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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.

Research Interests

Network theory

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


  1. Exploring the constituent mechanisms of hepatitis: a dynamical systems approach. Dunster JL, Gibbins JM, Nelson MR. Mathematical Medicine and Biology. (In press; accepted Sept 2022.)
  2. Structure-function clustering in weighted brain networks. Crofts JJ, Forrester M, Coombes S, O’Dea RD, Scientific Reports, 2022, 12(16793).
  3. The role of node dynamics in shaping emergent functional connectivity patterns in the brain. Forrester M, Crofts JJ, Sotiropoulos SN, Coombes S, O’Dea RD, Network Neuroscience, 2020, 4(2).
  4. Spatial considerations in the resolution of inflammation: elucidating leukocyte interactions via an experimentally-calibrated agent based model. Bayani A, Dunster JL, Crofts JJ, Nelson MR, PLoS Computational Biology, 2020, 16(11), e1008413.
  5. Modeling and Simulation of Rat Non-Barrel Somatosensory Cortex. Part I: Modeling Anatomy. Reimann MW, Puchet SB, Santander D, Smith JP, et al, bioRxiv:2022.11.28.516756 (2022).
  6. An application of neighbourhoods in digraphs to the classification of binary dynamics. Conceição P, Govc D, Lazovskis J, Levi R, Riihimäki H, Smith JP, Network Neuroscience 6(2) (2022).
  7. Complexes of tournaments, directionality filtrations and persistent homology. Govc D, Levi R, Smith JP, J Appl. and Comput. Topology 5 (2021).
  8. Predicting novel genomic regions linked to genetic disorders using GWAS and chromosome conformation data – a case study of schizophrenia. Buxton DS, Batten DJ, Crofts JJ, Chuzhanova N, Scientific Reports, 2019, 9(1):17940.
  9. Identification of novel genes associated with longevity in Drosophila melanogaster – a computational approach. Hall BS, Barnett YA, Crofts JJ, Chuzhanova N, Aging (Albany NY), 2019, 11(23):11244-11267. doi: 10.18632/aging.102527.
  10. A Composite Ranking of Risk Factors for COVID-19 Time-To-Event Data from a Turkish Cohort. Ulgen, A, Cetin, S,  Cetin, M, Sivgin, H, Li, W,  Computational Biology and Chemistry. 2022, 98:107681:1-12.
  11. Approximate reciprocal relationship between two cause-specific hazard ratios in COVID-19 data with mutually exclusive events. Li, W,  Cetin, S, Ulgen, A, Cetin, M, Sivgin, H, Yang Y, Int. J. Biostat. 2023, 1-14.
  12. Thought Disorder in Schizophrenia and Bipolar Disorder Probands and Their Relatives. Morgan, CJ, Coleman, MJ, Ulgen, A, Boling, L, Cole, JO, Johnson, FV, Lerbinger, Bodkin, JJA, Holzman, PS, Levy, DL, J. Schizophrenia Bulletin, 2017, 43(3):523–535.