Estimating the size of the solution space of metabolic networks

Abstract

Background: Cellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA) has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium) the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations.

Flux Measurement Selection in Metabolic Networks

“…The authors of [15] show that the sampling approach can be used to find probability distributions for all fluxes, that it can be used to measure pairwise correlation coefficients and to compute the network wide effects of changes in flux variables. Braunstein et al [3] show an alternative approach to approximate the volume and shape of the convex polytope using a message-passing algorithm based on belief propagation. Almaas et al [1] have used random sampling on the E. coli network to show that there is a core set of reactions carrying a high flux, termed the 'high-flux backbone'.…”

“…This metabolic network is a constraint-based network based on the kinetic model from [8]. We have used the model that was available as supplementary material from [3]. 3.…”

Network models in the study of metabolism

“…This algorithm based on conjecture or Bethe approximation (Bethe Ansatz) allows calculating the volume of a convex polygon of incomplete higher-order dimension. Was successfully used in the characterization of a metabolic network of 46 reactions and 34 metabolites in blood and red blood cells in predicting the effect on the disruption of some genes of central metabolism (gene knock-out) in E. coli (Braunstein et al 2008). Also this approach was used iteratively in the human metabolic network to identify missing components.…”

“…An approach to finding the volume and shape of possible phenotypes or solutions for the optimization of biological traits under genetic, environmental or evolutionary restrictions, is performed by the group of Braunstein et al (2008) where they suggest that the best technique that allows this characterization is based on the method of Monte Carlo sampling (MCS) of the area of metabolic flux of the network under steady state, given the complexity of metabolic networks and the enormous computational cost involved in these calculations. They propose a computational strategy known as message-passing algorithm derived from the field of Statistical Physics and Information Theory.…”

Modelling and multiobjective optimization for simulation of cyanobacterial metabolism

“…From this point on, two main trends have arisen in the field. Some researches have focused on studying the properties of the constraint-defined solution spaces: topology, convex basis, extreme behaviours, or dependencies among fluxes (Papin et al, 2004;Braunstein et al, 2008;Llaneras and Picó, 2010), appear among the most popular techniques within this approach. On the other hand, in some other cases the interest has been put in finding concrete flux distributions to gain insights into particular behaviours and phenotypes.…”