Completed Engineering Physics & Astronomy

LSI Doctoral Training Centres: University of Oxford

In plain English

AI plain-English summary

A new doctoral training centre at Oxford University will train researchers to build computer models that predict how entire cells behave, from the behaviour of individual proteins upwards. This matters because biology currently has a blind spot. Scientists can study a single protein in exquisite detail, or observe what a whole cell does, but they struggle to connect the two. The gap between molecular knowledge and whole-organism behaviour limits progress in drug development, synthetic biology, and understanding disease. Without a systematic way to bridge these scales, researchers are left with isolated facts that do not add up to a working picture of a living cell. The centre’s research programme will develop mathematical and computational tools to link theoretical predictions with experimental data across these scales. If successful, it could transform how biologists design experiments and interpret results—making it possible to predict, rather than just observe, how a cell responds to a drug or a genetic change. This is fundamental science. There is no immediate practical application. But similar systems-level thinking in physics and engineering has underpinned everything from weather forecasting to aircraft design. A deeper, predictive understanding of how cells work could eventually reshape medicine, biotechnology, and our ability to engineer living systems.

View original technical description
The DTC will have its own distinctive research programme that will be strongly linked to the University's new Centre for Integrative Systems Biology (CISB) and together will provide a focus within the University for the existing extensive research activity in Systems Biology. The research focus of the SB DTC will be in developing a systems approach to bridging the gap between theoretical and experimental knowledge from the level of the individual protein to the level of the whole cell/organism.

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Researchers

David Gavaghan (Principal Investigator)

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Original classification

Training Grant

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