Centre for Systems Biology at Edinburgh

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Systems Biology is a fascinating development in modern biology. We have achieved a good general understanding of how cells work, including the 'central dogma': genes are transcribed to RNA; a splicing process produces mature messenger RNA; mRNA is translated to proteins; and protein pathways regulate gene expression and perform other functions such as detecting intercellular signals. With the Human Genome Project we know our DNA sequence and have a partial map of our genes. And, finally, high-throughput biology is giving us massive amounts of time series data, e.g., of protein or mRNA concentrations. Systems Biology seeks to understand how biological systems function by integrating all this knowledge. System theories are implemented as in silico models: computer simulations built using mathematical models. Biological systems are extraordinarily complex with many levels of interacting subsystems. We therefore expect to construct models by combining submodels, beginning with pathways, and eventually proceeding to organelles, cells, physiological systems and whole organisms. One hopes to be able to predict the effect of variations, e.g.: environmental, or resulting from disease or adding drugs. Such a Systems Biology would produce an enormous increase in understanding and lead to major progress in medicine, agriculture and industry. The Edinburgh Centre for Systems Biology will advance our ability to make and use such models by applying advanced computer science and mathematical techniques to a carefully chosen range of important biological systems which are different enough to test our model-making ability to the limit. Our largest such system is the interferon pathway, an important signaling pathway in macrophages, the main immune system cells. It is already hard here to conveniently describe the pathway intricacies, and we shall develop new international graphical standards. The middle-sized system is RNA metabolism, the process leading from raw to mature RNA. It should be possible to model this still complex system in detail, correlating the models with high-throughput data. Finally comes circadian rhythm, biological clocks, where small genetic circuits regulate large parts of gene expression. Here we may perform mathematical analyses, e.g., investigating how light and temperature synchronise clocks in a noisy environment. The traditional modelling technique of mathematical biology uses systems of differential equations: systems biology presents new challenges. We shall produce SBSI, a modelling facility of industrial quality freely available to all. Probabilistic models are sometimes more realistic than differential ones, e.g., for few protein molecules. We shall explore such variations to ensure realistic yet tractable modelling. High-throughput data are noisy and hard to obtain in sufficient quantity. We shall apply Bayesian techniques, familiar from Artificial Intelligence, to help discover pathways. We wish to construct big systems from small ones (modules) and to experiment efficiently with system variants. Programming languages let one do this for computational systems; we shall apply the lessons learnt to design and use languages for biological ones, with the additional prospect of being able to query and design systems using special logics. In summary, we intend to build a science of Systems Biology using computer science and mathematics to produce models refined by and informing biological experiment. The variety of the biology we do will ensure the wide usefulness of the techniques; the variety of the techniques we will explore will give the enterprise every prospect of success. However to achieve usefulness requires much more. We will therefore combine our scientific effort with training and outreach programmes: the one to contribute to the production of the next generation of systems biologists; and the other to make our work available to our colleagues in academia and our partners in industry.