The Synthesis of Probabilistic Prediction & Mechanistic Modelling within a Computational & Systems Biology Context

Lead Research Organisation: University College London
Department Name: Statistical Science


The synergistic advances that can be made by the multidisciplinary interplay between abstracted computational modelling and biological experimental investigation within a system biology context are poised to make major contributions to our understanding of some of the most important biological systems implicated in the genesis of many serious diseases such as cancer. However, due to the unavoidable inherent levels of uncertainty, noise and relative scarcity of biological data it is vital that sound evidential based scientific reasoning be enabled within a systems biology context by formally embedding mechanistic models within a probabilistic inferential framework. The synthesis of mechanistic modelling & probabilistic inference provides outstanding opportunities to make further significant advances in understanding biological systems and processes at multiple levels, by defining system components and inferring how they dynamically interact. There is a major role that statistical machine learning methodology has to play in both computational & systems biology research and a number of important methodological challenges are presented by applications working at this interface.However, one of the most important aspects of successful computational & systems biology research is that it must be conducted in direct collaboration with world-class experimental biologists. An outstanding feature of this Fellowship is that it has set in place six exciting collaborations with internationally leading cancer researchers, proteomics technologists, biochemists and plant biologists who are all fully committed to successfully driving forward a potentially groundbreaking multidisciplinary systems biology research programme as detailed in this proposal. Three important application areas within biological science will shape and direct the research to be undertaken during this Fellowship. The applications are distinct, yet overlap in terms of the modelling & inferential issues which each present and this is important in ensuring a consistent and coherent line of research. They have also been selected for their major importance in the study of cellular mechanisms which are fundamental to cell function, some of which are implicated in certain serious diseases. In addition, the applicant has substantive ongoing collaborations with world-class laboratories engaged in these biological investigations. This ensures the proposed research programme is focused on realistic methodological problems which will have a direct impact on the major scientific questions being asked within each area, as well contributing to the computational and inferential sciences. The first application will develop the inferential tools required by cancer biologists when reasoning about the structures underlying the observed dynamics of the MAPK pathway and these tools will be employed in a large scale study of this pathway in collaboration with the Beatson Institute of Cancer Research. The second application, to be conducted with the Plant Sciences group at the University of Glasgow, will seek to elucidate, in a model-based inferential manner, the remarkable observed phenomenon of organ specificity of the circadian clock in soybean and Arabidopsis, in addition a study of models of transcriptional regulation in the cell-cycle will be conducted. The final application will investigate a number of open issues associated with clinical transcriptomics and proteomics where the identification of possible target genes and proteins is of vital importance to cancer researchers in their studies of, in this case breast and ovarian cancer. This study will be conducted in direct conjunction with the Institute of Cancer Research where an ongoing study of BRCA1&2 mutations implicated in breast and ovarian cancer is underway.


10 25 50

publication icon
Girolami M (2011) Riemann manifold Langevin and Hamiltonian Monte Carlo methods Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods in Journal of the Royal Statistical Society: Series B (Statistical Methodology)

publication icon
Molina F (2010) Systems biology: opening new avenues in clinical research. in Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association

publication icon
Stathopoulos V (2013) Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation. in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences

Related Projects

Project Reference Relationship Related To Start End Award Value
EP/E052029/1 01/11/2007 08/11/2010 £797,161
EP/E052029/2 Transfer EP/E052029/1 08/11/2010 30/04/2013 £343,928