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Group

Bioinformatics and Complex Systems Group

Unit(s) of assessment: Allied Health Professions, Dentistry, Nursing and Pharmacy

School: School of Science and Technology

Overview

Our fundamental goal is to understand the self-organising properties of complex biological systems and to comprehend how diverse collective 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; and 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. We apply differential equations, bifurcation analysis and other mathematical and computational concepts to better understand the mechanisms underlying a range of complex biological processes, including tumour growth, anti-inflammatory response, and neurodegeneration amongst others.

Collaboration

  • 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

Members

Publications

  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.