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Identification of metabolic engineering targets through analysis of optimal and...

Identification of optimal genetic manipulation strategies for redirecting substrate uptake towards a desired product is a challenging task owing to the complexity of metabolic networks, esp. in terms of large number of routes leading to the desired product. Algorithms that can exploit the whole range of optimal and suboptimal routes for product formation while respecting the biological objective of the cell are...

Random sampling of elementary flux modes in large-scale metabolic networks

Motivation: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway analysis. However, their application to large networks has been hampered by the combinatorial explosion in the number of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Results: Our algorithm is an adapta...

Identification of metabolic engineering targets through pathway analysis

Given the complexity of metabolic networks, identification of optimal metabolic intervention strategies for redirecting fluxes towards desired products is a challenging task. Several algorithms based on linear programming and pathway analysis have been proposed. However, there is still a lack of an algorithmic framework that exploits the range of optimal and suboptimal routes and the structural/regulatory prope...

Towards a biologically relevant description of phenotypes based on pathway anal...

In metabolic systems, the cellular network of reactions together with constraints on reversibility of enzymes determine the space of all possible steady-state phenotypes. In actuality, the cell does not invoke the large majority of those in given conditions. We propose a method in two steps to obtain a more precise description of cellular phenotypes through pathway analysis. The first step is based on a modifie...

OptFlux : an open-source software platform for in silico metabolic engineering

Background: Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other ex...

Metabolic network topology reveals transcriptional regulatory signatures of typ...

Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challeng...

OptFlux : a software for metabolic engineering

Heuristics for simulated annealing search of active sub-networks in bio-molecul...

Different kinds of ‘omics’ data for several organisms and bio-molecular interaction networks (e.g. reconstructed networks of biochemical reactions and protein-protein physical interactions) are becoming very common nowadays. These bio-molecular networks are being used as a platform to integrate genome-scale ‘omics’ datasets. Identification of sub-networks in these large networks that show maximum collective res...

Natural computation meta-heuristics for the in silico optimization of microbial...

Background: One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack o...

An improved evolutionary algorithm-based framework for identifying in silico me...

In metabolic engineering problems, due to the complexity of metabolic networks, it is often difficult to identify a priori which genetic manipulations will originate a given desired phenotype. Genome-scale metabolic models, available for several microorganisms, can be used to simulate the metabolic phenotype and therefore help the tasks of metabolic engineering. This simulation can be performed by calculating t...