A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models

Opdam, Sjoerd; Richelle, Anne; Kellman, Benjamin P.; Li, Sanzhong; Zielinski, Daniel C.; Lewis, Nathan E.

doi:10.1016/j.cels.2017.01.010

Cited by 199 publications

(307 citation statements)

References 64 publications

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“…The MBA algorithm [38] also uses MILP to generate context-specific models and relies on multiple tissue-specific molecular data. However, model accuracy levels have been shown to be similar for iMAT and MBA [41]. Additionally, iMAT ensured maximum addition of reactions corresponding to highly expressed genes, which were further expected to carry active metabolic flux.…”

Section: Building Metabolic Modelsmentioning

confidence: 98%

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Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma

et al. 2018

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Retinoblastoma (RB) is a childhood eye cancer. Currently, chemotherapy, local therapy, and enucleation are the main ways in which these tumors are managed. The present work is the first study that uses constraint‐based reconstruction and analysis approaches to identify and explain RB‐specific survival strategies, which are RB tumor specific. Importantly, our model‐specific secretion profile is also found in RB1‐depleted human retinal cells in vitro and suggests that novel biomarkers involved in lipid metabolism may be important. Finally, RB‐specific synthetic lethals have been predicted as lipid and nucleoside transport proteins that can aid in novel drug target development.

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Section: Building Metabolic Modelsmentioning

confidence: 98%

“…Additionally, if a gene is expressed across all the three healthy samples, it is assumed to be present in the combined model that represents the healthy case. However, model accuracy levels have been shown to be similar for iMAT and MBA [41]. A data matrix was constructed, using the above threshold.…”

Section: Building Metabolic Modelsmentioning

confidence: 99%

Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma

et al. 2018

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“…Not all reactions may be functional in a studied cell type, or under particular physiological conditions. This concern can be addressed to some extent by procedures for deriving organ-specific metabolic models [94]. Moreover, the metabolic state of a cell depends on the nutrients available in its environment, which are often poorly characterized.…”

Section: Discussionmentioning

confidence: 99%

In Silico Modeling of Metabolic State in Single Th17 Cells Reveals Novel Regulators of Inflammation and Autoimmunity

Wagner¹,

Wang

DeTomaso³

et al. 2020

Preprint

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Cellular metabolism, a key regulator of immune responses, is difficult to study with current technologies in individual cells Here, we present Compass, an algorithm to characterize the metabolic state of cells based on single-cell RNA-Seq and flux balance analysis. We applied Compass to associate metabolic states with functional variability (pathogenic potential) amongst Th17 cells and recovered a metabolic switch between glycolysis and fatty acid oxidation, akin to known differences between Th17 and Treg cells, as well as novel targets in amino-acid pathways, which we tested through targeted metabolic assays. Compass further predicted a particular glycolytic reaction (phosphoglycerate mutase -PGAM) that promotes an anti-inflammatory Th17 phenotype, contrary to the common understanding of glycolysis as pro-inflammatory. We demonstrate that PGAM inhibition leads non-pathogenic Th17 cells to adopt a pro-inflammatory transcriptome and induce autoimmunity in vivo. Compass is broadly applicable for characterizing metabolic states of cells and relating metabolic heterogeneity to other cellular phenotypes. / 27 RESULTS Compass -an algorithm for comprehensive characterization of single-cell metabolismWe reasoned that even though the mRNA expression of individual enzymes does not necessarily provide an accurate proxy for their metabolic activity, a global analysis the entire metabolic network (as enabled by RNA-Seq) in the context of a large sample set (as offered by single cell genomics) coupled with strict criteria for hypotheses testing, would provide an effective framework for predicting cellular metabolic status of the cell. This led us to develop the Compass algorithm, which integrates scRNA-Seq profiles with prior knowledge of the metabolic network to infer a metabolic state of the cell (Figure 1A).The metabolic network is encoded in a Genome-Scale Metabolic Model (GSMM) that includes reaction stoichiometry, biochemical constraints such as reaction irreversibility and nutrient availability, and gene-enzyme-reaction associations. Here, we use Recon2, which comprises of 7,440 reactions and 2,626 unique metabolites [41]. To explore the metabolic capabilities of each cell, Compass solves a series of constraint-based optimization problems (formalized as linear programs) that produce a set of numeric scores, one per reaction (Methods). Intuitively, the score of each reaction in each cell reflects how well adjusted is the cell's overall transcriptome to maintaining high flux through that reaction. Henceforth, we refer to the scores as quantifying the "potential activity" of a metabolic reaction (or "activity" in short when it is clear from the context that Compass predictions are discussed).Compass belongs to the family of Flux Balance Analysis (FBA) algorithms that model metabolic fluxes, namely the rate by which chemical reactions convert substrates to products and apply constrained optimization methods to find flux distributions that satisfy desired properties (a flux distribution is an assignment of flux value to ever...

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“…Systematic assessments of tools for systems biology have become very popular [63,64] due to the great impact they have in the community of potential users who certainly are searching the best tool to apply in their research. Knowing the strengths and limitations of each tool allow users to select the best tool(s) for their case, to save time in preliminary tests and to focus more on the analysis and modelling using those reconstructions.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Assessment Of Current Genome-Scale Metabolic Reconstruction Tools

Mendoza

Olivier

Molenaar

et al. 2019

Preprint

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Several genome-scale metabolic reconstruction software platforms have been developed and are being continuously updated. These tools have been widely applied to reconstruct metabolic models for hundreds of microorganisms ranging from important human pathogens to species of industrial relevance. However, these platforms, as yet, have not been systematically evaluated with respect to software quality, best potential uses and intrinsic capacity to generate high-quality, genome-scale metabolic models. It is therefore unclear for potential users which tool best fits the purpose of their research. In this work, we performed a systematic assessment of the current genome-scale reconstruction software platforms. To meet our goal, we first defined a list of features for assessing software quality related to genome-scale reconstruction, which we expect to be useful for the potential users of these tools. Subsequently, we used the feature list to evaluate the performance of each tool. In order to assess the similarity of the draft reconstructions to high-quality models, we compared each tool's output networks with that of the high-quality, manually curated, models of Lactobacillus plantarum and Bordetella pertussis, representatives of gram-positive and gram-negative bacteria, respectively. We showed that none of the tools outperforms the others in all the defined features and that model builders should carefully choose a tool (or combinations of tools) depending on the intended use of the metabolic model. Author SummaryMetabolic networks that comprise biochemical reactions at genome-scale have become very useful to study and predict the phenotype of important microorganisms. Several software platforms exist to build these metabolic networks. Based on different approaches and utilizing a variety of databases it is, unfortunately, unclear what are the best scenarios to use each of these tools. Hence, to understand the potential uses of these tools, we created a list of relevant features for metabolic reconstruction and we evaluated the tools in all these categories. Here, we show that none of the tools is better than the other in all the evaluated categories; instead, each tool is more suitable for particular purposes. Therefore, users should carefully select the tool(s) that best fit the purpose of their research. This is the first time these tools are systematically evaluated and this overview can be used as a guide for selecting the correct tool(s) for each case. Current state of genome-scale reconstruction toolsHere we provide a brief description of the current reconstruction tools (see also S1 Table).AutoKEGGRec (2018). AutoKEEGRec [13] is an easy-to-use automated tool that uses the KEGG databases to create draft genome-scale models for any microorganism in that database. It runs in MATLAB and is compatible with COBRA Toolbox v3 [14]. One of the advantages of this tool is that multiple queries (microorganisms) can be processed in one run making it appropriate for cases where several microorganisms need to be reconstructe...

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A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models

Cited by 199 publications

References 64 publications

Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma

Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma

In Silico Modeling of Metabolic State in Single Th17 Cells Reveals Novel Regulators of Inflammation and Autoimmunity

A Systematic Assessment Of Current Genome-Scale Metabolic Reconstruction Tools

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