METABOLIC PATHWAYS IN EXPERIMENTAL SUGAR AND RADIATION CATARACT.
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Carbohydrate Metabolism↗
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Results for “Metabolic Networks and Pathways”
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GeneNet in 2005.
The GeneNet system is designed for collection and analysis of the data on gene and metabolic networks, signal transduction pathways and kinetic characteristics of elementary processes. In the past 2 years, the GeneNet structure was considerably improved: (i) the current version of the database is now implemented using ORACLE9i; (ii) the capacities to describe the structure of the protein complexes and the interactions between the units are increased; (iii) two tables with kinetic constants and more detailed descriptions of certain reactions were added; and (iv) a module for kinetic modeling was supplemented. The current SRS release of the GeneNet database contains 37 graphical maps of gene networks, as well as descriptions of 1766 proteins, 1006 genes, 241 small molecules and 3254 relationships between gene network units, and 552 kinetic constants. Information distributed between 16 interlinked tables was obtained by annotating 1980 journal publications. SRS release of the GeneNet database, the graphical viewer and the modeling section are available at http://wwwmgs.bionet.nsc.ru/mgs/gnw/genenet/.
Computer Graphics↗
A new metabolic pathway for a sulfonamide group.
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Metabolic Networks and Pathways↗
An integrated modeling-experimental strategy for the analysis of metabolic pathways.
An inherent problem in studying the behavior of a metabolic pathway is the impossibility of developing a complete, detailed model that includes all the cellular processes that have an impact on the set of fluxes in such a pathway. Lacking this, one requires some means of modeling the interactions between a metabolic pathway and other cellular processes for the purpose of analyzing pathway characteristics within the cell (e.g., determining sensitivity coefficients for various steps in the pathway) with a minimal amount of time and effort. A general framework is developed for studying these issues in a rigorous manner. Using this framework, detailed knowledge about a metabolic pathway (i.e., a set of rate expressions for steps in the pathway) can be combined with the results from a relatively simple set of experiments in order to obtain estimates for the sensitivity of the pathway to enzyme activities, inhibition constants, and other parameters that determine the pathway's behavior, while accounting for the pathway's interaction with the rest of the cellular metabolism. A model system representing amino acid production is used to illustrate the problem and to provide results based on computational experiments. The modeling strategy described here should be useful in genetic design to improve pathway fluxes and metabolic network selectivity.
Amino Acids↗
Control in channelled pathways. A matrix method calculating the enzyme control coefficients.
The usual equations expressing the enzyme control coefficients (quantitative indicators of 'global' control properties of a pathway) via the elasticity coefficients (reflecting local kinetic properties of an enzyme reaction), cannot be applied to a variety of 'non-ideal' pathways, in particular to pathways with metabolic channelling. Here we show that the relationship between the control and elasticity coefficients can be obtained by considering such a metabolic pathway as a network of elemental chemical conversions (steps). To calculate the control coefficients of enzymes one should first determine the elasticity coefficients of such elemental steps and then take their appropriate combinations. Although the method is illustrated for a channelled pathway it can be used for any non-ideal pathway including those with high enzyme concentrations where the sequestration of metabolites by enzymes cannot be neglected.
Journal Article↗
How to find "missing" genes.
Assigning function to "new" proteins is frequently the rate-determining step for deciphering metabolic pathways and regulatory networks. Osterman and Begley break down this barrier by demonstrating that comparative analyses of microbial genomes is a powerful strategy for identifying pathway components.
Bacteria↗
Evolving protein interaction networks through gene duplication.
The topology of the proteome map revealed by recent large-scale hybridization methods has shown that the distribution of protein-protein interactions is highly heterogeneous, with many proteins having few edges while a few of them are heavily connected. This particular topology is shared by other cellular networks, such as metabolic pathways, and it has been suggested to be responsible for the high mutational homeostasis displayed by the genome of some organisms. In this paper we explore a recent model of proteome evolution that has been shown to reproduce many of the features displayed by its real counterparts. The model is based on gene duplication plus re-wiring of the newly created genes. The statistical features displayed by the proteome of well-known organisms are reproduced and suggest that the overall topology of the protein maps naturally emerges from the two leading mechanisms considered by the model.
Animals↗
Metabolic-flux profiling of the yeasts Saccharomyces cerevisiae and Pichia stipitis.
The so far largely uncharacterized central carbon metabolism of the yeast Pichia stipitis was explored in batch and glucose-limited chemostat cultures using metabolic-flux ratio analysis by nuclear magnetic resonance. The concomitantly characterized network of active metabolic pathways was compared to those identified in Saccharomyces cerevisiae, which led to the following conclusions. (i) There is a remarkably low use of the non-oxidative pentose phosphate (PP) pathway for glucose catabolism in S. cerevisiae when compared to P. stipitis batch cultures. (ii) Metabolism of P. stipitis batch cultures is fully respirative, which contrasts with the predominantly respiro-fermentative metabolic state of S. cerevisiae. (iii) Glucose catabolism in chemostat cultures of both yeasts is primarily oxidative. (iv) In both yeasts there is significant in vivo malic enzyme activity during growth on glucose. (v) The amino acid biosynthesis pathways are identical in both yeasts. The present investigation thus demonstrates the power of metabolic-flux ratio analysis for comparative profiling of central carbon metabolism in lower eukaryotes. Although not used for glucose catabolism in batch culture, we demonstrate that the PP pathway in S. cerevisiae has a generally high catabolic capacity by overexpressing the Escherichia coli transhydrogenase UdhA in phosphoglucose isomerase-deficient S. cerevisiae.
Amino Acids↗
cPath: open source software for collecting, storing, and querying biological pathways.
BACKGROUND: Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required. RESULTS: We have developed cPath, an open source database and web application for collecting, storing, and querying biological pathway data. cPath makes it easy to aggregate custom pathway data sets available in standard exchange formats from multiple databases, present pathway data to biologists via a customizable web interface, and export pathway data via a web service to third-party software, such as Cytoscape, for visualization and analysis. cPath is software only, and does not include new pathway information. Key features include: a built-in identifier mapping service for linking identical interactors and linking to external resources; built-in support for PSI-MI and BioPAX standard pathway exchange formats; a web service interface for searching and retrieving pathway data sets; and thorough documentation. The cPath software is freely available under the LGPL open source license for academic and commercial use. CONCLUSION: cPath is a robust, scalable, modular, professional-grade software platform for collecting, storing, and querying biological pathways. It can serve as the core data handling component in information systems for pathway visualization, analysis and modeling.
Computational Biology↗
The Relationship between Yeast Coexpressed Gene Clusters and Their Upstream cis-acting Elements.
DNA microarrays bring biology a new approach to study functions of genes and genomes from their expression pattern on a genomic scale. With its fully sequenced genome and newly published expression patterns available, budding yeast (Saccharomyces cerevisiae) was chosen to carry out an investigation of the relationship between gene 5' upstream cis-acting elements and expression patterns using bioinformatic tools. Results show that genes in the same cluster share common cis-acting element candidates and can be regulated by same transfactors. In the sites found by this study, some sites are corresponding to known cis-acting elements, while others may indicate new ones that can be tested by experiments. The results are helpful to understand more about gene functions, metabolic pathways and genetic networks.
Journal Article↗
Modeling and simulation of biological systems with stochasticity.
Mathematical modeling is a powerful approach for understanding the complexity of biological systems. Recently, several successful attempts have been made for simulating complex biological processes like metabolic pathways, gene regulatory networks and cell signaling pathways. The pathway models have not only generated experimentally verifiable hypothesis but have also provided valuable insights into the behavior of complex biological systems. Many recent studies have confirmed the phenotypic variability of organisms to an inherent stochasticity that operates at a basal level of gene expression. Due to this reason, development of novel mathematical representations and simulations algorithms are critical for successful modeling efforts in biological systems. The key is to find a biologically relevant representation for each representation. Although mathematically rigorous and physically consistent, stochastic algorithms are computationally expensive, they have been successfully used to model probabilistic events in the cell. This paper offers an overview of various mathematical and computational approaches for modeling stochastic phenomena in cellular systems.
Algorithms↗
In silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity.
BACKGROUND: Understanding how metabolic capabilities diverge across microbial species is essential for deciphering community function, ecological interactions, and the design of synthetic microbiomes. Despite shared core pathways, microbial phenotypes can differ markedly due to evolutionary adaptations and metabolic specialization. Genome-scale metabolic models (GEMs) provide a systems-level framework to explore these differences; however, their complexity hinders direct comparison. RESULTS: We introduce NIS (Neidhardt-Ingraham-Schaechter), a computational workflow that integrates the redGEM, lumpGEM, and redGEMX algorithms to systematically reduce genome-scale models into biologically interpretable modules. This approach enables direct, quantitative comparison of fueling pathways, biomass biosynthetic routes, and environmental exchange processes while retaining essential metabolic information. We first demonstrate the utility of NIS by analyzing Escherichia coli and Saccharomyces cerevisiae, which revealed both conserved and divergent strategies in central metabolism, biosynthetic cost, and substrate utilization. We then applied NIS to the core honeybee gut microbiome, uncovering distinct metabolic traits, functional redundancy, and complementarity that help explain auxotrophy, cross-feeding interactions, and microbial coexistence. CONCLUSIONS: NIS provides an automated, scalable, and reproducible framework for dissecting microbial metabolic networks beyond gene content or taxonomy. By linking metabolism to ecological function, NIS offers new opportunities to interpret microbial community dynamics and to support the rational design of microbiomes in health, agriculture, and environmental applications. Video Abstract.
Metabolic Networks and Pathways↗
Evidence for the presence of phospholipid hydroperoxide glutathione peroxidase in human platelets: implications for its involvement in the regulatory network of the 12-lipoxygenase pathway of arachidonic acid metabolism.
The 12-lipoxygenase pathway of arachidonic acid metabolism in platelets and other cells is bifurcated into a reduction route yielding 12-hydroxyeicosatetraenoic acid (12-HETE) and an isomerization route forming hepoxilins. Here we show for the first time the presence of phospholipid hydroperoxide glutathione peroxidase (PHGPx) protein and its activity in platelets. The ratio of the activity of PHGPx to that of cytosolic glutathione peroxidase (GPx-1) was consistently found to be approx. 1:60 in platelets and UT7 megakaryoblasts. Moreover, short-lived PHGPx mRNA was detected in megakaryocytes but not in platelets. Carboxymethylation of selenium-containing glutathione peroxidases by iodoacetate, which results in the inactivation of PHGPx and GPx-1 without inhibition of 12-lipoxygenase, markedly altered the pattern of arachidonic acid metabolism in human platelets. Whereas the formation of 12-HETE was inhibited by 80%, a concomitant accumulation of 12-hydroperoxyeicosatetraenoic acid (12-HpETE) by two orders of magnitude as well as the formation of hepoxilins A(3) and B(3) were observed. The formation of hepoxilins also occurred when 12-HpETE was added to untreated platelets. In selenium-deficient UT7 cells, which were devoid of GPx-1 but not of PHGPx, the reduction of 12-HPETE was retained, albeit with a lower rate than in control cells containing GPx-1. We therefore believe that both GPx-1 and PHGPx are involved in the regulatory network of the 12-lipoxygenase pathway in platelets and other mammalian cells. Moreover, the diminution of hydroperoxide tone in platelets incubated with arachidonic acid leads primarily to the formation of 12-HETE, whereas the increase in hydroperoxide tone (a situation found under oxidative stress or selenium deficiency or on incubation with 12-HPETE) partly diverts the 12-lipoxygenase pathway from the reduction route to the isomerization route, thus resulting in the formation of hepoxilins.
8,11,14-Eicosatrienoic Acid↗
Effects of a dose of parathyroid extract on bone metabolic pathways.
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Bone and Bones↗
Determination of metabolic flux ratios from 13C-experiments and gas chromatography-mass spectrometry data: protocol and principles.
Network topology is a necessary fundament to understand function and properties of microbial reaction networks. A valuable method for experimental elucidation of metabolic network topology is metabolic flux ratio analysis, which quantifies the relative contribution of two or more converging pathways to a given metabolite. It is based on 13C-labeling experiments, gas chromatography-mass spectrometry analysis, and probabilistic equations that relate mass distributions in proteinogenic amino acids to pathway activity. Here, we describe the protocol for sample generation and illustrate the principles underlying the calculation of metabolic flux ratios with three examples. These principles are also implemented in the publicly available software FiatFlux, which directly calculates flux ratios from the mass spectra of amino acids.
Amino Acids↗
Sequential blocking of metabolic pathways in vivo.
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Fluoroacetates↗
Metabolic pathways in experimental diabetic cataract.
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Cataract↗
Motif search in graphs: application to metabolic networks.
The classic view of metabolism as a collection of metabolic pathways is being questioned with the currently available possibility of studying whole networks. Novel ways of decomposing the network into modules and motifs that could be considered as the building blocks of a network are being suggested. In this work, we introduce a new definition of motif in the context of metabolic networks. Unlike in previous works on (other) biochemical networks, this definition is not based only on topological features. We propose instead to use an alternative definition based on the functional nature of the components that form the motif, which we call a reaction motif. After introducing a formal framework motivated by biological considerations, we present complexity results on the problem of searching for all occurrences of a reaction motif in a network and introduce an algorithm that is fast in practice in most situations. We then show an initial application to the study of pathway evolution. Finally, we give some general features of the observed number of occurrences in order to highlight some structural features of metabolic networks.
Algorithms↗