Identification of enzyme kinetics from dynamic data in large-scale metabolic networks

Project A. Abate (Alessandro)
Keywords:Systems biology, Discrete-event systems, Hybrid and nonlinear systems

Metabolic flux distributions are a result of genetic, transcriptional, and enzyme kinetic mechanisms. The identification of steady-state fluxes is a state of the art technique and has been improved significantly using 13C labeling techniques [1]. The next crucial step for a broader understanding of metabolism is to identify in-vivo kinetics [2]. This step faces three main challenges: the model for the evaluation contains highly nonlinear functions, the available data set contains only a few observations of noisy large-dimensional data, and the data set is extracted from experiments with limited dynamical range.

This work pursues a novel approach based on a decomposition of the problem over the time- and the concentration-dependent domain. We resort to a class of dynamical models known as piecewise affine (PWA), a subclass of the hybrid systems framework, which is widely investigated, recently used in systems biology studies, and prone to formal mathematical analysis [3].

The approach is scalable and is to be applied to larger, realistic metabolic networks. An example from Penicillium chrysogenum (see figure) is under study and leverages data provided by biologists. The project is in collaboration with dr. A. Wahl from the Bioprocess Technology group (Prof. Sef Heijnen) in the Department of Biotechnology.


[1] Wiechert, W., Möllney, M., Petersen, S., and de Graaf, A. A., “A universal framework for C-13 metabolic flux analysis”, Metabolic Engineering, vol. 3, no. 3, p. 265-283, 2001.

[2] Wahl, S.A., Nöh, K., and Wiechert, W., “13C labeling experiments at metabolic nonstationary conditions: An exploratory study”, BMC Bioinformatics, vol. 9, no. 1, p. 152, 2008.

[3] de Jong, H., “Modeling and simulation of genetic regulatory Systems: A literature review”, Journal of Computational Biology, vol. 9, no. 1, p. 67-103, 2002.

© Copyright Delft Center for Systems and Control, Delft University of Technology, 2017.