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Photo of Dr Ozgur Akman

Dr Ozgur Akman

Senior Lecturer


Telephone: 01392 724060

Extension: (Streatham) 4060

Office: Laver Building 814A


Twitter: @Dr_OzgurAkman

Research interests

I am a lecturer in Applied Mathematics and member of the Living Systems Mathematics group. My research focuses on the development of computational methods to systematically construct and analyse quantitative models of biochemical and neural systems. A particular focus of my work is combining machine learning with evolutionary algorithms to optimise highly-parametrised predictive models.

My doctoral work addressed the modelling of the neural networks underlying eye movement control and the tremor disorders resulting from their malfunction. Subsequently, I worked on modelling the low-level visual processing controlling attention during the viewing of multistable images. I then carried out further postdoctoral research on computational modelling of gene regulatory networks, in particular circadian clocks, during which I also worked with developmental biologists on using agent-based modelling to understand intracellular pore formation, and with computer scientists on using process algebras to represent and simulate biochemical networks.

My increasing interest in optimising high-dimensional network models led me to Exeter, where I began investigating reduced dynamic modelling to infer GRNs from multiple experimental datasets. This work was motivated by the following two questions: (i) to what extent can models of biological networks be simplified, yet still preserve predictive capacity? and (ii) how can such models be most effectively leveraged in the model construction process? A project related to this work is currently combining machine learning, single- and uni-objective evolutionary optimisation and landscape analysis to fit models of GRNs to experimental timeseries. Within this theme, I have also carried out work on using Bayesian statistical methods to efficiently infer the parameter distributions of dynamic models from experimental data. In addition, my group has been assessing the generalisability of the model-fitting algorithms developed as part of this line of research by applying them to spiking neuron models. The high-dimensional nature of the models that are central to these projects have required numerical algorithms specifically adapted to hybrid, high performance computing architectures.

Most recently, I have extended my research to encompass experimental reproducibility and the design of large-scale, automated biological experiments. The key question underpinning this work is the following: to what extent can artificial intelligence be utilised to design iterative, autonomous biotechnology experiments that are competitive with natural intelligence?


Current group members

Xingzuo Pan (PhD student): Machine learning and optimisation for synthetic biotechnology (co-supervisors: Dr. Paul JamesProf. Ed KeedwellProf. John Love)

Melike Karatas (PhD student): Network visualisation of high-dimensional search: developing data analytics for computational biology problems (primary/joint supervisor: Prof. Jonathan Fieldsend)

Dominic Dunstan (PhD student): Understanding the network dynamics of seizures (primary supervisor: Dr. Marc Goodfellow)

Previous group members

Jake Pitt (PDRA): EPSRC grant "The Parameter Optimisation Problem: Addressing a Key Challenge in Computational Systems Biology"

Khulood Alyahya (PDRA): EPSRC grant "The Parameter Optimisation Problem: Addressing a Key Challenge in Computational Systems Biology". Now lecturer in Computer Science at Exeter

Kevin Doherty (PDRA): EPSRC grant "The Parameter Optimisation Problem: Addressing a Key Challenge in Computational Systems Biology"

Arno Steinacher (PDRA): EPSRC grant "Evolving Controllers and Controlling Evolution"

Francesco Montefusco (PDRA): EPSRC grant "Evolving Controllers and Controlling Evolution"

Lefteris Avramidis (PhD student): Optimisation and computational methods to model the oculomotor system with focus on nystagmus

Varn Kothamachu (PhD student): An investigation into dynamic and functional properties of prokaryotic signalling networks  

Suhaib Mohammed (PhD student): Consensus network inference of microarray gene expression data  


I am always looking for talented PhD students and postdocs to join my group.

Currently, I have the following PhD projects available to self-funded applicants:

  • Computational approaches to maximising lettuce yield in plant factories

  • Machine learning and optimisation for combined antibiotic treatments

  • Boolean modelling of the hypothalamic-pituitary-adrenal axis

  • Iterative experimental design for spiking neuron models

Please contact me if you are interested in any of these projects, or if you have your own project in line with my research interests that you would like to develop further.


I am teaching on the following courses:

MTH3006: Mathematical Biology and Ecology

MTHM009: Advanced Topics in Mathematical and Computational Biology

NSCM005: Mathematical Modelling in Biology and Medicine

MTHM040: MSci Mathematics Project (module leader)

MTHM002: Methods for Stochastics and Finance (module leader)

Recent publications

Karatas MD, Akman OE, Fieldsend JE. Towards population-based fitness landscape analysis using local optima networks. Proc GECCO 2020. 1674-1682 (2021)

Abadi RV, Akman OE, Arblaster GE, Clement RA. Analysing nystagmus waveforms: a computational framework. Sci Rep. 11(1):9761 (2021)

Clement RA, Akman OE. Slow-fast control of eye movements: an instance of Zeeman's model for an action. Biol Cybern. 114(4-5):519-532 (2020)

Avramidis E, Lalik M, Akman OE. SODECL: An Open-Source Library for Calculating Multiple Orbits of a System of Stochastic Differential Equations in Parallel. ACM T Math Software, 46(3):24 (2020)

Foo M, Bates DG, Akman OE. A simplified modelling framework facilitates more complex representations of plant circadian clocks. PLoS Comput Biol. 16(3):e1007671 (2020)

Akman OE, Fieldsend JE. Multi-objective optimisation of gene regulatory networks: insights from a Boolean circadian clock model. Proc BICOB 2020,70:149-162 (2020)