MOTIVATION Elementary modes (EMs) and minimal cut sets (MCSs) provide important techniques for metabolic network modeling. Whereas EMs describe minimal subnetworks that can function in steady state, MCSs are sets of reactions whose removal will disable certain network functions. Effective algorithms were developed for EM computation while calculation of MCSs is typically addressed by indirect m...

The main purpose of metabolic engineering is the modification of biological systems towards specific goals using genetic manipulations. For this purpose, models are built that describe the stationary and dynamic behaviour of biochemical reaction networks inside a biological cell. Based on these models, simulations are carried out with the intention to understand the cell’s behaviour. The modeli...

Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP) based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. ...

Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among the metabolic profiles. However, these analyses cannot simultaneously carry out identification of m...

In our previous study, we showed that the branching process approximation is useful for estimating metabolic robustness, measured using the impact degree. By applying a theory of random family forests, we here extend the branching process approximation to consider the knockout of multiple reactions, inspired by the importance of multiple knockouts reported by recent computational and experiment...

The number of interactions per node in metabolic network graphs follows a power law. I discuss two possible evolutionary explanations of this feature. The first relates to the observation that graphs with this degree distribution are robust to random node removal. This observation has lead to the hypothesis that metabolic networks show power-law degree distributions because it endows them with ...

One of the most obvious phenotypes of a cell is its metabolic activity, which is defined by the fluxes in the metabolic network. Although experimental methods to determine intracellular fluxes are well established, only a limited number of fluxes can be resolved. Especially in eukaryotes such as yeast, compartmentalization and the existence of many parallel routes render exact flux analysis imp...