Deciphering Genetic Networks
Statistics is used to help understand how living organisms function at a genetic level. This image shows plausible pathways that describe how signals are transmitted within and between cells; our task is to develop statistical methodology that allows us to decide which model best describes the true underlying biology. It is hoped that by increasing our knowledge of how such pathways operate, we may also increase our understanding of the diseases that result when the pathways malfunction.
For more information have a look at the following research papers:
- Xu T., Vyshemirsky V., Gormand A., von Kriegsheim A., Girolami M., Baillie G., Ketley D., Dunlop A., Milligan G., Houslay M. and Walter Kolch. (2010) Inferring Signaling Pathway Topologies from Multiple Perturbation Measurements of Specific Biochemical Species. Science. Signal. Volume 3, Issue 113. pp 1 - 10.
- Calderhead B. and Girolami M. (2009) Estimating Bayes factors via thermodynamic Integration and Population MCMC. Computational Statistics and Data Analysis, 48. pp 4028 - 4045.
- Vyshemirsky V. and Girolami M. (2008) Bayesian ranking of biochemical system models. Bioinformatics, Volume 24, Issue 6, pp 833 - 839.
This work is funded via the following research grants:
- Inference-based Modelling in Population and Systems Biology - BBSRC BB/G006997/1 - 2010 to 2013
- Computational Statistics and Cognitive Neuroscience - EPSRC EP/H024875/1 - 2009 – 2011
- The Molecular Nose - EPSRC EP/E032745/1 2007 to 2011
- The Synthesis of Probabilistic Prediction and Mechanistic Modelling within a Systems Biology Context - EPSRC EP/E052029/1 - 2007 to 2012
- Bayesian Inference in Systems Biology: Modelling Organ Specificity of Circadian Control in Plants - Microsoft Research - 2007 to 2011
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