Discriminating between rival biochemical network models: three approaches to optimal experiment design
1 Life Sciences Interface Doctoral Training Centre, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK
2 Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, OX1 3TG, UK
3 Control Group, Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
4 Oxford Centre for Integrative Systems Biology, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, UK
5 Department of Biochemistry and Biophysics and California Institute for Quantitative Biomedical Research, University of California, San Francisco, 1700 4th Street, San Francisco, CA 94158, USA
BMC Systems Biology 2010, 4:38 doi:10.1186/1752-0509-4-38Published: 1 April 2010
The success of molecular systems biology hinges on the ability to use computational models to design predictive experiments, and ultimately unravel underlying biological mechanisms. A problem commonly encountered in the computational modelling of biological networks is that alternative, structurally different models of similar complexity fit a set of experimental data equally well. In this case, more than one molecular mechanism can explain available data. In order to rule out the incorrect mechanisms, one needs to invalidate incorrect models. At this point, new experiments maximizing the difference between the measured values of alternative models should be proposed and conducted. Such experiments should be optimally designed to produce data that are most likely to invalidate incorrect model structures.
In this paper we develop methodologies for the optimal design of experiments with the aim of discriminating between different mathematical models of the same biological system. The first approach determines the 'best' initial condition that maximizes the L2 (energy) distance between the outputs of the rival models. In the second approach, we maximize the L2-distance of the outputs by designing the optimal external stimulus (input) profile of unit L2-norm. Our third method uses optimized structural changes (corresponding, for example, to parameter value changes reflecting gene knock-outs) to achieve the same goal. The numerical implementation of each method is considered in an example, signal processing in starving Dictyostelium amœbæ.
Model-based design of experiments improves both the reliability and the efficiency of biochemical network model discrimination. This opens the way to model invalidation, which can be used to perfect our understanding of biochemical networks. Our general problem formulation together with the three proposed experiment design methods give the practitioner new tools for a systems biology approach to experiment design.