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Mathematical and Computational Biology

The complexity of the problems faced by researchers in the biological sciences is substantial and often difficult to overcome by those trained within a single field. Our commitment to supporting collaboration across departments opens new and exciting avenues of research.

Mathematical & Computational Biology Academics simulation “Turing on Turing”

Research Highlights

Jabberdock, New Open Source Software to Model Transmembrane Protein Docking

The new, open source transmembrane protein docking software Jabberdock was developed in Durham by the Degiacomi group. It allows users to model the docking interactions of transmembrane proteins, an important class of proteins that make up 50% of all targets in drug design.
Image of transmembrane protein interactions

Nearly-symmetrical Convex Polyhedral Cages and the Introduction of a New Mathematical Concept

At first glance the TRAP-cage, a structure made up of 24 hendecagons engineered from TRAP (trp RNA-binding attenuation protein), looks regular, but this is a mathematical impossibility. Tiny variations in the nearly-identical hendecagons allow the structure to “fit” with near-miss symmetry.
A structure referred to as a TRAP-cage, made out of 24 nearly regular hendecagons of TRAP (trp RNA-binding attenuation protein)


Visual PDE

VisualPDE is an online tool designed by Benjamin Walker (University of Bath), Adam Townsend (Durham Mathematical Sciences) and Andrew Krause (Durham Mathematical Sciences) to visualise partial differential equations, bringing them to life for a wider audience. The picture in the banner at the top of this page is a simulation based on the Schnakenberg model to form Turing patterns on a representation of Alan Turing. These patterns are important in mathematical biology as a way to describe morphogenesis; the way biological organisms develop their structure as they grow. 

Biomathematics and Biocomputing Special Interest Group

Members of the BSI, led by Dr Ulrik Beierholm (Durham Psychology) set up the Biomathematics and Biocomputing Special Interest Group in 2018. It brings together people from across the University to connect on these topics, including those who are not regularly part of the BSI community.


A snapshot of a Turing Pattern simulation on an earlier version of the Biophysical Sciences Institute logo.

Biomathematics and Biocomputing Special Interest Group

Attendees gather for the launch of the Biocomputation Special Interest Group held in-person during the summer of 2018 (image courtesy of Durham University).
A group of people at the Computational Biology Special Interest Group launch in 2018

Highlight Publications

Aston, S., Negen, J., Nardini, M., & Beierholm, U., 2022. Central tendency biases must be accounted for to consistently capture Bayesian cue combination in continuous response data. Behavior Research Methods, 54, 1.

Bale A., Rambo R., Prior C., 2023. The SKMT Algorithm: A method for assessing and comparing underlying protein entanglement. PLoS Computational Biology, 19(11).

Glover, J.D., Sudderick, Z.R., Shih, B.B.-., Batho-Samblas, C., Charlton, L., Krause, A.L., & Headon, D.J., 2023. The developmental basis of fingerprint pattern formation and variation. Cell, 186, 5.

Krause A.L., Gaffney E.A., Jewell T.J., Klika V., Walker B.J., 2024. Turing Instabilities are Not Enough to Ensure Pattern Formation, Bulletin of Mathematical Biology, 86(2), 2.

Krause, A.L., Gaffney, E.A. & Walker, B.J., 2023. Concentration-Dependent Domain Evolution in Reaction–Diffusion Systems. Bull Math Biol, 85, 14.

Piette, B.M.A.G., & Lukács, Á., 2023. Near-Miss Symmetric Polyhedral Cages. Symmetry, 15, 717.

Piette, B.M.A.G., Kowalczyk, A., & Heddle, J.G., 2022 . Characterization of near-miss connectivity-invariant homogeneous convex polyhedral cages. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 478, 2260.

Prior, C., Panter, J., & Kusumaatmaja, H., 2022. A minimal model of elastic instabilities in biological filament bundles. Journal of the Royal Society Interface, 19, 194.

Vali, M., Mohammadi, M., Zarei, N., Samadi, M., Atapour-Abarghouei, A., Supakontanasan, W., Suwan, Y., Subramanian, P.S., Miller, N.R., Kafieh, R., & Fard, M.A., 2023. Differentiating Glaucomatous Optic Neuropathy from Non-Glaucomatous Optic Neuropathies Using Deep Learning Algorithms. American Journal of Ophthalmology.

Yaghoubi N., Masumi H., Fatehi M.H., Ashtari F., Kafieh R., 2023. Deep learning and classic machine learning models in the automatic diagnosis of multiple sclerosis using retinal vessels. Multimedia Tools and Applications. doi:10.1007/s11042-023-16812-w

A purple dividing line

Mathematical & Computational Biology Academics

Dr Ulrik Beierholm, Department of Psychology 

Areas of Expertise: Computational Neuroscience             

Research Interests: How the nervous system deals with uncertainty, whether in perception, decision making or learning.


Dr Alessandro Borghi, Department of Engineering

Area of Expertise: Biomedical Engineering, Biomechanics

Research Interests

  • Biomechanics
  • Finite Element Analysis
  • Biological Tissue Characterisation
  • Biomaterials
  • Medical Image Processing
  • Surgical planning
  • Medical Devices
  • Machine Learning


Dr Matteo T. Degiacomi, Department of Physics               

Areas of Expertise: Molecular Dynamics, Machine Learning, Computational Biophysics    

Research Interests: The development of protein-protein docking methods, and techniques combining machine learning and molecular dynamics simulations to sample protein conformational spaces. 


Dr Lian Gan, Department of Engineering  

Areas of Expertise: Fluid Mechanics, Vortex Dynamics

Research Interests

  • Pulsatile and periodic flows
  • Vortex dynamics in cardiovascular system
  • Flow structure interaction in cardiovascular system
  • 4D flow MR 


Dr Ostap Hryniv, Department of Mathematical Sciences

Areas of Expertise: Probability and Stochastic Processes 

Research Interests

  • Phase transitions
  • Interacting particle systems
  • Large deviations
  • Stochastic modelling 


Dr Rahele Kafieh, Department of Engineering

Areas of Expertise: Biomedical image and signal processing, Artificial Intelligence

Research Interests

  • Medical Data Analysis
  • Machine learning / Deep Learning
  • Image Processing and Computer Vision
  • Data Acquisition and Management
  • Time-frequency methods
  • Dictionary learning
  • Data Quality Assessment


Professor Ashraf Khir, Department of Engineering

Areas of Expertise: Physiological Fluid Mechanics, Artificial Hearts/ Ventricular Assist Devices (VAD). 

Research Interests

  • Arterial waves
  • Arterial stiffness – wave speed - PWV
  • Non-invasive physiological hemodynamic ultrasound measurements
  • Wave intensity analysis (WIA)
  • Arterial wall mechanics
  • Intra-Aortic Balloon Pump (IABP)
  • Design of continuous and pulsatile flow blood pumps
  • Mock circulatory loops


Dr Andrew Krause, Department of Mathematical Sciences           

Areas of Expertise: Mathematical and Computational Modelling 

Research Interests

  • Pattern formation in developmental biology 
  • Reaction-diffusion systems 
  • Spatial population dynamics 
  • Nonlinear dynamical systems 


Professor Bernard Piette, Department of Mathematical Sciences              

Areas of Expertise: Mathematical Physics and Biophysics 

Research Interests

  • Mathematical modelling of biological systems
  • Geometries of nano-bio-materials 
  • Electron-phonon interaction in nano-systems 


Dr Christopher Prior, Department of Mathematical Sciences       

Areas of Expertise: Magnetohydrodynamics, Topological Constraints 

Research Interests

  • Magnetohydrodynamics
  • Solar physics
  • Protein dynamics
  • Biological filament elasticity and topological constraints


Professor Anne Taormina, Department of Mathematical Sciences            

Areas of Expertise: Biological Applications of Mathematics

Research Interests

  • Group theory and applications to mathematical biology
  • String and conformal field theory


Dr Adam Townsend, Department of Mathematical Sciences        

Areas of Expertise: Applied & Computational Mathematics, Mathematical Biology 

Research Interests

  • Development of mathematical and computational techniques to investigate complex fluids  
  • Non-Newtonian fluid mechanics 


Dr Chris Willcocks, Department of Computer Science     

Areas of Expertise: Computer Science 

Research Interests: Deep generative modelling, such as diffusion probabilistic models and normalising flows, with applications in unpaired domain translation, anomaly detection, physics, biology and chemistry.