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Institute for Mathematical & Molecular Biomedicine

Background and Mission of the Institute

The IMMB aims to spearhead the College's research and teaching activities at the interface between biology, medicine, mathematics and computation. It aims to be a leading international research centre devoted to the development of quantitative tools for biomedical problems, and an efficient source of mathematical and computational expertise for biomedical researchers. It is located at the College's London Bridge campus.

The Institute is a joint initiative of the Faculties of Natural & Mathematical Sciences (NMS) and Life Sciences & Medicine (LSM), and has close links with the Department of Mathematics (in NMS) and the Randall Division of Cell and Molecular Biophysics (in LSM). The IMMB seeks to contribute to training a new generation of biomedical researchers with a strong theoretical background, and to organise workshops, conferences, and short courses, as well as run the Systems Biomedicine Graduate Programme . It also makes documented software implementations of its research deliverables available to the wider academic community.

A report on the Institute's research activities in its first four years can be found here.

Research themes
 Theme Description
Complex biological networks Analysis of signalling processes in molecular networks (e.g. gene regulation, protein interaction, and metabolic networks), and inter-cellular networks (e.g. neural networks and immune networks). Information-theoretic and statistical analysis of network topologies.
Computational structural biology Analysis and prediction of protein, DNA and RNA structures, and of their interaction dynamics. Prediction of binding sites from structure, molecular dynamics of maromolecules and their environment. Computational analysis of molecular interaction networks.
Stochastic methods in medicine Medical statistics, data analysis and regression, population dynamics and epidemiology, stochastic processes (gene defect accumulation). Bayesian analysis and machine learning for predictive medical use of genetic data.
Survival Analysis Survival and competing risk, sub-cohort identification and characterisation. Theoretical foundations and some applications of our practical tools for our survival analysis are detailed, along with a non-mathematical summary of our analysis.
Bioinformatics Systems biology and functional genomics, analysis of large and complex biological networks, machine learning for property prediction and data classification for molecular signature and biomarker discovery.
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