scholarly journals iMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis

2019 ◽  
Vol 20 (4) ◽  
pp. 252-259
Author(s):  
Zhitao Mao ◽  
Hongwu Ma
Keyword(s):  
Gene Expression ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Network Models ◽  
Reaction Rates ◽  
Marker Genes ◽  
Published Data ◽  
Flux Analysis ◽  
Prediction Errors ◽  
Balance Analysis

Background:Constraint-based metabolic network models have been widely used in phenotypic prediction and metabolic engineering design. In recent years, researchers have attempted to improve prediction accuracy by integrating regulatory information and multiple types of “omics” data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed.Methods and Results:We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction boundaries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods.Conclusion:Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise predictions of metabolic fluxes by using expression values normalized based on GO.

scholarly journals ReMatch: a web-based tool to construct, store and share stoichiometric metabolic models with carbon maps for metabolic flux analysis

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Esa Pitkänen ◽  
Arto Åkerlund ◽  
Ari Rantanen ◽  
Paula Jouhten ◽  
Esko Ukkonen
Keyword(s):  
Data Integration ◽  
Network Model ◽  
Metabolic Flux Analysis ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Network Models ◽  
Flux Analysis ◽  
Stoichiometric Matrix ◽  
Web Based ◽  
Integration Problem

Summary ReMatch is a web-based, user-friendly tool that constructs stoichiometric network models for metabolic flux analysis, integrating user-developed models into a database collected from several comprehensive metabolic data resources, including KEGG, MetaCyc and CheBI. Particularly, ReMatch augments the metabolic reactions of the model with carbon mappings to facilitateThe construction of a network model consisting of biochemical reactions is the first step in most metabolic modelling tasks. This model construction can be a tedious task as the required information is usually scattered to many separate databases whose interoperability is suboptimal, due to the heterogeneous naming conventions of metabolites in different databases. Another, particularly severe data integration problem is faced inReMatch has been developed to solve the above data integration problems. First, ReMatch matches the imported user-developed model against the internal ReMatch database while considering a comprehensive metabolite name thesaurus. This, together with wild card support, allows the user to specify the model quickly without having to look the names up manually. Second, ReMatch is able to augment reactions of the model with carbon mappings, obtained either from the internal database or given by the user with an easy-touse tool.The constructed models can be exported into 13C-FLUX and SBML file formats. Further, a stoichiometric matrix and visualizations of the network model can be generated. The constructed models of metabolic networks can be optionally made available to the other users of ReMatch. Thus, ReMatch provides a common repository for metabolic network models with carbon mappings for the needs of metabolic flux analysis community. ReMatch is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/rematch/.

Fluxes through plant metabolic networks: measurements, predictions, insights and challenges

2014 ◽  
Vol 465 (1) ◽  
pp. 27-38 ◽  
Author(s):  
Nicholas J. Kruger ◽  
R. George Ratcliffe
Keyword(s):  
Metabolic Networks ◽  
Metabolic Flux ◽  
Cell Function ◽  
Kinetic Modelling ◽  
Reducing Power ◽  
Central Carbon Metabolism ◽  
Flux Analysis ◽  
Stable Isotope Labelling ◽  
Balance Analysis ◽  
Power And Energy

Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

scholarly journals Quantification of microbial phenotypes using 13C-Fluxomics

2011 ◽  
Vol 32 (4) ◽  
pp. 163
Author(s):  
Jens O Kromer
Keyword(s):  
Metabolic Networks ◽  
Metabolic Flux ◽  
Driving Forces ◽  
Building Blocks ◽  
Reaction Rates ◽  
Metabolic Phenotype ◽  
Flux Analysis ◽  
Material Transfer ◽  
Active Set ◽  
Metabolite Pool

Systems biology is an emerging tool in microbiology that helps us to understand cellular processes and to optimise microbes for production purposes1. It strongly relies on the use of large datasets created using omics tools followed by data mining and modelling in order to gain new insights into biology. The creation of the datasets is usually comprised of genomics defining the overall capacity of a microbe, transcriptomics and proteomics as a measure of the active set of reactions within the overall capacity and more recently metabolomics as a measure of the available building blocks and (if performed quantitatively) of the thermodynamic driving forces governing the intracellular reactions. The latter can define feasibility of pathways as well as reaction reversibility, which can be important constraints for the analysis of metabolic networks. However, all these omics techniques fail to quantitatively assess the metabolic phenotype in its ultimate form: The reaction rates, or metabolic fluxes inside the cell that define the material transfer rates from one metabolite pool to another and from pathway to pathway. The ?omics technology that enables the quantification of fluxes is metabolic flux analysis, or fluxomics.

scholarly journals Bridging the Gap between Fluxomics and Industrial Biotechnology

2010 ◽  
Vol 2010 ◽  
pp. 1-13 ◽  
Author(s):  
Xueyang Feng ◽  
Lawrence Page ◽  
Jacob Rubens ◽  
Lauren Chircus ◽  
Peter Colletti ◽  
...  
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Regulation ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Enzymatic Reaction ◽  
Industrial Biotechnology ◽  
Flux Analysis ◽  
Potential Applications ◽  
Balance Analysis ◽  
Precise Prediction

Metabolic flux analysis is a vital tool used to determine the ultimate output of cellular metabolism and thus detect biotechnologically relevant bottlenecks in productivity.13C-based metabolic flux analysis (13C-MFA) and flux balance analysis (FBA) have many potential applications in biotechnology. However, noteworthy hurdles in fluxomics study are still present. First, several technical difficulties in both13C-MFA and FBA severely limit the scope of fluxomics findings and the applicability of obtained metabolic information. Second, the complexity of metabolic regulation poses a great challenge for precise prediction and analysis of metabolic networks, as there are gaps between fluxomics results and other omics studies. Third, despite identified metabolic bottlenecks or sources of host stress from product synthesis, it remains difficult to overcome inherent metabolic robustness or to efficiently import and express nonnative pathways. Fourth, product yields often decrease as the number of enzymatic steps increases. Such decrease in yield may not be caused by rate-limiting enzymes, but rather is accumulated through each enzymatic reaction. Fifth, a high-throughput fluxomics tool hasnot been developed for characterizing nonmodel microorganisms and maximizing their application in industrial biotechnology. Refining fluxomics tools and understanding these obstacles will improve our ability to engineer highlyefficient metabolic pathways in microbial hosts.

scholarly journals Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

2020 ◽  
Author(s):  
Claudio Tomi-Andrino ◽  
Rupert Norman ◽  
Thomas Millat ◽  
Philippe Soucaille ◽  
Klaus Winzer ◽  
...  
Keyword(s):  
Experimental Data ◽  
Metabolic Flux Analysis ◽  
In Silico ◽  
Physicochemical Parameters ◽  
Metabolic Flux ◽  
Living Systems ◽  
Flux Analysis ◽  
Flux Balance ◽  
Metabolic Fluxes ◽  
Balance Analysis

AbstractMetabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.Author summaryBiotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.

scholarly journals Molecular Basis for Anaerobic Growth of Saccharomyces cerevisiae on Xylose, Investigated by Global Gene Expression and Metabolic Flux Analysis

2004 ◽  
Vol 70 (4) ◽  
pp. 2307-2317 ◽  
Author(s):  
Marco Sonderegger ◽  
Marie Jeppsson ◽  
Bärbel Hahn-Hägerdal ◽  
Uwe Sauer
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Global Gene Expression ◽  
Anaerobic Growth ◽  
Flux Analysis ◽  
Atp Production ◽  
Redox Imbalance ◽  
Chemostat Cultures

ABSTRACT Yeast xylose metabolism is generally considered to be restricted to respirative conditions because the two-step oxidoreductase reactions from xylose to xylulose impose an anaerobic redox imbalance. We have recently developed, however, a Saccharomyces cerevisiae strain that is at present the only known yeast capable of anaerobic growth on xylose alone. Using transcriptome analysis of aerobic chemostat cultures grown on xylose-glucose mixtures and xylose alone, as well as a combination of global gene expression and metabolic flux analysis of anaerobic chemostat cultures grown on xylose-glucose mixtures, we identified the distinguishing characteristics of this unique phenotype. First, the transcript levels and metabolic fluxes throughout central carbon metabolism were significantly higher than those in the parent strain, and they were most pronounced in the xylose-specific, pentose phosphate, and glycerol pathways. Second, differential expression of many genes involved in redox metabolism indicates that increased cytosolic NADPH formation and NADH consumption enable a higher flux through the two-step oxidoreductase reaction of xylose to xylulose in the mutant. Redox balancing is apparently still a problem in this strain, since anaerobic growth on xylose could be improved further by providing acetoin as an external NADH sink. This improved growth was accompanied by an increased ATP production rate and was not accompanied by higher rates of xylose uptake or cytosolic NADPH production. We concluded that anaerobic growth of the yeast on xylose is ultimately limited by the rate of ATP production and not by the redox balance per se, although the redox imbalance, in turn, limits ATP production.

scholarly journals Bayesian metabolic flux analysis reveals intracellular flux couplings

2019 ◽  
Vol 35 (14) ◽  
pp. i548-i557 ◽  
Author(s):  
Markus Heinonen ◽  
Maria Osmala ◽  
Henrik Mannerström ◽  
Janne Wallenius ◽  
Samuel Kaski ◽  
...  
Keyword(s):  
Steady State ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Reaction Network ◽  
Reaction Rates ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Metabolic Reaction ◽  
Metabolic Systems ◽  
Genome Scale

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.

Molecular Classification of Multiple Myeloma: A Distinct Transcriptional Profile Characterizes Patients Expressing CCND1 and Negative for 14q32 Translocations

2005 ◽  
Vol 23 (29) ◽  
pp. 7296-7306 ◽  
Author(s):  
Luca Agnelli ◽  
Silvio Bicciato ◽  
Michela Mattioli ◽  
Sonia Fabris ◽  
Daniela Intini ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Gene Expression Pattern ◽  
Marker Genes ◽  
Published Data ◽  
Cyclin D ◽  
Chromosome 13Q ◽  
13Q Deletion

Purpose The deregulation of CCND1, CCND2 and CCND3 genes represents a common event in multiple myeloma (MM). A recently proposed classification grouped MM patients into five classes on the basis of their cyclin D expression profiles and the presence of the main translocations involving the immunoglobulin heavy chain locus (IGH) at 14q32. In this study, we provide a molecular characterization of the identified translocations/cyclins (TC) groups. Materials and Methods The gene expression profiles of purified plasma cells from 50 MM cases were used to stratify the samples into the five TC classes and identify their transcriptional fingerprints. The cyclin D expression data were validated by means of real-time quantitative polymerase chain reaction analysis; fluorescence in situ hybridization was used to investigate the cyclin D loci arrangements, and to detect the main IGH translocations and the chromosome 13q deletion. Results Class-prediction analysis identified 112 probe sets as characterizing the TC1, TC2, TC4 and TC5 groups, whereas the TC3 samples showed heterogeneous phenotypes and no marker genes. The TC2 group, which showed extra copies of the CCND1 locus and no IGH translocations or the chromosome 13q deletion, was characterized by the overexpression of genes involved in protein biosynthesis at the translational level. A meta-analysis of published data sets validated the identified gene expression signatures. Conclusion Our data contribute to the understanding of the molecular and biologic features of distinct MM subtypes. The identification of a distinctive gene expression pattern in TC2 patients may improve risk stratification and indicate novel therapeutic targets.

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