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On the deduction of chemical reaction rate constants from measurements of time series of concentration

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Abstract: The estimation of parameter values is the bottleneck of the computational analysis of biological systems. Modeling approaches are central in systems biology, as they provide a rational framework to guide systematic strategies for key issues in medicine as well as the pharmaceutical and biotechnological industries. Inter- and intra-cellular processes require dynamic models, that contain the rate constants of the biochemical reactions. These kinetic parameters are often not accessible directly through experiments. Therefore methods that estimate rate constants with the maximum precision and accuracy are needed. We present here a new method for estimating rate coefficients from noisy observations of concentration levels at discrete time points. This is traditionally done by computing an error or cost function, that measures the distance between the behavior of the experimental data and the behavior of the model. However, estimation of the error function generally requires solving the reaction rate equations, which can become computationally unfeasible. We propose an alternative approach based on a probabilistic model of the variations in reactant concentration. Our method returns the rate coefficients, the level of noise and an error range on the estimates of rate constants. Its probabilistic formulation is key to a principled handling of the noise inherent in biological data, and it allows for a number of further extensions. We developed KInfer (Knowledge Inference), a freeware software tool that implements the new model of parameter inference (downloadable for free at http://www.cosbi.eu).

Biography: Paola Lecca received her B. S. in Theoretical Physics at the University of Trento (Italy) in 1997 and a PhD in Computer Science in 2006 at the International Doctorate School in Information and Communication Technologies of University of Trento. From 1997 to 2000 she was a research assistant by the Interactive Sensory System Division of ITC -Centre for Scientific and Technical Research of Trento. She worked by the group of Predictive Models for Biological and Environmental Data Analysis, where she dealt with the development of predictive models of archaeological sites in Trentino on the basis of historical and environmental information integrated and processes by GIS -Geographic Resources Analysis Support System. From 2001 to 2002 she obtained a scholarship at the Department of Physics of University of Trento in the area of data processing and modelling in Diamine and Explodet projects of the Italian National Institute of Nuclear Physics. She dealt with the development of new predictive processing methods to be applied to data obtained by neutrons radiation of the soil, for the detection of hidden explosives. In December 2006 Paola Lecca joined the Microsoft Research – University of Trento Centre for Computational and System Biology. Her current research interests in the areas of systems biology and computational cell biology include issues related to conceptual frameworks of stochasticity in modelling and simulating biochemical networks dynamics, model’s structure and model’s parameter inference for optimal experimental design, and the exploration of the expressiveness capabilities of process algebras formalisms for describing biological systems.

This talk is part of the CoSBi Computational and Systems Biology Series series.

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