The beat of an animal’s heart may soon be replaced by the hum of a computer in some experiments. Scientists have been able to model biology for a while, but they used to only take certain systems in the body into account, while leaving others out. Now biologists and computer scientists are working towards modeling animals – or even entire groups of them – as a whole. Their aim is to get a much deeper understanding of the systems at work, so that they can use their realistic models to test or explain behaviour in the wild. But if it’s reality you’re going for, when is a model ever real enough – when is it done? Prof David Harel from the Weizmann Institute in Israel will introduce you to the modelers and the questions they face.

There are many potential uses for biological modelling, and some aren’t what you might expect. One problem that scientists face is that they produce data faster than they can effectively analyse it, but techniques of modelling coupled with machine learning could help improve the situation. Prof Stephen Muggleton from Imperial College London will talk about his studies in this area. One used models of cells to predict the effects of toxins in a potential new drug, and another involved a robot that carried out experiments determining gene functions cheaper and quicker than it would be possible for a human scientist to do.

Prof David Harel has been at the Weizmann Institute of Science in Israel since 1980, and is incumbent of the William Sussman Professorial Chair. He currently heads the John von Neumann Minerva Center for the Development of Reactive Systems. In 1998 he hosted a series of programmes on Israeli television on Computer Science, and he has received numerous awards for his educational work.

Prof Stephen Muggleton holds the a Royal Academy of Engineering Research Chair (2006-) and is Director of Modelling for the Imperial College Centre for Integrated Systems Biology. Prof. Muggleton’s career has concentrated on the development of theory, implementations and applications of Machine Learning, particularly in the field of Inductive Logic Programming. Over the last decade he has collaborated with biological colleagues on applications of Machine Learning to Biological prediction tasks.

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