Biologists and computer scientists are building a new kind of computational toolkit to model how living systems—from a single cell to the entire planet—actually work as integrated wholes, rather than as collections of isolated parts. Current science struggles to predict how complex natural systems behave because their components interact in non-linear, tightly coupled ways across different scales. A cell’s behaviour, for example, cannot be understood by studying its genes alone; climate and ecosystems regulate each other in feedback loops that existing models cannot capture. This project addresses the gap between the data we have and the models we need: biological theories are tested against imperfect data sets, and no academic environment currently exists to build the necessary computational tools. If successful, this work will create a new software environment and theoretical methods that allow researchers to translate solutions between different scientific problems—for example, applying insights from cellular biology to ecosystem modelling. The consortium will also train a new generation of scientists who can work across these boundaries. This is fundamental science: the immediate output is a cultural and technological shift in how mathematical models are developed and shared, not a direct product. Past investments in such cross-disciplinary tool-building have enabled breakthroughs in fields from drug discovery to climate prediction.
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The key scientific challenges of the next century require that we fundamentally advance current understanding of complex natural systems - ranging from how cells work and why/how they go wrong, to how the interaction of climate and ecosystems regulates the planet's life support system. We wish to understand how these systems behave at the functional level, and how this behaviour arises as a result of highly dynamic, strongly non-linear, tightly coupled interactions between component processes occurring across multiple spatial and temporal scales. Entirely new kinds of exploratory and predictive models and research strategies are needed to address these challenges. A new generation of theoretical methods and tools are needed to enable the recognition and exploitation of synergies and similarities that allow the translation of solutions between different scientific problem domains. Biological theories are evaluated against often imperfect data sets. There is a need for judicious selection and validation of the test bed against which any model is evaluated and the development of novel technologies for gathering new data identified as critical to complex system behaviour. The investigation of systems-level behaviour requires the identification of biological hypotheses that could not have been expressed by looking at individual phenomena alone. The unprecedented degree of complexity will mean that these quantitative models will be analytically intractable, and exploring their behaviour will be possible only through a computational approach. In short, a novel computational approach and environment is needed for doing this kind of science - and this does not exist in academia today. Progress requires both a cultural and technological change in the way in which mathematical and computational models, tools and software are developed, and a concomitant change in the way in which groups of scientists are trained to develop and use these approaches. This work can only be done in the context of real biological problems. This proposal brings together a consortium of partners from academia and industry, each of whom has begun to focus on differing but complementary aspects of these problems. These centres are the ideal community to attempt such a cultural shift since they are already dedicated to training the next generation of scientists who will pioneer this kind of science.
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