Optimization approaches for the analysis of metabolic networks

Title: Optimization approaches for the analysis of metabolic networks


Supervisors: Richard Notebaart and Bas Teusink

Metabolic networks have been reconstructed for several species, including two different lactic-acid bacteria [1]. These bacteria have health promoting properties and play an important role in food-fermentations. Current metabolic engineering studies to analyze metabolism are focused on mathematical modeling of networks by optimization approaches. Recently different approaches have been developed [2-4]. The aim of this project is to implement several existing approaches, which allows us to study the metabolic properties of lactic acid bacteria on both the genomic and metabolic level. The optimization approaches can give insights into functional and physiological coupling between metabolic genes, which is a measure of how important two or more genes (i.e. as being together) are within a metabolic network. The next step is to compare the predicted genes sets with information from functional genomics data, such as gene-expression. For instance, to what extent are the genes within metabolic gene sets transcriptionally regulated? This is one example of genomic data analysis using integrative bioinformatics.


1.         Teusink B, van Enckevort FH, Francke C, Wiersma A, Wegkamp A, Smid EJ, Siezen RJ: In Silico Reconstruction of the Metabolic Pathways of Lactobacillus plantarum: Comparing Predictions of Nutrient Requirements with Those from Growth Experiments. Appl Environ Microbiol 2005, 71(11):7253-7262.
2.         Teusink B, Smid EJ: Modelling strategies for the industrial exploitation of lactic acid bacteria. Nat Rev Microbiol 2006, 4(1):46-56.
3.         Burgard AP, Nikolaev EV, Schilling CH, Maranas CD: Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res 2004, 14(2):301-312.
4.         Price ND, Schellenberger J, Palsson BO: Uniform sampling of steady-state flux spaces: means to design experiments and to interpret enzymopathies. Biophys J 2004, 87(4):2172-2186.