Algorithmic approaches for computing elementary modes in large biochemical reaction networks.
ABSTRACT The concept of elementary (flux) modes provides a rigorous description of pathways in metabolic networks and proved to be valuable in a number of applications. However, the computation of elementary modes is a hard computational task that gave rise to several variants of algorithms during the last years. This work brings substantial progresses to this issue. The authors start with a brief review of results obtained from previous work regarding (a) a unified framework for elementary-mode computation, (b) network compression and redundancy removal and (c) the binary approach by which elementary modes are determined as binary patterns reducing the memory demand drastically without loss of speed. Then the authors will address herein further issues. First, a new way to perform the elementarity tests required during the computation of elementary modes which empirically improves significantly the computation time in large networks is proposed. Second, a method to compute only those elementary modes where certain reactions are involved is derived. Relying on this method, a promising approach for computing EMs in a completely distributed manner by decomposing the full problem in arbitrarity many sub-tasks is presented. The new methods have been implemented in the freely available software tools FluxAnalyzer and Metatool and benchmark tests in realistic networks emphasise the potential of our proposed algorithms.
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ABSTRACT: This tutorial paper is concerned with the design of macroscopic bioreaction models on the basis a quantitative analysis of the underlying cell metabolism. The paper starts with a review of two fundamental algebraic techniques for the quantitative analysis of metabolic networks : (i) the decomposition of complex metabolic networks into elementary pathways (or elementary modes), (ii) the metabolic flux analysis which aims at computing the entire intracellular flux distribution from a limited number of flux measurements. Then it is discussed how these two fundamental techniques can be exploited to design minimal bioreaction models by using a systematic model reduction approach that automatically produces a family of equivalent minimal models which are fully compatible with the underlying metabolism and consistent with the available experimental data. The theory is illustrated with an experimental case-study on CHO cells.01/2008;
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ABSTRACT: We give a concise development of some of the major algebraic properties of extreme pathways (pathways that cannot be the result of combining other pathways) of metabolic networks, contrasting them to those of elementary flux modes (pathways involving a minimal set of reactions). In particular, we show that an extreme pathway can be recognized by a rank test as simple as the existing rank test for elementary flux modes, without computing all the modes. We make the observation that, unlike elementary flux modes, the property of being an extreme pathway depends on the presence or absence of reactions beyond those involved in the pathway itself. Hence, the property of being an extreme pathway is not a local property. As a consequence, we find that the set of all elementary flux modes for a network includes all the elementary flux modes for all its subnetworks, but that this property does not hold for the set of all extreme pathways.Journal of computational biology: a journal of computational molecular cell biology 02/2010; 17(2):107-19. · 1.69 Impact Factor
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ABSTRACT: Identifying multiple enzyme targets for metabolic engineering is very critical for redirecting cellular metabolism to achieve desirable phenotypes, e.g., overproduction of a target chemical. The challenge is to determine which enzymes and how much of these enzymes should be manipulated by adding, deleting, under-, and/or over-expressing associated genes. In this study, we report the development of a systematic multiple enzyme targeting method (SMET), to rationally design optimal strains for target chemical overproduction. The SMET method combines both elementary mode analysis and ensemble metabolic modeling to derive SMET metrics including l-values and c-values that can identify rate-limiting reaction steps and suggest which enzymes and how much of these enzymes to manipulate to enhance product yields, titers, and productivities. We illustrated, tested, and validated the SMET method by analyzing two networks, a simple network for concept demonstration and an Escherichia coli metabolic network for aromatic amino acid overproduction. The SMET method could systematically predict simultaneous multiple enzyme targets and their optimized expression levels, consistent with experimental data from the literature, without performing an iterative sequence of single-enzyme perturbation. The SMET method was much more efficient and effective than single-enzyme perturbation in terms of computation time and finding improved solutions.Biotechnology Journal 04/2013; · 3.71 Impact Factor