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Mathematics for mechanistic biology and drug discovery

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Abstract: Recently, mathematical and statically approaches have become increasingly common in cell biology research and drug discovery. Modelling of biomedical processes such as cell division, cancer progression or tumour-immune interaction, helps to elucidate the underlying mechanism by providing predictions that can be experimentally validated. For example, our cell cycle model (Arajuo et al., 2016) revealed how mitotic duration is kept short and constant in mammalian cells. Many such models are available in BioModels (Glont et al., 2018), a repository of mathematical models of biological processes. We do targeted curation of mathematical models in BioModels to enrich models of specific areas such as neurodegenerative disease, diabetes, cancer, blood coagulation, cell cycle, etc. Furthermore, we are developing computational tools that predict the dynamics of cell signalling and cellular response to disease treatments, by integrating appropriate mathematical models with gene and protein expression data from different disease conditions. Additionally, we are also developing visual programming based tools that employ network propagation of drug-target protein-protein interaction network to facilitate identification of novel drugs and their probable adverse effects. I will briefly discuss these tools and their potential application in drug discovery.

This talk is part of the Computational and Systems Biology series.

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