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Results for “Metabolic Networks and Pathways”

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At least 127 records · Page 7Linked to original sources
PubMed2025

Phenotypic profiling of carbon utilization of Pectobacterium brasiliense (Pbr1692).

Pectobacterium brasiliense 1692 (Pbr1692) is a necrotrophic pathogen that infects many crops such as potatoes and ornamental plants and derives nutrients from degraded plant tissue. Previous studies have identified Pbr1692 genes required for ecological fitness and virulence, however there is a lack of information on nutrient utilization in Pbr1692. Carbon source utilization profiling in Pbr1692 could provide a platform to decipher its metabolic flexibility and adaptation. This study assessed the nutrient utilization of Pbr1692 in different carbon sources, using Biolog Phenotypic Microarray (PM). An array of carbon sources utilized by Pbr1692 were identified, 32 carbohydrates and 8 carboxylic acids were among the preferred carbon nutrients utilized by Pbr1692. The PM results also revealed that the citric acid cycle, amino acid metabolism, and pentose phosphate metabolic pathways might be used to produce energy for Pbr1692. In addition, growth of Pbr1692 cells in minimal medium supplemented with citric acid, glucose, and aspartic acid retained the typical rod shape, suggesting that nutrient variation did not influence Pbr1692 cell morphology adaptation. This study provides an understanding on the adaptation of Pbr1692 and lays a foundation for understanding carbon metabolism of Pbr1692.

Carbon↗
PubMed1998

The anaerobic degradation of L-serine and L-threonine in enterobacteria: networks of pathways and regulatory signals.

The mechanisms controlling the biosynthesis and degradation of l-serine and l-threonine are remarkably complex. Their metabolism forms a network of pathways linking several amino acids, central primary metabolites such as pyruvate, oxaloacetate and 3-phosphoglycerate, and C1 metabolism. Studies on the degradation of these amino acids in Escherichia coli have revealed the involvement of fascinating enzymes that utilise quite diverse catalytic mechanisms. Moreover, it is emerging that both environmental and metabolic signals have a major impact in controlling enzyme synthesis This is exemplified by the anaerobically regulated tdc operon, which encodes a metabolic pathway for the degradation of serine and threonine. Studies on this pathway are beginning to provide insights into how an organism adapts its genetic makeup to meet the physiological demands of the cell.

Anaerobiosis↗
PubMed2003

Metabolic pathways in the post-genome era.

Metabolic pathways are a central paradigm in biology. Historically, they have been defined on the basis of their step-by-step discovery. However, the genome-scale metabolic networks now being reconstructed from annotation of genome sequences demand new network-based definitions of pathways to facilitate analysis of their capabilities and functions, such as metabolic versatility and robustness, and optimal growth rates. This demand has led to the development of a new mathematically based analysis of complex, metabolic networks that enumerates all their unique pathways that take into account all requirements for cofactors and byproducts. Applications include the design of engineered biological systems, the generation of testable hypotheses regarding network structure and function, and the elucidation of properties that can not be described by simple descriptions of individual components (such as product yield, network robustness, correlated reactions and predictions of minimal media). Recently, these properties have also been studied in genome-scale networks. Thus, network-based pathways are emerging as an important paradigm for analysis of biological systems.

Biochemistry↗
PubMed2006

Systems analyses characterize integrated functions of biochemical networks.

Metabolic, regulatory and signaling pathways have been characterized in detail over the past century. As the amount of genomic, proteomic and metabolic data has increased, and the mathematical and analytical capabilities of interrogating these data have advanced, the overlapping roles of pathway constituents have been described. These developments reflect the truly integrated nature of subcellular biochemical networks. Systems analyses, including the reconstruction of stoichiometric networks, provide a key set of tools for quantifying overlap among the metabolic, regulatory and signaling functions of network components. Accounting for this integration is crucial for accurately describing the function of biochemical networks.

Biochemistry↗
PubMed2000

Pathway analysis in metabolic databases via differential metabolic display (DMD).

MOTIVATION: A number of metabolic databases are available electronically, some with features for querying and visualizing metabolic pathways and regulatory networks. We present a unifying, systematic approach based on PETRI nets for storing, displaying, comparing, searching and simulating such nets from a number of different sources. RESULTS: Information from each data source is extracted and compiled into a PETRI net. Such PETRI nets then allow to investigate the (differential) content in metabolic databases, to map and integrate genomic information and functional annotations, to compare sequence and metabolic databases with respect to their functional annotations, and to define, generate and search paths and pathways in nets. We present an algorithm to systematically generate all pathways satisfying additional constraints in such PETRI nets. Finally, based on the set of valid pathways, so-called differential metabolic displays (DMDs) are introduced to exhibit specific differences between biological systems, i.e. different developmental states, disease states, or different organisms, on the level of paths and pathways. DMDs will be useful for target finding and function prediction, especially in the context of the interpretation of expression data.

Algorithms↗
PubMed2004

Metabolic pathway analysis of yeast strengthens the bridge between transcriptomics and metabolic networks.

Central carbon metabolism of the yeast Saccharomyces cerevisiae was analyzed using metabolic pathway analysis tools. Elementary flux modes for three substrates (glucose, galactose, and ethanol) were determined using the catabolic reactions occurring in yeast. Resultant elementary modes were used for gene deletion phenotype analysis and for the analysis of robustness of the central metabolism and network functionality. Control-effective fluxes, determined by calculating the efficiency of each mode, were used for the prediction of transcript ratios of metabolic genes in different growth media (glucose-ethanol and galactose-ethanol). A high correlation was obtained between the theoretical and experimental expression levels of 38 genes when ethanol and glucose media were considered. Such analysis was shown to be a bridge between transcriptomics and fluxomics. Control-effective flux distribution was found to be promising in in silico predictions by incorporating functionality and regulation into the metabolic network structure.

Biomass↗
PubMed2005

Cyclic peptides, a chemical genetics tool for biologists.

The genomics era has presented scientists with an overwhelming amount of sequence information that encodes for a continuously expanding array of metabolic pathways and regulatory networks. In order to unravel this elaborate maze, effective approaches need to be pioneered, and cyclic peptides represent an exciting new tool that can aid this challenging task. There are considerable benefits to cyclic peptides including enhanced protein binding affinity and metabolic stability, but perhaps most important is the ability to biologically synthesize them using intein chemistry. This capacity for genetic encoding allows biologists access to diverse, intracellular small-molecule libraries that can be tailored for numerous chemical genetics approaches. An increasing number of molecular strategies are being developed to aid in the implementation of cyclic peptides in forward and reverse genetics experiments, which will provide researchers with a powerful toolbox to complement existing genomic methodologies.

Animals↗
PubMed2025

MiNEApy: enhancing enrichment network analysis in metabolic networks.

MOTIVATION: Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism. RESULTS: Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data. AVAILABILITY AND IMPLEMENTATION: MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.

Metabolic Networks and Pathways↗
PubMed2004

Current understanding of the regulation of methionine biosynthesis in plants.

Plants can provide most of the nutrients for the human diet. However, the major crops are often deficient in some of the nutrients. Thus, malnutrition, with respect to micronutrients such as vitamin A, iron, and zinc, but also macronutrients such as the essential amino acids lysine and methionine, affects more than 40% of the world's population. Recent advances in molecular biology, but also the grasp of biochemical pathways, metabolic fluxes, and networks can now be exploited to produce crops enhanced in key nutrients to increase the nutritional value of plant-derived foods and feeds. Some of the predictions appear to be accurate, while others not, reflecting the fact that plant metabolism is more complex than presently understood. A good example for a complex regulation is the methionine biosynthetic pathway in plants. The nutritional importance of Met and cysteine has motivated extensive studies of their roles in plant molecular physiology, especially regarding to their transport, synthesis, and accumulation in plants. Recent studies have demonstrated that Met metabolism is regulated differently in various plant species.

Gene Expression Regulation, Plant↗
PubMed2025

MEANtools integrates multi-omics data to identify metabolites and predict biosynthetic pathways.

During evolution, plants have developed the ability to produce a vast array of specialized metabolites, which play crucial roles in helping plants adapt to different environmental niches. However, their biosynthetic pathways remain largely elusive. In the past decades, increasing numbers of plant biosynthetic pathways have been elucidated based on approaches utilizing genomics, transcriptomics, and metabolomics. These efforts, however, are limited by the fact that they typically adopt a target-based approach, requiring prior knowledge. Here, we present MEANtools, a systematic and unsupervised computational integrative omics workflow to predict candidate metabolic pathways de novo by leveraging knowledge of general reaction rules and metabolic structures stored in public databases. In our approach, possible connections between metabolites and transcripts that show correlated abundance across samples are identified using reaction rules linked to the transcript-encoded enzyme families. MEANtools thus assesses whether these reactions can connect transcript-correlated mass features within a candidate metabolic pathway. We validate MEANtools using a paired transcriptomic-metabolomic dataset recently generated to reconstruct the falcarindiol biosynthetic pathway in tomato. MEANtools correctly anticipated five out of seven steps of the characterized pathway and also identified other candidate pathways involved in specialized metabolism, which demonstrates its potential for hypothesis generation. Altogether, MEANtools represents a significant advancement to integrate multi-omics data for the elucidation of biochemical pathways in plants and beyond.

Metabolomics↗
PubMed2000

Reconstruction of ABC Transporter Pathways of Archaea and Comparison of Their Genomes.

Reconstruction and comparison of metabolic pathway and regulatory network is an advanced task in genome function prediction. In this study, many bioinformatic tools were employed to reconstruct all ABC transporter pathways and predict their functional features of an archaeon, Pyrococcus abyssi, on genome scale. The comparison between ABC transporter pathways of P.abyssi and those of another archaeon, M.jannaschii, revealed that there was no peptide uptake ABC transporter system in M.jannaschii. This may result from their different metabolic types.

Journal Article↗
PubMed2001

Estimation of bidirectional metabolic fluxes from MS and NMR data using positional representations.

It is quite important to estimate the metabolic flux distribution (MFD) vectors in vivo, and to investigate the effect of culture environments on the flux distributions to uncover the metabolic regulation mechanism of microbial cells. The conventional approach is to compute the MFD using the stoichiometric equations and the measured specific rates (input and output variables). However, this method cannot give the MFD for the complex metabolic network which includes cyclic pathways. In the present investigation, we considered the method of analysing the metabolic fluxes based on 13C tracer experiments. In particular, we compared the different techniques of estimating the bidirectional fluxes in the metabolic networks, studying their applicability with respect to the different types of data formats obtained through GC-MS (gas chromatography-mass spectrometry) and NMR (nuclear magnetic resonance) measurements in labeling experiments. It was found that some techniques cannot be applied for GC-MS and NMR data. In the present research, therefore, a new preprocessing method for MS and NMR data was developed, to solve some of the problems encountered in the conventional approaches.

Carbon Isotopes↗
PubMed1999

METATOOL: for studying metabolic networks.

MOTIVATION: To reconstruct metabolic pathways from biochemical and/or genome sequence data, the stoichiometric and thermodynamic feasibility of the pathways has to be tested. This is achieved by characterizing the admissible region of flux distributions in steady state. This region is spanned by what can be called a convex basis. The concept of 'elementary flux modes' provides a mathematical tool to define all metabolic routes that are feasible in a given metabolic network. In addition, we define 'enzyme subsets' to be groups of enzymes that operate together in fixed flux proportions in all steady states of the system. RESULTS: Algorithms for computing the convex basis and elementary modes developed earlier are briefly reviewed. A newly developed algorithm for detecting all enzyme subsets in a given network is presented. All of these algorithms have been implemented in a novel computer program named METATOOL, whose features are outlined here. The algorithms are illustrated by an example taken from sugar metabolism. AVAILABILITY: METATOOL is available from ftp://bmsdarwin.brookes.ac. uk/pub/software/ibmpc/metatool. SUPPLEMENTARY INFORMATION: http://www. biologie.hu-berlin.de/biophysics/Theory/tpfeiffer/metatoo l.html

Algorithms↗