Systematic evaluation of objective functions for predicting intracellular fluxes in
            <i>Escherichia coli</i>

Schuetz, Robert D.; Kuepfer, Lars; Sauer, Uwe

doi:10.1038/msb4100162

Cited by 655 publications

(688 citation statements)

References 78 publications

(126 reference statements)

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“…Szekely et al 26 calculate the Pareto front of biological homeostasis circuits in the space of employed parameters. Moreover, Schuetz et al 27 used a metabolic model to provide flux estimates 28 , and proposed that Escherichia coli has evolved towards optimal flux distributions in one condition while minimizing the changes required between conditions.…”

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confidence: 99%

Metabolic efficiency underpins performance trade-offs in growth of Arabidopsis thaliana

et al. 2014

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Growth often involves a trade-off between the performance of contending tasks; metabolic plasticity can play an important role. Here we grow 97 Arabidopsis thaliana accessions in three conditions with a differing supply of carbon and nitrogen and identify a trade-off between two tasks required for rosette growth: increasing the physical size and increasing the protein concentration. We employ the Pareto performance frontier concept to rank accessions based on their multitask performance; only a few accessions achieve a good trade-off under all three growth conditions. We determine metabolic efficiency in each accession and condition by using metabolite levels and activities of enzymes involved in growth and protein synthesis. We demonstrate that accessions with high metabolic efficiency lie closer to the performance frontier and show increased metabolic plasticity. We illustrate how public domain data can be used to search for additional contending tasks, which may underlie the sub-optimality in some accessions. L iving organisms perform many different and sometimes contending tasks, leading to trade-offs in which optimal performance of one task comes at the cost of a sub-optimal performance of another task. Trade-offs are influenced by the environment and their resolution depends on metabolic resources and plasticity 1,2 . One important environmental variable affecting plant growth is resource availability 3-6 . We first ask whether there is a trade-off between two tasks during vegetative growth of Arabidopsis thaliana: the maximization of physical size and the maximization of protein concentration. We then apply two methods to rank accessions: the first based solely on the performance of tasks, and the second based on the efficiency with which metabolic resources are deployed to perform tasks. In addition, we investigate whether the trade-off and the ranking of accessions are affected by the availability of carbon (C) and nitrogen (N) and the way in which it shapes the accession-specific metabolic profiles.Plants use light energy to transform CO 2 and inorganic nutrients into a plethora of metabolic precursors that are used to drive growth. As plants are sessile, resource acquisition is constrained by their physical size. In land plants, the majority of a mature cell is occupied by the vacuole, allowing the generation of a much larger physical size per unit protein than is usually obtained by microbes or animals 7 . For simplification, we do not consider the impact of shape and phenology on the relation between physical size and resource acquisition. On the other hand, proteins are required to catalyse the transformation of resources into biomass. In particular, the majority of the protein in a plant leaf is involved in photosynthesis [8][9][10] . We hypothesize that there is a trade-off between physical size and protein concentration. While production of leaves with a higher protein concentration will allow higher rates of photosynthesis and metabolism per unit biomass, it also increases the costs of growth a...

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confidence: 99%

Metabolic efficiency underpins performance trade-offs in growth of Arabidopsis thaliana

et al. 2014

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“…For example, systematic evaluation of a diverse range of objective functions, including, maximization of biomass yield, maximization of ATP yield, minimization of the overall intracellular flux, maximization of ATP yield per flux unit, maximization of biomass yield per flux unit, minimization of glucose consumption rate, minimization of the redox potential, minimization of ATP producing fluxes, maximization of ATP producing fluxes, and minimization of reaction steps, when compared to experimental in vivo 13 Cdetermined fluxes can provide insight to which optimization function best represents the metabolic network (Schuetz et al, 2007). Yet another example employed a bi-level programming framework for identification of optimal gene deletions resulting in overproduction of a desired product by stoichiometrically including a drain towards biomass formation, thereby coupling production and biomass formation.…”

Section: Discussionmentioning

confidence: 99%

Industrial systems biology

Otero

Nielsen

2009

Biotech & Bioengineering

132

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ABSTRACT:The chemical industry is currently undergoing a dramatic change driven by demand for developing more sustainable processes for the production of fuels, chemicals, and materials. In biotechnological processes different microorganisms can be exploited, and the large diversity of metabolic reactions represents a rich repository for the design of chemical conversion processes that lead to efficient production of desirable products. However, often microorganisms that produce a desirable product, either naturally or because they have been engineered through insertion of heterologous pathways, have low yields and productivities, and in order to establish an economically viable process it is necessary to improve the performance of the microorganism. Here metabolic engineering is the enabling technology. Through metabolic engineering the metabolic landscape of the microorganism is engineered such that there is an efficient conversion of the raw material, typically glucose, to the product of interest. This process may involve both insertion of new enzymes activities, deletion of existing enzyme activities, but often also deregulation of existing regulatory structures operating in the cell. In order to rapidly identify the optimal metabolic engineering strategy the industry is to an increasing extent looking into the use of tools from systems biology. This involves both x-ome technologies such as transcriptome, proteome, metabolome, and fluxome analysis, and advanced mathematical modeling tools such as genome-scale metabolic modeling. Here we look into the history of these different techniques and review how they find application in industrial biotechnology, which will lead to what we here define as industrial systems biology.

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“…It should be noted that these methods perform an assessment of the solution spaces associated with the mathematical representation of a reconstruction 2 ; these methods are categorized as unbiased and biased methods 3 . The latter category relies on an observer bias that is stated through an objective function (that is now beginning to be experimentally examined 83 ) and is utilized in most of the studies reviewed here use the general application of flux balance analysis (FBA) [84][85][86] . Each category of application is now detailed, with emphasis on the first three that have the greatest practical utility.…”

Section: Ask Not What You Can Do For a Reconstruction But What A Recmentioning

confidence: 99%

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

2008

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The number and scope of methods developed to interrogate and use metabolic network reconstructions has significantly expanded since the first review of the use of constraint-based analysis in Nature Biotechnology some 14 years ago. In particular, the Escherichia coli metabolic network reconstruction has reached the genome-scale and has been broadly adapted. Specifically, it has been used to address a broad spectrum of basic and practical applications, falling into five main categories: 1) metabolic engineering, 2) model-directed discovery, 3) interpretations of phenotypic screens, 4) analysis of network properties, and 5) studies of evolutionary processes. With these accomplishments in hand, the field is expected to move forward and seek to further, i) broaden the scope and content of network reconstructions, ii) develop new and novel in silico analysis tools, and iii) expand in adaptation to uses of proximal and distal causation in biology. Taken together, these efforts will solidify a mechanistic genotype-phenotype relationship for microbial metabolism.The availability of reconstructed metabolic networks for microorganisms has increased rapidly in recent years, and a growing number of research groups are reconstructing metabolic networks for organisms of interest 1 . A network reconstruction represents a highly curated set of primary biological information for a particular organism and thus can be considered a biochemically, genetically and genomically structured (BiGG) data base 1, 2 . A curated BiGG data base (de facto a knowledge base) can be converted into a mathematical format (i.e., an in silico model), and used to computationally assess phenotypic properties using a variety of computational methods 2,3 . Genome-scale reconstructions are thus, a key step in quantifying the genotype-phenotype relationship and can be used to 'bring genomes to life' 4 . The purpose of this review is to summarize and classify applications utilizing the E. coli reconstruction to answer a broad spectrum of biological questions. These studies provide both an up to date review of the applications of constraint-based analysis and a guide to similar applications for the growing number of organisms for which genome-scale reconstructions are becoming available. The Key Steps in the Formulation of Genome-scale Metabolic Network ModelsThe four key steps in the formulation and use of genome-scale models are illustrated in Fig. 1. Foundational to the process is the generation of global, or genome-scale, omics data. Omics data, along with legacy information (i.e., the 'bibliome') and small-scale detailed experiments, can be used to define the interactions amongst the biological components that are used to reconstruct organism-specific networks 1 . Network reconstruction is also an iterative, on-going process that continually integrates data in a formal fashion as it becomes available 5 . As a result, a current and well curated genome-scale network reconstruction is a common denominator for those studying systems biology of an or...

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Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli

Cited by 655 publications

References 78 publications

Metabolic efficiency underpins performance trade-offs in growth of Arabidopsis thaliana

Metabolic efficiency underpins performance trade-offs in growth of Arabidopsis thaliana

Industrial systems biology

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

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