Bioinformatics and Complex Systems Group
Unit(s) of assessment: Allied Health Professions, Dentistry, Nursing and Pharmacy
School: School of Science and Technology
Our goals are to understand the self-organising properties of complex biological systems. And also to comprehend how diverse behaviours emerge on comparatively static networks of interacting units or dynamical systems.
Our interest is in developing new mathematical concepts that permit a better understanding of the organisational and functional properties of complex biological systems. Current work includes:
- the development of novel, bio-inspired network measures, capable of detecting features of pertinence to system functionality
- the extension of network science concepts to more general network structures, such as hypernetworks and multi-layered networks
- the construction of measures that incorporate important, often ignored network characteristics, such as directionality and/or weight.
Computational analysis of biomedical data
The current focus is on applications to neuroimaging data of human brain structure and function, but also encompasses the study of other biological networks such as protein-protein interaction networks and metabolic networks. We use mathematical and statistical techniques including linear algebra, network science, optimisation, combinatorics, machine learning and applied topology to solve complex biological problems.
Modelling of complex biological systems
We use theories of dynamical systems to model aspects of human physiology. Mathematical and computational methods are used to understand the mechanisms behind a range of complex biological processes, including tumour growth, anti-inflammatory responses, and neurodegeneration.
Statistical Genetics and Epidemiology
Statistical genetics is an interdisciplinary field with the goal of finding human disease genes. We use tools from mathematics, statistics, computer science, genetics and epidemiology to analyse complex disorders. Our work on statistical epidemiology investigates the incidence, distribution, and risk/prognostic factors related to health and disease.
- Joanne L. Dunster and Jonathan M Gibbins, Institute for Cardiovascular and Metabolic Research, University of Reading
- Marcus Kaiser, Faculty of Medicine & Health Sciences, University of Nottingham
- Steve Coombes and Reuben O’Dea, Department of Mathematics and Statistics, University of Nottingham
- Keith Smith, Computer and Information Sciences, University of Strathclyde
- Neuro-Topology Research Group, School of Natural and Computing Sciences, University of Aberdeen
- Connectomics Group, Blue Brain Project, EPFL
- The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
- Department of Biomedical Engineering, University of Arizona, USA
- Jonathan Crofts
- Martin Nelson
- Jason Smith
- Ayse Ulgen
- Nadia Chuzhanova (Emeritus Professor)