledge from Data
- Michael Tippet Room Staff House
For more information or to request a place at this workshop please email Lauren Rawlins
Professor Dave Smith, School of Mathematics and Institute of Metabolism and Systems Research
Technological developments in the life sciences, for example, imaging, mass spectroscopy, flow cytometry, DNA and RNA sequencing, provide hitherto unimaginable volumes of data, from intra and inter-cellular signalling, organ function, to whole organisms, to populations and ecological systems. These technologies provide the potential for breakthroughs in biomedical science, new diagnostics and drugs, and personalised medicine. Examples of this type of research at the University of Birmingham include sperm motility and DNA damage research for fertility treatment, stem cell therapy for haematological cancers, adrenal hormone dysfunction and adrenal cancers, antimicrobial resistant infections. However, to make sense of data involves making the connection to models – precise descriptions of scientific knowledge. A crucial part of this process is model parameterisation – finding the unknown rates, affinities, dimensions etc. of the models. The process of model parameterisation enables us to determine which models are better descriptions of biological reality, how patients differ from each other, and crucially the ability to make predictions:
- if/how a disease will develop
- whether a treatment will be successful
- how to deliver the best possible outcome
Many existing techniques have focused on ordinary differential equation models, which describe for example how molecular concentrations vary in time. A key challenge in understanding living systems is to expand these methods to address systems with stochasticity (random variability), spatial variation, optics and mechanics (e.g. fluid flow and solid deformation).
The workshop will be focused on cutting edge techniques, bringing international and national expertise to bear immediately on problems of clinical and industrial importance, and putting the theory into practice.
University of Birmingham Participants