Cells are constantly bombarded with competing signals—chemical instructions that tell them to divide, die, or stay put—but scientists still cannot reliably predict which instruction a cell will follow. Most drug discovery fails because researchers have studied signalling pathways in isolation, missing the messy reality of how multiple signals interact inside a living cell. This project tackles that blind spot by building computer models that capture the full conversation between a cell’s DNA, its protein factories, and the external cues it receives. The team will then use those models to design synthetic genetic circuits that force cells to produce a predictable outcome when exposed to a drug or stimulus. If the models work, they could transform how drugs are tested. Instead of screening compounds against simplified cell cultures that often mislead, pharmaceutical companies could use engineered cells that behave more like real human tissue. That would reduce the number of promising drugs that fail in late-stage trials—a major source of wasted time and money in the industry. This is fundamental science with a clear engineering goal. The immediate payoff is a deeper, quantitative understanding of how cells make decisions. The long-term impact could be faster, cheaper routes from lab discovery to clinic.
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A cell ensures its survival and proliferation by sensing and responding to environmental cues through a series of intracellular signals that modify its internal functions. These signals can provide mutually exclusive cellular outcomes whilst functioning alongside a multitude of other signals responding to different cues. However, most of our understanding of intracellular cell signalling has been gleaned from reductionist approaches which trace processes of isolated individual pathways. These approaches fail to encapsulate a complete understanding of complex cellular regulation that includes multiple signalling pathways, transcription/translation and heterogeneous responses to stimuli associated with cell fate. One of the most acute manifestations of this failure is the huge burden of drug discovery that is seldom successfully delivered to the clinic. Understanding of the heterogeneous and refractory responses to therapeutics requires adopting a holistic approach to understanding the intricate communication networks of cell fate. In this proposal we aim to deepen our understanding of pathways through engineering of cells to produce a reproducible outcome when they are triggered by external stimuli or therapeutic drugs. The engineering process will involve data-driven modelling of cell fate pathways, transcription and translational regulation, and the design and implementation of synthetic components and circuits. This will be achieved through the collective efforts of an international consortium of experts in cell, computational and synthetic biology. We will provide bespoke research training across the international collaboration network to foster the next generation of interdisciplinary scientists and research leaders in biological engineering.
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