Infoscience

Presentation / Talk

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

Information about kinetics and enzyme activities while essential for quantitative understanding of metabolic dynamics remains rarely available and always involves uncertainty. In this work, we introduce the first computational methodology capable of determining the important enzymes in the network along with the operating ranges of their saturation by substrates and products and their parameters that correspond to a given metabolic flux and a given metabolite concentration. The proposed approach is based on the ORACLE[1] (Optimization and Risk Analysis of Complex Living Entities) framework and machine learning methods and it offers information about enzymes that supplements the one obtained by experimental techniques. Though we initially determine kinetic parameters that involve a high degree of uncertainty, through the use of kinetic modeling and machine learning principles we are able to obtain more accurate ranges of kinetic parameters, and hence we are able to reduce the uncertainty in the model analysis. We computed the distribution of kinetic parameters for glucose-fed E. coli producing 1,4-butanediol and we discovered that the observed physiological state corresponds to a narrow range of kinetic parameters of only a few enzymes, whereas the kinetic parameters of other enzymes can vary widely. Furthermore, this analysis suggests which are the enzymes that should be manipulated in order to engineer the reference state of the cell in a desired way. The proposed method can be considered also as a new parameter estimation procedure since it can identify enzymes whose saturations, if constrained to a narrow range, allow us to build the kinetic models capable to describe the studied physiology, and by this mean to provide accurate estimates of ranges of kinetic parameters relevant for the studied physiology. Furthermore, the proposed approach sets up the foundations of a novel type of approaches for efficient, non-asymptotic, uniform sampling of solution spaces.

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