Dr Ozgur Akman
Skills Development Fellowship Vision
A successful fellowship would enable a scientist with a quantitative/biomedical background to establish a truly interdisciplinary research group. They would utilise the opportunity afforded by the award to build a broad network of both theoretical and experimental collaborators, and to determine the fundamental research questions that would shape their post-fellowship career.
- 2010-present: University of Exeter – Lecturer & Senior Lecturer, Mathematics
- 2007-2009: University of Edinburgh – Research Associate, Centre for Systems Biology
- 2004-2007: University of Warwick – Research Associate, Mathematics Institute
- 2003: University College London – Research Fellow, Institute of Child Health
The central theme of my group’s research is developing efficient methods to build and optimise highly-parametrised predictive models of biological systems. Such models are critical in increasing the accuracy of predictions regarding the effects of genetic and chemical manipulations, and will have a significant impact on the life sciences in economically critical areas of medicine and food security. My group’s work addresses a key bottleneck in the systems biology cycle of modelling-prediction-validation, namely the exponential increase in parameter number with model size (the parameter explosion problem). Currently, this bottleneck strictly constrains the size and complexity of models systems that can be directly optimised to experimental data, restricting the range of biological systems amenable to quantitative analysis.
By integrating applied mathematics, computer science and high performance computing, I have applied my methods to gene regulatory networks (including plant signalling networks) and neural systems (including motor control networks). To date, this research has been carried out in collaboration with colleagues from Computer Science and Biosciences. Recently, the broader applicability of the model-fitting methods I have developed has provided scope for new collaborations with the Statistical Science group (on uncertainty quantification) and with biotechnology companies (on integrating artificial intelligence and laboratory automation).
- E. Avramidis & O.E. Akman. Optimisation of an exemplar oculomotor model using multi-objective genetic algorithms executed on a GPU-CPU combination. BMC Syst. Biol., 11: 40 (2017)
- S. Aitken & O.E. Akman. Nested sampling for parameter inference in systems biology: application to an exemplar circadian model. BMC Syst. Biol., 7: 72 (2013)
- O.E. Akman, S. Watterson, A. Parton, N. Binns, A.J. Millar. Digital clocks: simple Boolean models can describe circadian systems. J. Roy. Soc. Interface, 9(74): 2365-2382 (2012)
Ongoing Projects & Grants
EPSRC. EP/P020224/1: GW4 Tier 2 HPC Centre for Advanced Architectures
This project by a consortium of the GW4 Alliance of Bristol, Bath, Cardiff and Exeter, in partnership with Cray and the Met Office, is aiming to provide one of the world’s first ARM64 production High Performance Computing systems, based on new hardware that trades off much greater provision of memory bandwidth for less emphasis on peak FLOP/s, the former being more important for most codes used in computational science research.
As part of the project, a group of expert research software engineers is helping the general research community develop new algorithms, port and optimise scientific codes and rigorously evaluate this important new architecture across a range of application areas, including computational biology modelling. This work has the potential to significantly reduce time-to-solution, thereby expanding the range of techniques that can be employed for parameter optimisation, and the size of models that can be optimised within a reasonable timeframe.
EPSRC. EP/N017846/1: The Parameter Optimisation Problem: Addressing a Key Challenge in Computational Systems Biology
For the predictive power of models to be fully realised in the systems biology domain, methods are required that enable the parameter values yielding the best fits to experimental data to be determined in a computationally efficient manner (parameter optimisation problem). This project is addressing this challenge by: (i) utilising state-of-the-art methods from computer science to accelerate the optimisation process; and (ii) developing new methods specifically engineered for the systems biology domain that can provide insight into model behaviour, beyond simply returning a single estimate of the best fit parametrisation (e.g. methods for identifying parameters which simultaneously fit the model to data generated in diverse experimental conditions).
To test and refine the algorithms developed, they are being applied to the gene network that generates circadian oscillations (the circadian clock) in the key plant species Arabidopsis thaliana, for which high-quality experimental data recorded in a range of genetic and environmental backgrounds is available, together with a suite of mathematical models of varying complexity. In the long term, the ability to optimise such models may help predict how the viability of economically important crop species will be affected by future temperature shifts resulting from climate change.
- Jonathan Fieldsend, University of Exeter
- Andrew Millar, University of Edinburgh
- James Locke, University of Cambridge
- David Rand, University of Warwick
Research Group Connections