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Graphical Abstract Recent results obtained by Chindelevitch et al (2014) are challenged by Ebrahim et al, illustrating several issues in the reproducibility of computational biology methods.

In this study, we modify and extend the bilevel optimization framework OptKnock for identifying gene knockout strategies in the Escherichia coli metabolic network, leading to the overproduction of representative amino acids and key precursors for all five families. These strategies span not only the central metabolic network genes but also the amino acid biosynthetic and degradation pathways. In addition to gene deletions, the transport rates of carbon dioxide, ammonia, and oxygen, as well as the secretion pathways for key metabolites, are introduced as optimization variables in the framework. Computational results demonstrate the importance of manipulating energy-producing/consuming pathways, controlling the uptake of nitrogen and oxygen, and blocking the secretion pathways of key competing metabolites. The identified pathway modifications include not only straightforward elimination of competing reactions but also a number of nonintuitive knockouts quite distant from the amino acid-producing pathways. Specifically, OptKnock suggests three reactions (i.e., pyruvate kinase, phosphotransacetylase, and ATPase) for deletion, in addition to the straightforward elimination of 2-ketoglutarate dehydrogenase, to generate a glutamate-overproducing mutant. Similarly, phosphofructokinase and ATPase are identified as promising knockout targets to complement the removal of pyruvate formate lyase and pyruvate dehydrogenase for enhancing the yield of alanine. Although OptKnock in its present form does not consider regulatory constraints, it does provide useful suggestions largely in agreement with existing practices and, more importantly, introduces a framework for incorporating additional modeling refinements as they become available. © 2003 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The advent of genome-scale models of metabolism has laid the foundation for the development of computational procedures for suggesting genetic manipulations that lead to overproduction. In this work, the computational OptKnock framework is introduced for suggesting gene deletion strategies leading to the overproduction of chemicals or biochemicals in E. coli. This is accomplished by ensuring that a drain towards growth resources (i.e., carbon, redox potential, and energy) must be accompanied, due to stoichiometry, by the production of a desired product. Computational results for gene deletions for succinate, lactate, and 1,3-propanediol (PDO) production are in good agreement with mutant strains published in the literature. While some of the suggested deletion strategies are straightforward and involve eliminating competing reaction pathways, many others suggest complex and nonintuitive mechanisms of compensating for the removed functionalities. Finally, the OptKnock procedure, by coupling biomass formation with chemical production, hints at a growth selection/adaptation system for indirectly evolving overproducing mutants. © 2003 Wiley Periodicals. Biotechnol Bioeng 84: 647–657, 2003. [ABSTRACT FROM AUTHOR]

An optimization-based framework is introduced for testing whether experimental flux data are consistent with different hypothesized objective functions. Specifically, we examine whether the maximization of a weighted combination of fluxes can explain a set of observed experimental data. Coefficients of importance (CoIs) are identified that quantify the fraction of the additive contribution of a given flux to a fitness (objective) function with an optimization that can explain the experimental flux data. A high CoI value implies that the experimental flux data are consistent with the hypothesis that the corresponding flux is maximized by the network, whereas a low value implies the converse. This framework (i.e., ObjFind) is applied to both an aerobic and anaerobic set of Escherichia coli flux data derived from isotopomer analysis. Results reveal that the CoIs for both growth conditions are strikingly similar, even though the flux distributions for the two cases are quite different, which is consistent with the presence of a single metabolic objective driving the flux distributions in both cases. Interestingly, the CoI associated with a biomass production flux, complete with energy and reducing power requirements, assumes a value 9 and 15 times higher than the next largest coefficient for the aerobic and anaerobic cases, respectively. © 2003 Wiley Periodicals, Inc. Biotechnol Bioeng 82: 670–677, 2003. [ABSTRACT FROM AUTHOR]