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At least 19 recordsLinked to original sources
PubMed1994

The Caco-2 cell monolayers as an intestinal metabolism model: metabolism of dipeptide Phe-Pro.

The metabolism of Phe-Pro was investigated in Caco-2 cell monolayers, a model of small intestinal epithelium. The results indicate that the majority of Phe-Pro was hydrolyzed during passage from the apical (AP) to basolateral (BL) side. The enzyme responsible for the hydrolysis is prolidase, a cytosolic enzyme. Through kinetic studies of a supernatant enzyme preparation, a Km of 30.4 microM and Vmax of 38.9 nmol/min per mg of protein were obtained. The enzyme catalyzed hydrolysis was inhibited by proline (66%), Zn+ (86%), Cu++ (100%), Fe (100%), PCMB (89%), and captopril (66%), but not by leucine. We also studied the transcellular transport of Phe-Pro by measuring the amount of Phe in the receiver media. In the presence of a proton gradient (AP pH6, BL pH7.4), the appearance rate of Phe in the BL media after Phe-Pro was loaded apically was at least 100 times faster than that in the AP media after Phe-Pro was loaded basolaterally. The former is also higher than the appearance rate of Phe without a transepithelial proton gradient (pH 6-pH 6) or against a proton gradient (pH7.4-pH6). The rate of appearance of Phe in the BL media (pH7.4) after Phe-Pro was loaded on the AP side (pH 6) was decreased by the presence in the AP media of proline (42%), leucine (40%), and captopril (17%), but not by Zn++. In conclusion, the transmembrane uptake of Phe-Pro is dependent on a proton gradient, and the intracellular metabolism of Phe-Pro is complete via hydrolysis by prolidase.

Cell Division↗
PubMed2004

Applications of metabolic modelling to plant metabolism.

In this paper some of the general concepts underpinning the computer modelling of metabolic systems are introduced. The difference between kinetic and structural modelling is emphasized, and the more important techniques from both, along with the physiological implications, are described. These approaches are then illustrated by descriptions of other work, in which they have been applied to models of the Calvin cycle, sucrose metabolism in sugar cane, and starch metabolism in potatoes.

Computer Simulation↗
PubMed2025

Reducing redundancy and enhancing accuracy through a phylogenetically-informed microbial community metabolic modeling approach.

MOTIVATION: Metabolic modeling has emerged as a powerful tool for predicting community functions. However, current modeling approaches face significant challenges in balancing the metabolic trade-offs between individual and community-level growth. In this study, we investigated the effect of metabolic relatedness among taxa on growth rate calculations by merging related taxa based on their metabolic similarity, introducing this approach as PhyloCOBRA. RESULTS: This approach enhanced the accuracy and efficiency of microbial community simulations by combining genome-scale metabolic models (GEMs) of closely related organisms, aligning with the concepts of niche differentiation and nestedness theory. To validate our approach, we implemented PhyloCOBRA within the MICOM and OptCom package (creating PhyloMICOM and PhyloOptCom, respectively), and applied it to metagenomic data from 186 individuals and four-species synthetic community (SynCom). Our results demonstrated significant improvement in the accuracy and reliability of growth rate predictions compared to the standard methods. Sensitivity analysis revealed that PhyloMICOM models were more robust to random noise, while Jaccard index calculations showed a reduction in redundancy, highlighting the enhanced specificity of the generated community models. Furthermore, PhyloMICOM reduced the computational complexity, addressing a key concern in microbial community simulations. This approach marks a significant advancement in community-scale metabolic modeling, offering a more stable, efficient, and ecologically relevant tool for simulating and understanding the intricate dynamics of microbial ecosystems. AVAILABILITY AND IMPLEMENTATION: PhyloCOBRA implementations are available as extensions to the MICOM packages and can be accessed at https://github.com/sepideh-mofidifar/PhyloCOBRA.

Phylogeny↗
PubMed2006

Metabolic models of ruminant metabolism: recent improvements and current status.

The NC-1009 regional research project has two broad goals of quantifying the properties of feeds and the metabolic interactions among nutrients that influence nutrient availability for milk production and that alter synthesis of milk, and using those quantitative relationships to challenge and refine computer-based nutrition systems for dairy cattle. The objective of this paper was to review progress in modeling. Significant progress has been made in model refinements over the past 10 yr as exemplified by the most recent NRC model (2001) and work on the Molly model of Baldwin and colleagues (1987). These models have different objectives but share many properties. The level of aggregation of the NRC model (2001) does not allow detailed analyses of specific metabolic reactions that affect nutritional efficiency. The Baldwin model is aggregated at the pathway level and is therefore amenable to assessment with a broad range of biological measurements. Recent improvements to that model include the addition of an ingredient based input scheme, use of in situ data to set ruminal protein degradation rates, and refinement of the representation of mammary cell numbers and activity. Although the Baldwin model appears to be appropriate structurally, several parameters are known to be inadequate. Predictions of ruminal N metabolism and total-tract starch digestions have similar accuracy as the NRC model. However, the NRC more accurately predicts total-tract fiber digestion and both models significantly overpredict total-tract lipid digestion. These errors contribute to overpredictions of weight retention when simulating full lactations with the Baldwin model and may result in performance prediction errors with the NRC model. Limitations remain in the descriptions of metabolism and metabolic regulation of the splanchnic, viscera, adipose tissue, body muscle, and mammary tissue. Integration of genetic control mechanisms can expand these efforts to assist genetic selection as well as feeding management decisions.

Animal Feed↗
PubMedDate not supplied

Modeling hybridoma cell metabolism using a generic genome-scale metabolic model of Mus musculus.

The reconstructed cellular metabolic network of Mus musculus, based on annotated genomic data, pathway databases, and currently available biochemical and physiological information, is presented. Although incomplete, it represents the first attempt to collect and characterize the metabolic network of a mammalian cell on the basis of genomic data. The reaction network is generic in nature and attempts to capture the carbon, energy, and nitrogen metabolism of the cell. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, including transport reactions between the compartments and the extracellular medium. The reaction list consists of 872 internal metabolites involved in a total of 1220 reactions, whereof 473 relate to known open reading frames. Initial in silico analysis of the reconstructed model is presented.

Amino Acids↗
PubMed2006

Analysis of growth of Lactobacillus plantarum WCFS1 on a complex medium using a genome-scale metabolic model.

A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for interpretation of complex fermentation data, as L. plantarum is adapted to nutrient-rich environments and only grows in media supplemented with vitamins and amino acids. (i) Based on experimental input and output fluxes, maximal ATP production was estimated and related to growth rate. (ii) Optimization of ATP production further identified amino acid catabolic pathways that were not previously associated with free-energy metabolism. (iii) Genome-scale elementary flux mode analysis identified 28 potential futile cycles. (iv) Flux variability analysis supplemented the elementary mode analysis in identifying parallel pathways, e.g. pathways with identical end products but different co-factor usage. Strongly increased flexibility in the metabolic network was observed when strict coupling between catabolic ATP production and anabolic consumption was relaxed. These results illustrate how a genome-scale metabolic model and associated constraint-based modeling techniques can be used to analyze the physiology of growth on a complex medium rather than a minimal salts medium. However, optimization of biomass formation using the Flux Balance Analysis approach, reported to successfully predict growth rate and by product formation in Escherichia coli and Saccharomyces cerevisiae, predicted too high biomass yields that were incompatible with the observed lactate production. The reason is that this approach assumes optimal efficiency of substrate to biomass conversion, and can therefore not predict the metabolically inefficient lactate formation.

Culture Media, Conditioned↗
PubMed2004

Basic principles of metabolic modeling of NMR (13)C isotopic turnover to determine rates of brain metabolism in vivo.

Metabolic modeling is a necessary part of the analysis of isotopic labeling data that is being obtained in the brain and other organs. Here are explained the basic principles of metabolic modeling of isotopic labeling studies, particularly with regard to (13)C isotopic measurements performed in vivo. The basic elements needed to simulate isotopic flows are described, and how to combine them to perform modeling analyses is explained. Procedures to introduce and evaluate model constraints and simplifications are discussed. The basic principle of isotopomer analysis is explained, as are mechanics of least-squares fitting of simulations to data. Closely related to the fitting is the effect of data scatter, which is discussed in the context of the non-normal distributions of uncertainty that are often seen with (13)C labeling measurements in vivo. This article is meant to provide a general background for investigators to begin to apply metabolic modeling analysis to (13)C isotopic labeling studies performed in vivo.

Brain↗
PubMed2026

gMISpy: integration of complex regulatory networks and genome scale metabolic models.

MOTIVATION: Genome-scale metabolic models lack explicit regulatory mechanisms, limiting their predictive accuracy for genetic interventions. Current methods for computing genetic Minimal Cut Sets either ignore regulatory networks entirely or use simplified acyclic representations that cannot capture regulatory feedback loops, ubiquitous features critical in cellular modeling. RESULTS: We developed gMISpy, a Python package that that enables efficient computation of genetic Minimal Intervention Sets (gMISs) in integrated genome-scale metabolic and regulatory networks. gMISpy incorporates cyclic regulatory logic into our previous computational framework using layered Boolean networks and BoNesis framework, resulting in a more accurate modeling of how regulatory interactions affect metabolic genes. Benchmarking across four different regulatory networks with Human-GEM showed consistent improvements in prediction accuracy, with Matthews correlation coefficient gains ranging from 2.50% to 14.42%. Validation against cancer data from DepMap and Project Score confirmed that cyclic integration reduces false positives and better captures biological vulnerabilities compared to acyclic approaches. AVAILABILITY AND IMPLEMENTATION: https://github.com/PlanesLab/cyclic-gMISpy.

Software↗
PubMed1979

Metabolic model for cadmium in man.

A metabolic model for cadmium has been formulated in terms of a flow scheme for cadmium among eight body compartments. The mathematical description of the flow of cadmium between compartments consits of a number of differential equations, and the accumulation of cadmium may thus be calculated. Coeffcients for the flow of cadmium were estimated from empirical data both from animals and man. The modelling serves two main purposes: it provides a means of using present knowledge about metabolism in order to calculate expected accumulation in critical organs and other tissues and body fluids at certain intake levels and it makes it possible to define deficiencies in our present knowledge about metabolism. Data recently collected, partly as a result of considerations related to the model, have confirmed the assumptions of a very long biological half-time in other tissues and the small excretion of cadmium via bile.

Adult↗
PubMed1985

[Methodological aspects of the determination of nitrogen metabolism parameters from 15N-tracer experiments on swine based on nitrogen metabolism models. 2. Calculation of N metabolism parameters from the time of urinary excretion of 15N].

After a 45-hour infusion of 15N-lysine and 15N-glycine resp. with pigs of 25 kg live weight, the infusion of the tracer was disrupted and a 3-day reduction phase was made part of the experiment. The N-metabolism parameters such as protein synthesis and decomposition quotas were calculated on the basis of the temporal development of 15N-amounts excreted in urine. The labelling and reduction phases were evaluated simultaneously. By means of including the total N-amount of the animal body the balance calculation becomes more stable. The possible influence of the tracer amino acid in connection with the 3-pool-model is discussed on the basis of the ascertained parameters.

Animals↗
PubMed2025

Evolution and applications of genome-scale metabolic models in yeast systems biology studies.

Genome-scale metabolic models (GEMs) can be used to simulate the metabolic network of an organism in a systematic and holistic way. Different yeast species, including Saccharomyces cerevisiae, have emerged as powerful cell factories for bioproduction. Recently, with the dedicated efforts from the scientific community, significant progress has been made in the development of yeast GEMs. Numerous versions of yeast GEMs and the derived multiscale models have been released, facilitating integrative omics analysis and rational strain design for different types of yeast cell factories. These advancements reflected the evolution and maturation of yeast GEMs together with a model ecosystem around them. This review will summarize the development and expansion of yeast GEMs and discuss their applications in yeast systems biology studies. It is anticipated that yeast GEMs will continue to play an increasingly important role in pioneering yeast physiological and metabolic studies in coming years.

Systems Biology↗
PubMed2006

Development of a kinetic metabolic model: application to Catharanthus roseus hairy root.

A kinetic metabolic model describing Catharanthus roseus hairy root growth and nutrition was developed. The metabolic network includes glycolysis, pentose-phosphate pathway, TCA cycle and the catabolic reactions leading to cell building blocks such as amino acids, organic acids, organic phosphates, lipids and structural hexoses. The central primary metabolic network was taken at pseudo-steady state and metabolic flux analysis technique allowed reducing from 31 metabolic fluxes to 20 independent pathways. Hairy root specific growth rate was described as a function of intracellular concentration in cell building blocks. Intracellular transport and accumulation kinetics for major nutrients were included. The model uses intracellular nutrients as well as energy shuttles to describe metabolic regulation. Model calibration was performed using experimental data obtained from batch and medium exchange liquid cultures of C. roseus hairy root using a minimal medium in Petri dish. The model is efficient in estimating the growth rate.

Catharanthus↗
PubMed2006

ScrumPy: metabolic modelling with Python.

ScrumPy is a software package used for the definition and analysis of metabolic models. It is written using the Python programming language that is also used as a user interface. ScrumPy has features for both kinetic and structural modelling, but the emphasis is on structural modelling and those features of most relevance to analysis of large (genome-scale) models. The aim is at describing ScrumPy's functionality to readers with some knowledge of metabolic modelling, but implementation, programming and other computational details are omitted. ScrumPy is released under the Gnu Public Licence, and available for download from http://mudshark.brookes.ac.uk/ ScrumPy.

Adaptation, Physiological↗
PubMed2002

Genome-scale metabolic model of Helicobacter pylori 26695.

A genome-scale metabolic model of Helicobacter pylori 26695 was constructed from genome sequence annotation, biochemical, and physiological data. This represents an in silico model largely derived from genomic information for an organism for which there is substantially less biochemical information available relative to previously modeled organisms such as Escherichia coli. The reconstructed metabolic network contains 388 enzymatic and transport reactions and accounts for 291 open reading frames. Within the paradigm of constraint-based modeling, extreme-pathway analysis and flux balance analysis were used to explore the metabolic capabilities of the in silico model. General network properties were analyzed and compared to similar results previously generated for Haemophilus influenzae. A minimal medium required by the model to generate required biomass constituents was calculated, indicating the requirement of eight amino acids, six of which correspond to essential human amino acids. In addition a list of potential substrates capable of fulfilling the bulk carbon requirements of H. pylori were identified. A deletion study was performed wherein reactions and associated genes in central metabolism were deleted and their effects were simulated under a variety of substrate availability conditions, yielding a number of reactions that are deemed essential. Deletion results were compared to recently published in vitro essentiality determinations for 17 genes. The in silico model accurately predicted 10 of 17 deletion cases, with partial support for additional cases. Collectively, the results presented herein suggest an effective strategy of combining in silico modeling with experimental technologies to enhance biological discovery for less characterized organisms and their genomes.

Amino Acids↗
PubMed2003

Validation of ICRP metabolic models for the transuranics in a Japanese population.

Distributions of (239,240)Pu and 241Am in the tissues of Japanese were determined and then compared to those estimated using recent ICRP metabolic models. Intakes by inhalation and ingestion were calculated and used as input to the ICRP-30 model or a combination of the ICRP-66 lung model and the ICRP-67 metabolic model. The (239,240)Pu distribution in the lung, liver, skeleton, kidney, and muscle using the combination ICRP-66 and 67 models agreed well with the measured data. However, the measured plutonium concentration in the spleen was higher than predicted and than found in the kidney or muscle and indicates that the spleen should be treated as a separate organ in the ICRP model. The fractional uptake via ingestion of (239,240)Pu was estimated to be 11% with 5 x 10(-4) as the f1 value. The combination of ICRP-66 and 67 models were adequate descriptors of the organ burdens of 241Am measured in Akita and Niigata district populations. The 241Am ingrowth from 241Pu taken into in the human body contributes 90% of the measured burden.

Administration, Inhalation↗
PubMed2002

Proposed modifications to metabolic model for glycogen-accumulating organisms under anaerobic conditions.

Filipe et al. (2001) proposed an anaerobic metabolic model for glycogen-accumulating organisms (GAO) in which the succinate-propionate pathway was used to describe the production of propionyl-CoA. However, propionyl-CoA is only an intermediate product in the above pathway. Stopping at propionyl-CoA instead of propionate (the end product of the pathway) results in the consumption of one ATP from succinate to succinyl-CoA, which was not accounted for in the model of Filipe et al. (2001). This resulted in significant errors in the stoichiometric coefficients in the final metabolic model. A modified model is presented in this communication and is shown to fit the experimental data significantly better than the original model.

Acetyl Coenzyme A↗
PubMed2006

In vivo 13C NMR spectroscopy and metabolic modeling in the brain: a practical perspective.

In vivo 13C NMR spectroscopy has the unique capability to measure metabolic fluxes noninvasively in the brain. Quantitative measurements of metabolic fluxes require analysis of the 13C labeling time courses obtained experimentally with a metabolic model. The present work reviews the ingredients necessary for a dynamic metabolic modeling study, with particular emphasis on practical issues.

Animals↗