Studying in Complexity Science
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Image: Fractals

Where is complexity science headed?



Fully-funded PhD places are available at the new Center for Doctoral Training in Next Generation Computational Modelling. The CDT pursues computational modelling research spanning engineering, computer science, mathematics, and the physical, natural and life sciences.


One of the most exciting aspects of complexity science is its interdisciplinary nature, and the interface with the life sciences is paramount here. Biology (from ecology, to organismic biology, to neurology, to cellular biology, and molecular biology) is filled with marvellous examples of complex adaptive systems that not only cope with emergent dynamical behaviours but have adapted to control and exploit them in every way imaginable. A lot of research in complexity science is looking for ways to model, understand and extract the useful properties of biological systems. This is both with a view to better understanding of the biological systems systems biology and for inspiration for new approaches to solving technological and engineering challenges.

In the figure below, the left side lists some biological complex systems, and the right side list some example systems from ICT (information and communication technology) that need new approaches to handling complexity. The topics in the centre are examples of subjects that help connect the biological inspiration on the left with the challenges on the right.

Social insect foraging and collective construction behaviour Neurons: mechanisms and coding Evolution and population dynamics DNA and self-replication Epidemics and transmission Metabolic networks Gene regulation networks Immune systems and repair Ecosystems stability and sustainability Morphogenesis and pattern formation Molecular evolution and Enzymatic reactions Cellular differentiation and development plasticity  Dynamical systems Network science Monte-Carlo simulation Graphical models/Bayes Nets Percolation models Associative memory Feedback control Machine learning Statistical theory of complex systems Algorithms for learning from examples Game theory Information theory, probability Developmental representations/evolutionary design Computational complexity measures Principles and concepts of Modularity Population genetics Meta-languages Self-organisation Spontaneous symmetry breaking Artificial life/simulation modelling Time-series analysis Evolutionary Algorithms Amorphous computing Autonomic computing  Economic mechanism design/market based mechanisms Large-scale software development Infrastructure networks Peer-to-peer architectures Semantic web Grid computing Telecoms Infrastructure growth World-Wide-Web Network reconfiguration Business organisation and operations Software dependency and encapsulation

Biological complex systems

Topics and Tools

ICT complex systems