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Details of Grant 

EPSRC Reference: EP/K040987/1
Title: Boolean modelling of biochemical networks
Principal Investigator: Akman, Professor OE
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Mathematical Sciences
Organisation: University of Exeter
Scheme: First Grant - Revised 2009
Starts: 20 January 2014 Ends: 19 January 2016 Value (£): 101,108
EPSRC Research Topic Classifications:
Logic & Combinatorics Non-linear Systems Mathematics
Numerical Analysis
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
12 Jun 2013 Mathematics Prioritisation Panel Meeting June 2013 Announced
Summary on Grant Application Form
The study of biological systems, from cells, to organisms and populations, is becoming increasingly quantitative. In particular, the way that biochemical networks are described is changing from the traditional diagrammatic sketch of possible interactions to a set of mathematical equations that simulate (i.e. model) how the concentration of each molecular species varies with time. A key advantage of mathematical models is that they can be used to predict the response of networks to external perturbations, such as changes in environmental conditions or the addition of pharmacological agents. This reduces the need for large numbers of expensive and time-consuming experiments. However, the more complex a biochemical network model, the greater the range of possible dynamic behaviours it can exhibit. Consequently, extensive computer simulations are necessary for accurate predictions of experimental behaviour to be obtained. For biologically realistic models that can comprise hundreds of molecular species, the number of simulations required far exceeds that which is possible on a practical timescale. It follows that for the predictive power of mathematical models in biology to be fully realised, there is a pressing need for methods that allow their behaviour to be comprehensively explored in a computationally efficient manner.

The proposed project will address this need by developing a new modelling methodology based on representing biochemical networks as digital circuits. In this approach, each species is considered to be either "on" (i.e. present) or "off" (i.e. absent), and changes in concentration are simply treated as transitions between these two states. The significant reduction in computational complexity that this affords has the potential to greatly expand the range of networks that can be usefully modelled. As a test case of the approach, digital circuit models will be constructed of the gene network that generates circadian oscillations in an important plant species, Arabidopsis thaliana. Circadian oscillations regulate many processes critical for plant growth and reproduction, such as photosynthesis and seed germination. As part of this work, the computational tractability of the digital circuit models will be exploited to determine how temperature modifies the plant circadian network. In the long term, this may prove important for predicting how future temperature shifts will affect the viability of important crop species, thereby informing strategies for breeding varieties with increased resistance to climate change.

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Organisation Website: http://www.ex.ac.uk