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

Metabolites ◽  
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.

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

2017 ◽  
Vol 9 (10) ◽  
pp. 830-835 ◽  
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.

scholarly journals Comparison of metabolic states using genome-scale metabolic models

2021 ◽  
Vol 17 (11) ◽  
pp. e1009522
Author(s):  
Chaitra Sarathy ◽  
Marian Breuer ◽  
Martina Kutmon ◽  
Michiel E. Adriaens ◽  
Chris T. Evelo ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Biological Significance ◽  
Tca Cycle ◽  
Cell Types ◽  
Knowledge Bases ◽  
Objective Functions ◽  
Functional Differences ◽  
Metabolic States ◽  
Metabolic Models ◽  
Genome Scale

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.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
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.

scholarly journals Cellular determinants of metabolite concentration ranges

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.

scholarly journals A Systematic Assessment Of Current Genome-Scale Metabolic Reconstruction Tools

2019 ◽  
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.

The glycomes of Caenorhabditis elegans and other model organisms

2002 ◽  
Vol 69 ◽  
pp. 117-134 ◽  
Author(s):  
Stuart M. Haslam ◽  
David Gems ◽  
Howard R. Morris ◽  
Anne Dell
Keyword(s):  
Caenorhabditis Elegans ◽  
Current Knowledge ◽  
Structural Data ◽  
Model Organisms ◽  
Entire Genome ◽  
C Elegans ◽  
The Face ◽  
Area Of Concern ◽  
Nematode Worm

There is no doubt that the immense amount of information that is being generated by the initial sequencing and secondary interrogation of various genomes will change the face of glycobiological research. However, a major area of concern is that detailed structural knowledge of the ultimate products of genes that are identified as being involved in glycoconjugate biosynthesis is still limited. This is illustrated clearly by the nematode worm Caenorhabditis elegans, which was the first multicellular organism to have its entire genome sequenced. To date, only limited structural data on the glycosylated molecules of this organism have been reported. Our laboratory is addressing this problem by performing detailed MS structural characterization of the N-linked glycans of C. elegans; high-mannose structures dominate, with only minor amounts of complex-type structures. Novel, highly fucosylated truncated structures are also present which are difucosylated on the proximal N-acetylglucosamine of the chitobiose core as well as containing unusual Fucα1–2Gal1–2Man as peripheral structures. The implications of these results in terms of the identification of ligands for genomically predicted lectins and potential glycosyltransferases are discussed in this chapter. Current knowledge on the glycomes of other model organisms such as Dictyostelium discoideum, Saccharomyces cerevisiae and Drosophila melanogaster is also discussed briefly.

Learning from metabolic networks: current trends and future directions for precision medicine.

2020 ◽  
Vol 28 ◽  
Author(s):  
Ilaria Granata ◽  
Mario Manzo ◽  
Ari Kusumastuti ◽  
Mario R Guarracino
Keyword(s):  
Systems Biology ◽  
Precision Medicine ◽  
Metabolic Networks ◽  
Information Modeling ◽  
Model Systems ◽  
Specific Information ◽  
Important Indicator ◽  
Predictive Values ◽  
Metabolic Systems ◽  
Metabolic Models

Purpose: Systems biology and network modeling represent, nowadays, the hallmark approaches for the development of predictive and targeted-treatment based precision medicine. The study of health and disease as properties of the human body system allows the understanding of the genotype-phenotype relationship through the definition of molecular interactions and dependencies. In this scenario, metabolism plays a central role as its interactions are well characterized and it is considered an important indicator of the genotype-phenotype associations. In metabolic systems biology, the genome-scale metabolic models are the primary scaffolds to integrate multi-omics data as well as cell-, tissue-, condition-specific information. Modeling the metabolism has both investigative and predictive values. Several methods have been proposed to model systems, which involve steady-state or kinetic approaches, and to extract knowledge through machine and deep learning. Method: This review collects, analyzes, and compares the suitable data and computational approaches for the exploration of metabolic networks as tools for the development of precision medicine. To this extent, we organized it into three main sections: "Data and Databases", "Methods and Tools", and "Metabolic Networks for medicine". In the first one, we have collected the most used data and relative databases to build and annotate metabolic models. In the second section, we have reported the state-of-the-art methods and relative tools to reconstruct, simulate, and interpret metabolic systems. Finally, we have reported the most recent and innovative studies which exploited metabolic networks for the study of several pathological conditions, not only those directly related to the metabolism. Conclusion: We think that this review can be a guide to researchers of different disciplines, from computer science to biology and medicine, in exploring the power, challenges and future promises of the metabolism as predictor and target of the so-called P4 medicine (predictive, preventive, personalized and participatory).

scholarly journals Optimization of a modeling platform to predict oncogenes from genome‐scale metabolic networks of non‐small‐cell lung cancers

FEBS Open Bio ◽  
2021 ◽  
Author(s):  
You‐Tyun Wang ◽  
Min‐Ru Lin ◽  
Wei‐Chen Chen ◽  
Wu‐Hsiung Wu ◽  
Feng‐Sheng Wang
Keyword(s):  
Metabolic Networks ◽  
Small Cell ◽  
Small Cell Lung ◽  
Lung Cancers ◽  
Genome Scale

scholarly journals The Metano Modeling Toolbox MMTB: An Intuitive, Web-Based Toolbox Introduced by Two Use Cases

Metabolites ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 113
Author(s):  
Julia Koblitz ◽  
Sabine Will ◽  
S. Riemer ◽  
Thomas Ulas ◽  
Meina Neumann-Schaal ◽  
...  
Keyword(s):  
Metabolic Networks ◽  
Point Of View ◽  
Use Cases ◽  
Mathematical Methods ◽  
State Behavior ◽  
Web Based ◽  
Research Fields ◽  
Balance Analysis ◽  
Metabolic Models ◽  
User Friendly

Genome-scale metabolic models are of high interest in a number of different research fields. Flux balance analysis (FBA) and other mathematical methods allow the prediction of the steady-state behavior of metabolic networks under different environmental conditions. However, many existing applications for flux optimizations do not provide a metabolite-centric view on fluxes. Metano is a standalone, open-source toolbox for the analysis and refinement of metabolic models. While flux distributions in metabolic networks are predominantly analyzed from a reaction-centric point of view, the Metano methods of split-ratio analysis and metabolite flux minimization also allow a metabolite-centric view on flux distributions. In addition, we present MMTB (Metano Modeling Toolbox), a web-based toolbox for metabolic modeling including a user-friendly interface to Metano methods. MMTB assists during bottom-up construction of metabolic models by integrating reaction and enzymatic annotation data from different databases. Furthermore, MMTB is especially designed for non-experienced users by providing an intuitive interface to the most commonly used modeling methods and offering novel visualizations. Additionally, MMTB allows users to upload their models, which can in turn be explored and analyzed by the community. We introduce MMTB by two use cases, involving a published model of Corynebacterium glutamicum and a newly created model of Phaeobacter inhibens.

Systematic Investigation of Mouse Models of Parkinson’s Disease by Transcriptome Mapping on a Brain-Specific Genome-Scale Metabolic Network

2021 ◽  
Author(s):  
Ecehan Abdik ◽  
Tunahan Cakir
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Metabolic Network ◽  
Mouse Models ◽  
Mus Musculus ◽  
Metabolic Networks ◽  
Systematic Investigation ◽  
Omics Data ◽  
Metabolic Alterations ◽  
Genome Scale

Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model...

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