Complexity Science: Systems Thinking from New Biology to New ICT Challenges

We propose to run six one-week taught courses in complexity science. 'Complexity' is used here in a special sense to refer to systems that have a large number of relatively simple parts that interact to produce collective behaviour (which may not be evident from the behaviour of the individual parts). Although historically complexity science has addressed systems as uniform as the interaction of grains in piles of sand, the richest examples of such systems are biological systems: e.g. organisms composed of cells, or metabolic systems composed of networks of interacting organic molecules. Interesting examples in man-made systems include the somewhat self-organised properties of world-wide-web content, or urban development patterns. The theme of these courses will be the unification of complexity principles from computing/information technologies and the biological sciences. This will address the new challenges that these disciplines are facing: 1) Modern Information and Communication Technology (ICT) systems need to be flexible, robust, adaptable and scalable but conventional approaches to design, control and software engineering are increasingly unable to provide this. 2) Very recently, the biological sciences have been able to collect enormous amounts of valuable data about the details of genetics, proteins, and other levels of biological organisation and it is now, like never before, able to approach broader research questions about how these systems are organised and how their functions integrate into a living whole. This requires new modelling and analysis techniques that address the complex dynamic properties of these systems. Underlying these challenges in both disciplines are a common set of principles and concepts rooted centrally in complexity science. We propose a new training programme in complexity science that tackles the core principles and issues unifying these two domains. We will provide a new syllabus using examples from all levels of biology: from molecules, to cells, to tissues, to organisms, to populations and social groups; and compare these with example technological systems and their challenges and needs. We use these examples to teach a complexity science framework of thinking that provides the modelling, analysis and 'systems mind set' necessary to move beyond conventional toolkits. Such examples force new ways of thinking about design, control and analysis: in some cases biological analogies have already led to established biologically-inspired computational methods (neural networks, evolutionary computation, ant colony optimisation), in other cases, new models and formalisms provide new analogies for thinking about ICT systems. In the other direction, the application of physical sciences tools and methods to biological systems provides a toolkit of modelling, formalisation and abstraction for the complexity science topics that the biological sciences require to move forward.The courses will provide a mixture of biological and technological examples matched with modelling techniques and practical analysis tools that can be applied to these domains. Each afternoon will provide practical lab sessions where attendees can use computational models and tools to explore and apply what they have learned to example systems. The evenings will include group problem exercises that students can do in groups in a casual atmosphere ('bar exercises') to discuss and reinforce ideas and learn about each others research areas and how complexity issues are involved. We will offer four different sub-themes on complexity science each having a different emphasis and examples, and teaching different tools and analysis techniques: Networks and Robustness, Adaptation and Plasticity, Evolvability and Scalability, and Communication and Collective Organisation. Students may attend just one sub-theme or several. Two sub-themes will be repeated making six courses offered in total.