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 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 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 A systematic simulation of the effect of salicylic acid on sphingolipid metabolism

2014 ◽  
Author(s):  
Chao Shi ◽  
Jian Yin ◽  
Zhe Liu ◽  
Jian-Xin Wu ◽  
Qi Zhao ◽  
...  
Keyword(s):  
Salicylic Acid ◽  
Turnover Rate ◽  
Metabolic Flux ◽  
Defense Responses ◽  
Sphingolipid Metabolism ◽  
Short Time Scale ◽  
Flux Analysis ◽  
Cell Simulation ◽  
Balance Analysis ◽  
Short Time

The phytohormone salicylic acid (SA) affects plant development and defense responses. Recent studies revealed that SA is also involved in the regulation of sphingolipid metabolism, but the details of this regulation remain to be explored. Here, we use in silico Flux Balance Analysis (FBA) with published microarray data to construct a whole-cell simulation model, including 23 pathways, 259 reactions and 172 metabolites, to predict the alterations in flux of major sphingolipid species after treatment with exogenous SA. This model predicts significant changes in fluxes of certain sphingolipid species after SA treatment, changes that likely trigger downstream physiological and phenotypic effects. To validate the simulation, we used isotopic non-stationary metabolic flux analysis to measure sphingolipid contents and turnover rate in Arabidopsis thaliana seedlings treated with SA or the SA analog benzothiadiazole (BTH). The results show that both SA and BTH affect sphingolipid metabolism by not only concentration of certain species, but also the optimal flux distribution and turnover rate of sphingolipid contents. Our strategy allows us to formally estimate sphingolipid fluxes on a short time scale and gives us a systemic view of the effect of SA on sphingolipid homeostasis.

scholarly journals Flux-dependent graphs for metabolic networks

2018 ◽  
Author(s):  
Mariano Beguerisse-Díaz ◽  
Gabriel Bosque ◽  
Diego Oyarzún ◽  
Jesús Picóo ◽  
Mauricio Barahona
Keyword(s):  
Metabolic Networks ◽  
Metabolic Model ◽  
Central Carbon Metabolism ◽  
System Level ◽  
Metabolic Fluxes ◽  
Genetic Perturbations ◽  
Balance Analysis ◽  
Frame Work ◽  
Context Specific ◽  
Specific Reactions

Cells adapt their metabolic fluxes in response to changes in the environment. We present a frame-work for the systematic construction of flux-based graphs derived from organism-wide metabolic networks. Our graphs encode the directionality of metabolic fluxes via edges that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes probabilistically, or can be tailored to different environ-mental conditions by incorporating flux distributions computed through constraint-based approaches such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism ofEscherichia coliand on a metabolic model of human hepatocytes. The flux-dependent graphs under various environmental conditions and genetic perturbations exhibit systemic changes in their topo-logical and community structure, which capture the re-routing of metabolic fluxes and the varying importance of specific reactions and pathways. By integrating constraint-based models and tools from network science, our framework allows the study of context-specific metabolic responses at a system level beyond standard pathway descriptions.

scholarly journals Atom Identifiers Generated by a Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization across Metabolic Databases

Metabolites ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 368
Author(s):  
Huan Jin ◽  
Joshua M. Mitchell ◽  
Hunter N. B. Moseley
Keyword(s):  
Metabolic Network ◽  
Graph Coloring ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Enzyme Commission ◽  
Metabolic Model ◽  
Metabolic Reprogramming ◽  
Automatic Identification ◽  
Flux Analysis ◽  
Database Curation

Metabolic flux analysis requires both a reliable metabolic model and reliable metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a neighborhood-specific graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation.

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