Comparison of metabolic states using genome-scale metabolic models

Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.

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ComMet: A method for comparing metabolic states in genome-scale metabolic models

10.1101/2020.09.14.296145 ◽

2020 ◽

Author(s):

Chaitra Sarathy ◽

Marian Breuer ◽

Martina Kutmon ◽

Michiel E. Adriaens ◽

Chris T. Evelo ◽

...

Keyword(s):

Large Scale ◽

Metabolic Flux ◽

Knowledge Bases ◽

Human Models ◽

Comprehensive Knowledge ◽

Metabolic States ◽

Network Modules ◽

Metabolic Models ◽

Branched Chain ◽

Genome Scale

Being a comprehensive knowledge bases of cellular metabolism, Genome-scale metabolic models (GEMs) serve as mathematical tools for studying cellular flux states in various or-ganisms. However, analysis of large-scale GEMs, such as human models, still presents considerable challenges with respect to objective selection and reaction flux constraints. In this study, we introduce a model-based method, ComMet (Comparison of Metabolic states), for comprehensive analysis of large metabolic flux spaces and comparison of various metabolic states. ComMet allows (a) an in-depth characterisation of flux states achievable by GEMs, (b) comparison of flux spaces from several conditions of interest, (c) identification of metabolically distinct network modules and (d) visualisation of network modules as reaction and metabolic map. As a proof-of-principle, we employed ComMet to extract the biochemical differences in the human adipocyte network (iAdipocytes1809) arising due to unlimited/blocked uptake of branched-chain amino acids. Our study opens avenues for exploring several metabolic condi-tions of interest in both microbe and human models. ComMet is open-source and is available at https://github.com/macsbio/commet.

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Model-guided identification of novel gene amplification targets for improving succinate production in Escherichia coli NZN111

Integrative Biology ◽

10.1039/c7ib00077d ◽

2017 ◽

Vol 9 (10) ◽

pp. 830-835 ◽

Cited By ~ 2

Author(s):

Xingxing Jian ◽

Ningchuan Li ◽

Qian Chen ◽

Qiang Hua

Keyword(s):

Escherichia Coli ◽

Metabolic Engineering ◽

Gene Amplification ◽

Metabolic Networks ◽

Computational Analysis ◽

Succinate Production ◽

Novel Gene ◽

Metabolic Models ◽

Genome Scale

Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed.

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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data

PLoS Computational Biology ◽

10.1371/journal.pcbi.1006971 ◽

2019 ◽

Vol 15 (4) ◽

pp. e1006971 ◽

Cited By ~ 19

Author(s):

Jean-Christophe Lachance ◽

Colton J. Lloyd ◽

Jonathan M. Monk ◽

Laurence Yang ◽

Anand V. Sastry ◽

...

Keyword(s):

Experimental Data ◽

Objective Functions ◽

Metabolic Models ◽

Genome Scale

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Genome scale metabolic models and analysis for evaluating probiotic potentials

Biochemical Society Transactions ◽

10.1042/bst20190668 ◽

2020 ◽

Vol 48 (4) ◽

pp. 1309-1321

Author(s):

Yoon-Mi Choi ◽

Yi Qing Lee ◽

Hyun-Seob Song ◽

Dong-Yup Lee

Keyword(s):

In Silico ◽

Metabolic Characteristics ◽

Knowledge Based ◽

Main Challenge ◽

Metabolic States ◽

Probiotic Strains ◽

Metabolic Models ◽

Genome Scale

Probiotics are live beneficial microorganisms that can be consumed in the form of dairy and food products as well as dietary supplements to promote a healthy balance of gut bacteria in humans. Practically, the main challenge is to identify and select promising strains and formulate multi-strain probiotic blends with consistent efficacy which is highly dependent on individual dietary regimes, gut environments, and health conditions. Limitations of current in vivo and in vitro methods for testing probiotic strains can be overcome by in silico model guided systems biology approaches where genome scale metabolic models (GEMs) can be used to describe their cellular behaviors and metabolic states of probiotic strains under various gut environments. Here, we summarize currently available GEMs of microbial strains with probiotic potentials and propose a knowledge-based framework to evaluate metabolic capabilities on the basis of six probiotic criteria. They include metabolic characteristics, stability, safety, colonization, postbiotics, and interaction with the gut microbiome which can be assessed by in silico approaches. As such, the most suitable strains can be identified to design personalized multi-strain probiotics in the future.

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Modeling the Differences in Biochemical Capabilities ofPseudomonasSpecies by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences?

The Scientific World JOURNAL ◽

10.1155/2014/416289 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Parizad Babaei ◽

Tahereh Ghasemi-Kahrizsangi ◽

Sayed-Amir Marashi

Keyword(s):

In Silico ◽

Metabolic Networks ◽

Closely Related Species ◽

Systematic Analysis ◽

Reconstruction Process ◽

Flux Balance ◽

Wide Range ◽

Balance Analysis ◽

Metabolic Models ◽

Genome Scale

To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of threePseudomonasmetabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related toP. aeruginosaPAO1,P. putidaKT2440, andP. fluorescensSBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable forin silicosimulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare thein silicoresults to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.

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Suggestions for Standardized Identifiers for Fatty Acyl Compounds in Genome Scale Metabolic Models and Their Application to the WormJam Caenorhabditis elegans Model

Metabolites ◽

10.3390/metabo10040130 ◽

2020 ◽

Vol 10 (4) ◽

pp. 130

Author(s):

Michael Witting

Keyword(s):

Caenorhabditis Elegans ◽

Metabolic Networks ◽

Fatty Acid Biosynthesis ◽

Structural Information ◽

Current Knowledge ◽

Fatty Acyl ◽

Charge Balance ◽

Structural Annotation ◽

Metabolic Models ◽

Genome Scale

Genome scale metabolic models (GSMs) are a representation of the current knowledge on the metabolism of a given organism or superorganism. They group metabolites, genes, enzymes and reactions together to form a mathematical model and representation that can be used to analyze metabolic networks in silico or used for analysis of omics data. Beside correct mass and charge balance, correct structural annotation of metabolites represents an important factor for analysis of these metabolic networks. However, several metabolites in different GSMs have no or only partial structural information associated with them. Here, a new systematic nomenclature for acyl-based metabolites such as fatty acids, acyl-carnitines, acyl-coenzymes A or acyl-carrier proteins is presented. This nomenclature enables one to encode structural details in the metabolite identifiers and improves human readability of reactions. As proof of principle, it was applied to the fatty acid biosynthesis and degradation in the Caenorhabditis elegans consensus model WormJam.

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Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT

PLoS Computational Biology ◽

10.1371/journal.pcbi.1002518 ◽

2012 ◽

Vol 8 (5) ◽

pp. e1002518 ◽

Cited By ~ 256

Author(s):

Rasmus Agren ◽

Sergio Bordel ◽

Adil Mardinoglu ◽

Natapol Pornputtapong ◽

Intawat Nookaew ◽

...

Keyword(s):

Human Cell ◽

Metabolic Networks ◽

Cell Types ◽

Cancer Types ◽

Genome Scale

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Cellular determinants of metabolite concentration ranges

10.1101/439232 ◽

2018 ◽

Author(s):

Jeanne M. O. Eloundou-Mbebi ◽

Anika Küken ◽

Georg Basler ◽

Zoran Nikoloski

Keyword(s):

Escherichia Coli ◽

Large Scale ◽

Metabolic Networks ◽

Reaction Rates ◽

Mass Action ◽

Cellular Mechanisms ◽

Real World Data ◽

A Genome ◽

Metabolic Models ◽

Genome Scale

AbstractCellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component’s concentration resides in a given range remain elusive. We present network properties which suffice to identify components whose concentration ranges can be efficiently computed in mass-action metabolic networks. We show that the derived ranges are in excellent agreement with simulations from a detailed kinetic metabolic model of Escherichia coli. We demonstrate that the approach can be used with genome-scale metabolic models to arrive at predictions concordant with measurements from Escherichia coli under different growth scenarios. By application to 14 genome-scale metabolic models from diverse species, our approach specifies the cellular determinants of concentration ranges that can be effectively employed to make predictions for a variety of biotechnological and medical applications.Author SummaryWe present a computational approach for inferring concentration ranges from genome-scale metabolic models. The approach specifies a determinant and molecular mechanism underling facile control of concentration ranges for components in large-scale cellular networks. Most importantly, the predictions about concentration ranges do not require knowledge of kinetic parameters (which are difficult to specify at a genome scale), provided measurements of concentrations in a reference state. The approach assumes that reaction rates follow the mass action law used in the derivations of other types of kinetics. We apply the approach with large-scale kinetic and stoichiometric metabolic models of organisms from different kingdoms of life to show that we can identify a proportion of metabolites to which our approach is applicable. By challenging the predictions of concentration ranges in the genome-scale metabolic network of E. coli with real-world data sets, we further demonstrate the prediction power and limitations of the approach.

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A Systematic Assessment Of Current Genome-Scale Metabolic Reconstruction Tools

10.1101/558411 ◽

2019 ◽

Cited By ~ 3

Author(s):

S. N. Mendoza ◽

B. G Olivier ◽

D Molenaar ◽

B Teusink

Keyword(s):

Software Quality ◽

Metabolic Networks ◽

Metabolic Reconstruction ◽

Human Pathogens ◽

High Quality ◽

Systematic Assessment ◽

Reconstruction Software ◽

Software Platforms ◽

Metabolic Models ◽

Genome Scale

AbstractSeveral 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.

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Reconstruction of consensus tissue-specific metabolic models

10.1101/327262 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sara Correia ◽

Bruno Costa ◽

Miguel Rocha

Keyword(s):

Cell Line ◽

Case Studies ◽

Cancer Metabolism ◽

Cell Types ◽

Tissue Cell ◽

Tissue Specific ◽

Metabolic Phenotypes ◽

Distinct Cell ◽

Metabolic Models ◽

Genome Scale

AbstractGenome-Scale Metabolic Models have shown promising results in biomedical applications, such as understanding cancer metabolism and drug discovery. However, to take full advantage of these models there is the need to address the representation and simulation of the metabolic phenotypes of distinct cell types. With this aim, several algorithms have been recently proposed to reconstruct tissue-specific metabolic models based on available data. Here, the most promising were implemented and used to reconstruct models for two case studies, using omics data from distinct sources. The set of obtained models were compared and analyzed, being shown they are highly variable and that no combination of algorithm and data source can achieve models with acceptable phenotype predictions. We propose an algorithm to achieve a consensus model from the set of models available for a given tissue/cell line, and to improve it given functional data (e.g. known metabolic tasks). The results show that the resulting models are more accurate, both considering the prediction of known metabolic phenotypes and of experimental data not used in the model construction. Two case studies used for model validation consider healthy hepatocytes and a glioblastoma cell line. The open-source implementation of the algorithms is provided, together with the models built, in a software container, allowing full reproducibility, and representing by itself a contribution for the community.

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