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

By Robert Schuetz, L. Kuepfer, Uwe Sauer. Molecular Systems Biology, 2007

752 citations recorded by the source corpus.

To which extent can optimality principles describe the operation of metabolic networks? By explicitly considering experimental errors and in silico alternate optima in flux balance analysis, we systematically evaluate the capacity of 11 objective functions combined with eight adjustable constraints to predict 13C‐determined in vivo fluxes in Escherichia coli under six environmental conditions. While no single objective describes the flux states under all conditions, we identified two sets of objectives for biologically meaningful predictions without the need for further, potentially artificial constraints. Unlimited growth on glucose in oxygen or nitrate respiring batch cultures is best described by nonlinear maximization of the ATP yield per flux unit. Under nutrient scarcity in continuous cultures, in contrast, linear maximization of the overall ATP or biomass yields achieved the highest predictive accuracy. Since these particular objectives predict the system behavior without preconditioning of the network structure, the identified optimality principles reflect, to some extent, the evolutionary selection of metabolic network regulation that realizes the various flux states.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/070aca20cfb01e5e111e187cf3175fe486ae3369

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Genome-scale models of bacterial metabolism: reconstruction and applications (2008, neutral): “coli depending on the environmental conditions (Schuetz et al., 2007).”
Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction (2013, neutral): “These studies concluded that no single objective function applied to M-Models can accurately represent fluxomic data from all environmental conditions studied (Schuetz et al, 2007).”
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, background): “Complementary COBRA methods also improve the definition of cellular objectives by integrating data to systematically assess 90–92 , predict 93 or modify objective functions 79,81,87 .”
Applications of genome-scale metabolic reconstructions (2009, background): “Some studies have explored this by examining whether cells follow nonbiomass objective functions under certain conditions (Schuetz et al, 2007; Gianchandani et al, 2008), but even these studies stop short of questioning the optimization premise itself.”
The Role of Soil Microorganisms in Plant Mineral Nutrition—Current Knowledge and Future Directions (2017, background): “However, as mentioned before, the simulation results are determined by the choice of an optimality principle, which is still an extremely subjective and approximate choice (Schuetz et al., 2007).”
A protocol for generating a high-quality genome-scale metabolic reconstruction (2010, neutral): “19| Add spontaneous reactions to the reconstruction.”
Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli (2008, methodology): “First, this model is based on an objective which maximizes the growth rate, and it has been shown that other objectives may be more accurate predictors of phenotype, depending on the growth conditions (Schuetz et al., 2007; Segre et al., 2002; Burgard and Maranas, 2003).”
Network-based prediction of human tissue-specific metabolism (2008, background): “More refined tissue-specific models could potentially be generated by identifying tissue-specific objective functions or measuring tissue-specific metabolite exchange patterns that could further limit the space of possible functional state”
Reconstruction of Biochemical Networks in Microbial Organisms (2008, background): “Aside from simulations to produce biomass constituents, additional gap-filling analyses can be performed to add missed pathways or remove those that have been incorrectly included from the automated reconstruction process, and additional cellular objective functions can be evaluated computationally ”
Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli (2013, methodology): “…various genetic (Fong and Palsson, 2004) and environmental (Ibarra et al , 2002) conditions, explore different objectives of microbial metabolism (Schuetz et al , 2007; Ow et al , 2009) to examine the driving force behind cellular function, and better understand the suboptimal behavior of cells…”
OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities (2012, background): “Studies similar to those conducted for mono-cultures that examine and compare various presumed hypotheses on cellular objective function [82,83,84,85,86,87] or algorithms that identify/test a relevant objective function using experimental flux data [88,89] are needed in the context of multi-species ”
Using Genome-Scale Models to Predict Biological Capabilities (2015, neutral): “Impressively, the biomass objective function can explain a large amount of the variability of ﬂuxes (Schuetz et al., 2007).”
Context-Specific Metabolic Networks Are Consistent with Experiments (2008, methodology): “Second, in the absence of a TRN, optimization procedures based on the assumption that the organism picks out the best set of reactions to meet a physiological objective have been used [7].”