Determination of optimal metabolic pathways through a new learning algorithm

Abstract

In the present article, we introduce a new method for identification of metabolic pathways in constraint based models that consider enzyme and substrate concentrations. It generates data on reaction fluxes based on biomass conservation constraint and then a set of constraints is formulated incorporating weighting coefficients corresponding to concentration of enzymes catalyzing reactions in the pathway. Finally, the rate of yield of the target metabolite, starting with a given substrate, is maximized in order to identify an optimal pathway through these weighting coefficients. In an attempt to solve this problem, we have developed a learning technique that optimizes a given objective function to find the optimal pathways. Finally, we propose a modification of the Newton Raphson method and incorporate it to our proposed methodology, which yields more relevant results from the perspective of biology.

An Optimization Rule for In Silico Identification of Targeted Overproduction in Metabolic Pathways

“…To demonstrate the benefits of the new second-order learning algorithm, we benchmark it on some of the metabolic networks used in the previous study [9] and on some other networks. The networks considered are a synthetic network (see Fig.…”

“…All reversible reactions are considered as two internal fluxes occurring in opposite directions. The theoretical formulation of the second-order optimization problem has been given here extensively in Section 3.2.4 and in Section 3.1, which was not considered in the previous work in [9].…”

“…We have recently presented a supervised second-order learning algorithm, a modification of the Newton-Raphson method that identifies some optimal metabolic pathways accurately and efficiently leading to the overproduction of a biochemical product of interest [9]. The learning method can be considered as a nonlinear global optimization problem in which the goal is to minimize a nonlinear objective function that involves the weights using heuristic strategies [10].…”