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THemes

Dynamics of Physical and Biological Systems

Physical and biological systems are notoriously hard to model and understand from a theoretical point of view. Their complexity and apparent randomness are compounded by the difficulty of producing reliable data from experiments conducted on ever-changing samples. Nonetheless, in recent years, there has been an explosion of activity in this area, mostly linked to the increasing power of computers. This push for more reliable predictive theories in physical and biological systems can broadly be linked to the appetite of the biomedical community for numerical simulations, which are cheap and do not require ethical approval.

Economic and Financial Dynamics

Economic systems are any thing but Complex Systems, which are essentially a non-equilibrium process, a process of continuous change. The dynamics of economic systems are governed by inherently nonlinear interaction between the constituent parts (actors or markets) to give rise to a labile macro structure. In this research theme we study both the economic and financial systems from the perspective of non-equilibrium process.

Cognition, Decision-Making and Human Behaviour

Complex systems lie at the heart of all psychological and behavioural phenomena, and the study of complex systems is acknowledged as one major agenda to be developed in the immediate future of the psychological, cognitive and the cognitive neurosciences – the latter representing the largest world-wide research effort in psychology at this present time.

Information, Computation and Network Dynamics

Our modern world is complex, and comprises a multitude of natural, engineered and social systems. The goal of this research theme is to design computational simulation approaches that can help us understand the way in which these systems interact, so that we can design better futures. This research theme encompasses a number of methodological approaches, including: System Dynamics, Artificial Intelligence techniques, Machine learning algorithms including Reinforcement learning, Bayesian Networks for Classification and Agent-based simulation and Game Theory.

Biostatistics and Bioinformatics

In this theme we are interested in systems genetics modeling of complex human diseases and molecular phenotypes. In particular, we work in understanding the impact of genetic mutations and polymorphisms on gene expression and transcript processing and the role of these mutations in complex disease susceptibility. The research focuses on the use of model-based methods for statistical analysis of the diverse, and increasingly large, datasets obtained in the biological and medical sciences.

 

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