The Parameter Optimisation Problem: Addressing a Key Challenge in Computational Systems Biology

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Biological Sciences


In recent years, advances in both the physical and life sciences have increasingly come from the collaborations of researchers across disciplines, and the development and use of tools from a range of areas. A prototypical example of this interdisciplinary approach to science is systems biology, the field concerned with quantifying how the interaction of individual system components control biological function and behaviour. Systems biology has become increasingly quantitative, with a shift from diagrammatic representations of interaction networks to sets of mathematical equations that model (i.e. simulate) how the concentrations of molecular species vary with time. A key advantage of such models is that they can be used to predict how the networks they represent will respond to specific perturbations, such as changes in environmental conditions (e.g. temperature) or the addition of pharmacological agents. The ability to easily generate such predictions reduces the need for large numbers of expensive and time-consuming experiments.

However, the more complex a biological network is, the more complex the corresponding model needs to be, and the greater the range of possible biological behaviours that can be exhibited. This means that extensive computer simulations are needed to adjust the parameters controlling the model so as to accurately reproduce (i.e. fit) the experimental behaviour observed. For biologically realistic models which can involve hundreds of different molecular species, the number of simulations required to adjust the parameters of a given model to achieve the optimal fit to data can be prohibitively large, far exceeding that which is possible on practical timescales. Thus, for the predictive power of mathematical models to be fully realised in the systems biology domain, methods are required that allow this parameter optimisation procedure to be carried out in a computationally efficient manner.

The proposed project will address this need by bringing state-of-the-art methods from computer science to bear on the problem, which have been successfully applied previously to highly parametrised problems like aircraft conflict alert systems, design optimisation of lightweight materials and routing of mesh sensor networks (amongst others). In addition, we propose to develop 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 yielding equally good fits to data, and also parameters which simultaneously fit the model to data generated in diverse experimental conditions). As part of this, we will develop a package of open source software tools that will be embedded within a software infrastructure designed for systems biologists, enabling the methods developed in this work to be readily applied to problems in the field that are currently computationally intractable.

To test and refine the algorithms developed, they will be 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. As part of this work, biochemically detailed models of the clock will be directly fitted to multiple experimental datasets for the first time, yielding models with greater predictive power. Many processes critical for plant growth and reproduction are regulated by the clock (e.g. photosynthesis and flowering time). In the long term, the ability to optimise plant models of increasing complexity with the class of methods we will develop here may thus help predict how the viability of economically important crop species will be affected by future temperature shifts resulting from climate change.


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