Title:Metabolic Network Analysis: Current Status and Way Forward. VOLUME: 8 ISSUE: 3. Author(s):Shweta Kolhi and Ashok S. Kolaskar. Affiliation:Bioinformatics Center, University of Pune, Pune - 411007, India.. Keywords:Biological organization, metabolic categorization, metabolic networks analysis, metabolic pathways alignment, modular organization, network properties.. Abstract:One of the fundamental aims of life science is to gain insights into the functioning of an organism at systems level. Generation and systematic storage of enormous biological data in the post genomic era has made systems level studies a reality. For studies involving systems level investigation, metabolic pathways data inferred from intricate interactions amongst genes/enzymes/proteins are best suited and are being used extensively as they represent dynamic interactions in an organism. Consequently research in the field of comparative metabolomics as well as metabolic networks analysis is undergoing rapid improvement. ...
Most diseases are the consequence of the breakdown of cellular processes, but the relationships among genetic/epigenetic defects, the molecular interaction networks underlying them, and the disease phenotypes remain poorly understood. To gain insights into such relationships, here we constructed a bipartite human disease association network in which nodes are diseases and two diseases are linked if mutated enzymes associated with them catalyze adjacent metabolic reactions. We find that connected disease pairs display higher correlated reaction flux rate, corresponding enzyme-encoding gene coexpression, and higher comorbidity than those that have no metabolic link between them. Furthermore, the more connected a disease is to other diseases, the higher is its prevalence and associated mortality rate. The network topology-based approach also helps to uncover potential mechanisms that contribute to their shared pathophysiology. Thus, the structure and modeled function of the human metabolic network ...
Genome-scale metabolic network reconstructions are now routinely used in the study of metabolic pathways, their evolution and design. The development of such reconstructions involves the integration of information on reactions and metabolites from the scientific literature as well as public databases and existing genome-scale metabolic models. The reconciliation of discrepancies between data from these sources generally requires significant manual curation, which constitutes a major obstacle in efforts to develop and apply genome-scale metabolic network reconstructions. In this work, we discuss some of the major difficulties encountered in the mapping and reconciliation of metabolic resources and review three recent initiatives that aim to accelerate this process, namely BKM-react, MetRxn and MNXref (presented in this article). Each of these resources provides a pre-compiled reconciliation of many of the most commonly used metabolic resources. By reducing the time required for manual curation of ...
Recent development of high-throughput analytical techniques has made it possible to qualitatively identify a number of metabolites simultaneously. Correlation and multivariate analyses such as principal component analysis have been widely used to analyse those data and evaluate correlations among the metabolic profiles. However, these analyses cannot simultaneously carry out identification of metabolic reaction networks and prediction of dynamic behaviour of metabolites in the networks. The present study, therefore, proposes a new approach consisting of a combination of statistical technique and mathematical modelling approach to identify and predict a probable metabolic reaction network from time-series data of metabolite concentrations and simultaneously construct its mathematical model. Firstly, regression functions are fitted to experimental data by the locally estimated scatter plot smoothing method. Secondly, the fitted result is analysed by the bivariate Granger causality test to determine which
TY - JOUR. T1 - From genomes to in silico cells via metabolic networks. AU - Borodina, Irina. AU - Nielsen, Jens. PY - 2005. Y1 - 2005. N2 - Genome-scale metabolic models are the focal point of systems biology as they allow the collection of various data types in a form suitable for mathematical analysis. High-quality metabolic networks and metabolic networks with incorporated regulation have been successfully used for the analysis of phenotypes from phenotypic arrays and in gene-deletion studies. They have also been used for gene expression analysis guided by metabolic network structure, leading to the identification of commonly regulated genes. Thus, genome-scale metabolic modeling currently stands out as one of the most promising approaches to obtain an in silico prediction of cellular function based on the interaction of all of the cellular components. AB - Genome-scale metabolic models are the focal point of systems biology as they allow the collection of various data types in a form ...
Powerful approach to design cells with optimized metabolic functionalities is to apply the metabolic pathway analysis tool to analyze cellular metabolism and elucidate interaction of cell genotype and phenotype as outlined in the recent review (4). A metabolic network describing a cellular metabolism typically contains hundreds to thousands of reactions catalyzed by functional enzymes to convert feed substrates into precursor metabolites used to synthesize cell components for growth or other metabolites secreted to extracellular environments. These functional enzymes are directly encoded by functional genes that determine cell phenotypes. By using elementary mode analysis as the metabolic pathway analysis tool, a metabolic network can be decomposed into unique pathways, each of which contains a minimal set of enzymatic reactions supporting cell functions (5). Each of these independent pathways can represent a physiological state of cell operation. The knowledge of these pathways allows the ...
Oxygen is thought to promote species and biomolecule diversity. Previous studies have suggested that oxygen expands metabolic networks by acquiring metabolites with different chemical properties (higher hydrophobicity, for example). However, such conclusions are typically based on biased evaluation, and are therefore non-conclusive. Thus, we re-investigated the effect of oxygen on metabolic evolution using a phylogenetic comparative method and metadata analysis to reduce the bias as much as possible. Notably, we found no difference in metabolic network expansion between aerobes and anaerobes when evaluating phylogenetic relationships. Furthermore, we showed that previous studies have overestimated or underestimated the degrees of differences in the chemical properties (e.g., hydrophobicity) between oxic and anoxic metabolites in metabolic networks of unicellular organisms; however, such overestimation was not observed when considering the metabolic networks of multicellular organisms. These findings
In this PhD, we present some algorithms and complexity results for two general problems that arise in the analysis of a metabolic network: the search for elementary modes of a network and the search for minimal precursors sets. Elementary modes is a common tool in the study of the cellular characteristic of a metabolic network. An elementary mode can be seen as a minimal set of reactions that can work in steady state independently of the rest of the network. It has therefore served as a mathematical model for the possible metabolic pathways of a cell. Their computation is not trivial and poses computational challenges. We show that some problems, like checking consistency of a network, finding one elementary mode or checking that a set of reactions constitutes a cut are easy problems, giving polynomial algorithms based on LP formulations. We also prove the hardness of central problems like finding a minimum size elementary mode, finding an elementary mode containing two given reactions, counting the
A minimal cut set is a minimal set of reactions whose inactivation would guarantee a failure in a certain network function or functions. Minimal cut sets (MCSs) were initially developed from the metabolic pathway analysis method (MPA) of elementary modes (EMs); they provide a way of identifying target genes for eliminating a certain objective function from a holistic perspective that takes into account the structure of the whole metabolic network. The concept of MCSs is fairly new and still being explored and developed; the initial concept has developed into a generalized form and its similarity to other network characterizations are discussed. MCSs can be used in conjunction with other constraints-based methods to get a better understanding of the capability of metabolic networks and the interrelationship between metabolites and enzymes/genes. The concept could play an important role in systems biology by contributing to fields such as metabolic and genetic engineering where it could assist in finding
Metabolic networks are extensively regulated to facilitate tissue-specific metabolic programs and robustly maintain homeostasis in response to dietary changes. Homeostatic metabolic regulation is achieved through metabolite sensing coupled to feedback regulation of metabolic enzyme activity or expression. With a wealth of transcriptomic, proteomic, and metabolomic data available for different cell types across various conditions, we are challenged with understanding global metabolic network regulation and the resulting metabolic outputs. Stoichiometric metabolic network modeling integrated with "omics" data has addressed this challenge by generating nonintuitive, testable hypotheses about metabolic flux rewiring. Model organism studies have also yielded novel insight into metabolic networks. This review covers three topics: the feedback loops inherent in metabolic regulatory networks, metabolic network modeling, and interspecies studies utilizing Caenorhabditis elegans and various bacterial ...
As most of the uses of plants are intimately linked to their metabolic output or activity, there is a renewed interest in understanding the behavior and regulation of plant metabolic networks. The only direct measure of metabolic activity, and the facet most closely related to biological function, is flux through the metabolic network (Libourel and Shachar-Hill, 2008). There has been a considerable research effort in the last few years to develop and refine methods that allow fluxes in large metabolic networks to be determined. The best established of these methods, steady-state metabolic flux analysis (MFA), involves measuring the redistribution of a supplied stable isotope, usually 13C, at metabolic and isotopic steady state (Ratcliffe and Shachar-Hill, 2006; Allen et al., 2009a). Flux maps of a range of heterotrophic plant cells and tissues have been produced, providing information on the operation of different flux modes (Sriram et al., 2004, 2007; Schwender et al., 2006; Allen et al., ...
In the post-genomic era, the biochemical information for individual compounds, enzymes, reactions to be found within named organisms has become readily available. The well-known KEGG and BioCyc databases provide a comprehensive catalogue for this information and have thereby substantially aided the scientific community. Using these databases, the complement of enzymes present in a given organism can be determined and, in principle, used to reconstruct the metabolic network. However, such reconstructed networks contain numerous properties contradicting biological expectation. The metabolic networks for a number of organisms are reconstructed from KEGG and BioCyc databases, and features of these networks are related to properties of their originating database.
incollection{2087361, abstract = {Metabolism can be defined as the complete set of chemical reactions that occur in living organisms in order to maintain life. Enzymes are the main players in this process as they are responsible for catalyzing the chemical reactions. The enzyme--reaction relationships can be used for the reconstruction of a network of reactions, which leads to a metabolic model of metabolism. A genome-scale metabolic network of chemical reactions that take place inside a living organism is primarily reconstructed from the information that is present in its genome and the literature and involves steps such as functional annotation of the genome, identification of the associated reactions and determination of their stoichiometry, assignment of localization, determination of the biomass composition, estimation of energy requirements, and definition of model constraints. This information can be integrated into a stoichiometric model of metabolism that can be used for detailed ...
Pathway Tools is a comprehensive symbolic systems biology software system that supports several use cases in bioinformatics and systems biology: *Development of organism-specific databases called Pathway/Genome Databases (PGDBs) that integrate many bioinformatics datatypes, from genomes to pathways to regulatory networks. *Development of metabolic-flux models using flux-balance analysis *Scientific visualization, web publishing, and dissemination of those organism-specific databases, including: **Automatic display of metabolic pathways and full metabolic networks; generation of metabolic map diagram and of metabolic map poster ([http://bioinformatics.ai.sri.com/posters/ecoli-metab.pdf example]). **Genome browser; comparative genome browser; generation of genome poster ([http://bioinformatics.ai.sri.com/posters/ecoli-genome.pdf example]). **Display of operons, regulons, and full transcriptional regulatory networks *Analysis of omics datasets, including painting omics data on to diagrams of the ...
Metabolic reconstruction and subsequent mathematical computation has become a useful tool in the post‐genomic era by aiding both biological computation and experimentation. In this work, we present, characterize and utilize the iAF1260 metabolic reconstruction of E. coli K‐12 MG1655. The reconstruction serves as both a BiGG database containing the current knowledge of E. coli metabolism, as well as a framework for mathematical analysis. Accordingly, the major contributions from this work are: (1) an expansion in size, scope and detail of the metabolic network of E. coli, effectively exhausting the available literature, (2) an enumeration and description of the parameters and methods needed to utilize the reconstruction as a predictive model; examples of simulation results compared with high‐throughput experimental data are presented and (3) the inclusion of thermodynamic information and a novel thermodynamic consistency analysis for chemical transformations accounted for in the ...
Researchers are studying whether when animals eat affects what eventually happens to what they eat. Its a reasonable question since different metabolic pathways are most active at different times of the day.
Designing and executing a workflow having flow-based and constraint-based regions. A user selects one or more activities to be part of a constraint-based region. Each constraint-based region has a constraint associated therewith. The workflow is executed by executing the flow-based region and the constraint-based region. The flow-based region executes sequentially. The constraint is evaluated, and the constraint-based region executes responsive to the evaluated constraint.
ChEBI and genome-scale metabolic reconstructions Neil Swainston Manchester Centre for Integrative Systems Biology 2 nd ChEBI User Group Workshop, EMBL-EBI, Hi…
In biochemistry, metabolic pathways are series of chemical reactions occurring within a cell. In each pathway, a principal chemical is modified by a series of chemical reactions. Enzymes catalyze these reactions, and often require dietary minerals, vitamins, and other cofactors in order to function properly. Because of the many chemicals (a.k.a. metabolites) that may be involved, metabolic pathways can be quite elaborate. In addition, numerous distinct pathways co-exist within a cell. This collection of pathways is called the metabolic network. Pathways are important to the maintenance of homeostasis within an organism. Catabolic (break-down) and Anabolic (synthesis) pathways often work interdependently to create new biomolecules as the final end-products. [Metabolic pathway. Wikipedia] |br|The biochemical diagram example Metabolic pathway map was created using the ConceptDraw PRO diagramming and vector drawing software extended with the Biology solution from the Science and Education area of
SteatoNet metabolic network.The key metabolic pathways and their regulation by hormones, adipokines and transcriptional and post-translational regulatory factor
Biochemical network reconstructions have become popular tools in systems biology. Metabolicnetwork reconstructions are biochemically, genetically, and genomically (BiGG) structured databases of biochemical reactions and metabolites. They contain information such as exact reaction stoichiometry, reaction reversibility, and the relationships between genes, proteins, and reactions. Network reconstructions have been used extensively to study the phenotypic behavior of wild-type and mutant stains under a variety of conditions, linking genotypes with phenotypes. Such phenotypic simulations have allowed for the prediction of growth after genetic manipulations, prediction of growth phenotypes after adaptive evolution, and prediction of essential genes. Additionally, because network reconstructions are organism specific, they can be used to understand differences between organisms of species in a functional context.There are different types of reconstructions representing various types of biological networks
Metabolic network showing the number of O. carboxidovorans proteins identified in each COG category in the current study.Proteins (referred by locus tag number
The availability of annotated genome sequences has enabled the reconstruction of genome-scale metabolic networks for an increasing number of microorganisms. A popular and efficient method to study the characteristics and capabilities of such large-scale biochemical networks is flux balance analysis (FBA). In this tutorial we introduce the mathematical backgrounds of FBA and related methods, and present some of its recent biological applications ranging from biotechnology to evolutionary biology. ...
Metabolism is refer to all chemical reactions that occur in living organisms, including digestion and the transport of substances into and between different cells. Metabolism is usually divided into catabolism, that breaks down organic matter and harvests energy by way of cellular respiration, and anabolism that uses energy to construct components of cells such as proteins and nucleic acids. |br|The chemical reactions of metabolism are organized into metabolic pathways, in which one chemical is transformed through a series of steps into another chemical, by a sequence of enzymes. [Metabolism. Wikipedia] |br|The biochemical pathway map example Key metabolic processes was created using the ConceptDraw PRO diagramming and vector drawing software extended with the Biology solution from the Science and Education area of ConceptDraw Solution Park. Metabolic Maps
Immune cell activation and differentiation occurs concurrently with metabolic reprogramming. This ensures that activated cells generate the energy and substrates necessary to perform their specified function. Likewise, the metabolic programs among different cells of the immune system vary. By targeting different metabolic pathways, these differences allow for selective regulation of immune responses. Further, the relative susceptibility of cells to a metabolic inhibitor is dictated by their metabolic demands; cellular selectivity is based on demand. Therefore, where differences exist in metabolic pathways between healthy and pathogenic cells, there is opportunity for selective regulation with agents lacking intrinsic specificity. There are now a host of studies demonstrating how inhibitors of metabolism (e.g., glycolysis, glutamine metabolism, and fatty acid oxidation) can regulate immune responses and treat immune-mediated pathogenesis. In this brief review we detail how inhibitors of ...
Background: Constraint-based analysis of genome-scale metabolic models typically relies upon maximisation of a cellular objective function such as the rate or efficiency of biomass production. Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass per se. Results: An alternative objective function is presented, that is based upon maximising the correlation between experimentally measured absolute gene expression data and predicted internal reaction fluxes. Using quantitative transcriptomics data acquired from Saccharomyces cerevisiae cultures under two growth conditions, the method outperforms traditional approaches ...
Background: Constraint-based analysis of genome-scale metabolic models typically relies upon maximisation of a cellular objective function such as the rate or efficiency of biomass production. Whilst this assumption may be valid in the case of microorganisms growing under certain conditions, it is likely invalid in general, and especially for multicellular organisms, where cellular objectives differ greatly both between and within cell types. Moreover, for the purposes of biotechnological applications, it is normally the flux to a specific metabolite or product that is of interest rather than the rate of production of biomass per se. Results: An alternative objective function is presented, that is based upon maximising the correlation between experimentally measured absolute gene expression data and predicted internal reaction fluxes. Using quantitative transcriptomics data acquired from Saccharomyces cerevisiae cultures under two growth conditions, the method outperforms traditional approaches ...
De Martino, Daniele. "Scales and Multimodal Flux Distributions in Stationary Metabolic Network Models via Thermodynamics." Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 95, no. 6, American Institute of Physics, 2017, p. 062419, doi:10.1103/PhysRevE.95.062419 ...
Metabolic networks are extensively regulated to facilitate tissue-specific metabolic programs and robustly maintain homeostasis in response to dietary changes. Homeostatic metabolic regulation is achieved through metabolite sensing coupled to feedback regulation of metabolic enzyme activity or expression. With a wealth … Continue reading →. ...
Schilling, C.H., Letscher, D., and Palsson, B.Ø (2000) Journal of Theoretical Biology, Vol. 203, 229-248, Theory for the Systemic Definition of Metabolic Pathways and their use in Interpreting Metabolic Function from a Pathway-Oriented Perspective. Papin, J.A., Price, N.D., and Palsson, B.Ø (2002) Genome Research, Vol. 12, 1889-1900, Extreme Pathway Lengths and Reaction Participation in Genome-Scale Metabolic Networks. Price, N.D., Reed, J.L., and Palsson B.Ø (2004) Nature Reviews Microbiology, Vol. 2, 886 - 897, Genome-scale models of microbial cells: evaluating the consequences of constraints. ...
Cells respond to fatty acid exposure by metabolic reorganization and proliferation of peroxisomes. Described here is the development and application of a genome-wide screen to identify nonessential yeast genes necessary for efficient metabolism of myristic and oleic acids. Comparison of the resultant fitness data set with an integrated data set of genes transcriptionally responsive to fatty acids revealed very little overlap between the data sets. Furthermore, the fitness data set enriched for genes involved in peroxisome biogenesis and other processes related to cell morphology, whereas the expression data set enriched for genes related to metabolism. These data suggest that in response to fatty acid exposure, transcriptional control is biased towards metabolic reorganization, and structural changes tend to be controlled post-transcriptionally. They also suggest that fatty acid responsive metabolic networks are more robust than those related to cell structure. Statistical analyses of these and ...
5-HTP is the intermediate metabolite between the amino acid L-tryptophan and the neurotransmitter serotonin.It is extracted from the seed of an African plant (Griffonia simplicifolia). It supports a positive mood.Capsules are gluten free, Non-GMO, nut free, soy free, and vegan. One capsule contains 100 mg 5-HTP. Product Features 5-HTP is the intermediate metabolite between the [More] ...
Biological membranes constitute a chemical barrier to the environment and are thus the prerequisite for the establishment and maintenance of a controlled intracellular milieu, the cytoplasm. In eukaryotes, membranes are also responsible for the formation of chemically distinct intracellular compartments. The lipid bilayer membranes contain a great diversity of proteins that fulfill different functions and serve as an interface to the environment and between different compartments. Among these membrane proteins are receptors involved in signaling cascades and pathogen defense reactions, enzymes such as the apparatus for cell wall biosynthesis, and transporters responsible for the import and export of solutes and ions and the establishment of electrochemical gradients across membranes, thereby connecting the different metabolic pathways of the cellular compartments and organelles.
After adding core work to your daily routine, the next way you can take your fitness to the next level is to incorporate both types of cardio work: anaerobic and aerobic. Each one uses different metabolic pathways in our bodies, the first without oxygen and the second uses oxygen. I am going to explain why this is important and how to add each one to whatever kind of exercise you are currently doing.. Back in the 80s the emphasis was on aerobic exercise only. Now, research is supporting a mixture of both aerobic and anaerobic. It actually leads to more favorable effects on our hormones, including cortisol, insulin, glucagons and human growth hormone (HGH). This affects our energy levels, immune system, inflammation, as well as the way our body processes and stores sugar, protein and fat. Balancing aerobic and anaerobic training will allow your body to have more efficient metabolism.. Another reason to balance these two forms of working out is to better manage the end products of the pathways. ...
A wide range of research areas in bioinformatics, molecular biology and medicinal chemistry require precise chemical structure information about molecules and reactions, e.g. drug design, ligand docking, metabolic network reconstruction, and systems biology. Most available databases, however, treat chemical structures more as illustrations than as a datafield in its own right. Lack of chemical accuracy impedes progress in the areas mentioned above. We present a database of metabolites called BioMeta that augments the existing pathway databases by explicitly assessing the validity, correctness, and completeness of chemical structure and reaction information. The main bulk of the data in BioMeta were obtained from the KEGG Ligand database. We developed a tool for chemical structure validation which assesses the chemical validity and stereochemical completeness of a molecule description. The validation tool was used to examine the compounds in BioMeta, showing that a relatively small number of compounds
We developed a general approach for facilitating the reconstruction of plant metabolic networks from sequenced genomes or transcriptomes. Four components were created for the system: (1) PlantCyc, a pan-plant reference database of metabolic pathways and enzymes; (2) RESD, a reference enzyme sequence database containing protein sequences with literature-supported enzyme activities; (3) an enzyme sequence annotation pipeline that predicts enzyme functions from predicted protein sequences based on sequence similarity to RESD sequences; and (4) a modified pathway prediction procedure that uses both PlantCyc and MetaCyc as the reference for reconstructing single-species metabolic networks from the predicted enzymes. Using such a consensus approach will make it easier to interpret the results of cross-species metabolic comparisons. The individual components of the infrastructure can also be used on their own in a number of ways.. We applied the system to the sequenced genome of poplar (Poptr 1.1 ...
It is common to characterize metabolic models by the numbers of reactions and metabolites included, but these can be misleading measures without qualification. One reason is that there is a case during model building for adding more reactions than can be connected to the network. This will arise if a gene is annotated to an enzyme with a broad substrate range (e.g. alcohol dehydrogenase, EC 1.1.1.1), since it will not be clear while the model is incomplete as to which alcohols and aldehydes will be available by being produced or consumed by other enzymes in the network. A simple solution is to add more than is likely to be necessary and to remove at a later stage the ones that are not functional.. What criteria do we have for reactions that are non-functional? One test is where a metabolite is only involved in a single reaction in the network. In this case, the metabolite cannot reach a steady state because it is impossible to have its balanced production and consumption. Furthermore, the ...
ABSTRACT: BACKGROUND: Despite the availability of numerous complete genome sequences from E. coli strains, published genome-scale metabolic models exist only for two commensal E. coli strains. These models have proven useful for many applications, such as engineering strains for desired product formation, and we sought to explore how constructing and evaluating additional metabolic models for E. coli strains could enhance these efforts.$\backslash$n$\backslash$nRESULTS: We used the genomic information from 16 E. coli strains to generate an E. coli pangenome metabolic network by evaluating their collective 76,990 ORFs. Each of these ORFs was assigned to one of 17,647 ortholog groups including ORFs associated with reactions in the most recent metabolic model for E. coli K-12. For orthologous groups that contain an ORF already represented in the MG1655 model, the gene to protein to reaction associations represented in this model could then be easily propagated to other E. coli strain models. All ...
The PMN staff members work at the Carnegie Institution for Science in the Department of Plant Biology, located on the Stanford University campus. The PMN curators are involved in curating pathways from Arabidopsis into AraCyc, plus they also curate pathways from diverse species that can be entered into the appropriate species-specific metabolic databases, or directly into PlantCyc. In addition, all of the pathways supported by experimental evidence are entered into MetaCyc.. PMN staff members will also be involved in the generation of new species-specific metabolic pathway databases, such as PoplarCyc.. To send a general message to the PMN, please use our Feedback Form, but we also welcome messages to individual PMN staff members.. ...
68. Reliance upon the morphologic wie sollte man kamagra einnehmen of lesions on arteri- ograms also carries known hazards. The genes for components in the RAS, b-AR systems, and lipoprotein metabolic pathways are better characterized; hence, more trans- genic animals have been produced for testing drugs targeted at these compo- nents. Med.
Cyclic dinucleotides (CDNs) of bacterial and host origin mediate cytosolic immune responses through the stimulator of interferon genes (STING) signaling pathway, though evidence suggests alternative receptors exist. We identified the oxidoreductase RECON as a high-affinity host receptor specific for bacterial CDNs. RECONs enzyme activity was inhibited by bacterial CDNs, which bound in the substrate and cosubstrate sites. During bacterial infection of macrophages, RECON antagonized STING activation by acting as a molecular sink for CDNs. In hepatocytes, RECON negatively regulated NF-kappaB activation via its enzymatic activity. Loss of RECON resulted in increased NF-kappaB activation and reduced bacterial survival. Therefore, CDN inhibition of RECON promotes a proinflammatory, antibacterial state. We investigated the molecular pathways by which RECON controls inflammation and found that several branches of the arachidonic acid inflammatory cascade were involved. At the top of the cascade, free ...
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An international consortium of researchers are using the power of supercomputing to produce what they say is the most comprehensive virtual reconstruction of human metabolism available, which could help identify and create individualized treatments for a wide range of diseases. Recon 2 is based on work by University of California, San Diego, researchers and could help study and treat of cancer and diabetes, as well as psychiatric and neurodegenerative disorders. Metabolism is the conversion of food sources into energy. Metabolic imbalances can play a role in disease. In turn, genetics, environment, and nutrition play a role in metabolism. High-powered computing has enabled scientists to construct huge interactive databases of biological data regarding metabolic pathways and analyze the information they contain. UC San Diego researchers built Recon 1 in 2007, basing it on more than 3,300 known biochemical reactions documented in more than 50 years of metabolic research. Recon 2, by contrast, ...
The modeling of cellular metabolism has always been a popular topic among researchers. The existing examples are usually confined to a particular pathway or cellular process. However, the massive research efforts in genetic annotation of the last decade have expanded the field to incorporate genome scale models (GSMs), which include the entire cellular metabolism. Due to the lack of metabolic kinetic information, GSMs were largely implemented with the flux balance analysis (FBA) strategy, a steady-state based methodology absent of kinetic parameters. Although instrumental in predicting a number of experimental observations, FBA models have severe limitations. In my work, I sought to develop the necessary tools and methodologies to construct full kinetic GSMs. I have demonstrated that the metabolic modeling field has progressed in a way that a kinetic GSM can finally be developed. A number of experimental observations in both mammalian and algae cell lines have been correctly predicted by this ...
Current antibiotics, derived mainly from natural sources, inhibit a narrow spectrum of cellular processes, namely DNA replication, protein synthesis, and cell wall biosynthesis. With the worldwide explosion of drug resistance, there is renewed interest in the investigation of alternate essential cellular processes, including bacterial central metabolic pathways, as a drug target space for the next generation of antibiotics. However, the validation of targets in central metabolism is more complex, as essentiality of such targets can be conditional and/or contextual. Bearing in mind our enhanced understanding of prokaryotic central metabolism, a key question arises: can central metabolism be bacterias Achilles heel and a therapeutic target for the development of new classes of antibiotics? In this review, we draw lessons from oncology and attempt to address some of the open questions related to feasibility of targeting bacterial central metabolism as a strategy for developing new antibacterial ...
Each of the 371 databases of BioCyc describes the genome and metabolic pathways of a single organism. The BioCyc databases are divided into 3 tiers, based on their quality: Tier 1: 3 Databases, which have been created through intensive manual efforts and receive continuous updating: 1) *EcoCyc* describes the model organism Escherichia coli K-12. 2) *MetaCyc* describes metabolic pathways and enzymes for more than 1,500 organisms. 3) *BOCD* (BioCyc Open Compounds Database) is an open collection of chemical compound data from the BioCyc databases. BOCD includes metabolites, enzyme activators, inhibitors, and cofactors. Tier 2: 20 Databases, which were computationally generated and have undergone moderate curation. Tier 3: 349 Databases, which were also computationally generated, but have undergone no curation. Scientists can use the BioCyc Web site to visualize individual metabolic pathways or to view the complete metabolic map of an organism. The latter diagram can be used to analyze gene ...
Vitamin B12 aka Cobalamin is a water-soluble vitamin that plays a key role in several vital body functions and processes including in the central nervous system, cardiovascular system, endocrine system and in multiple metabolic pathways. It is best known for its role in the formation and maintenance of nerve and red blood cells. It also aids in the synthesis of DNA ...
Computational approaches to study biochemistry require machine accessible representations of biochemical knowledge. Based on the requirements for assembling, managing and analysing genome-scale constraint-based models of liver metabolism we developed a semantic data model for representation of liver biochemical knowledge. Adopting a consistent perspective on biochemical processes ...
5-HTP 50mg by Now Foods 30 Capsule 5-HTP 50mg 30 Capsule Product 5-HTP the intermediate metabolite between the amino acid L-tryptophan and serotonin is ...
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Week of May 29 - June 4 05.29.2017 Monday was Memorial Day, so we enjoyed a long, relaxing weekend! 05.30.2017 Tuesday, however, we got right back to work! The universal ribosome binding sites arrived from IDT. We began reaching out to experts for our Human Practices work. Work was started on metabolic pathway modeling and we found five potential universal promoter sequences! Additionally, we began looking into the lung mimic logistics; we contacted Drs. Breur and Kasinski about donating rat lungs from their laboratories to the project and made a spreadsheet of common genera of bacteria in the lung microbiome and the optimal culture conditions for each. At the end of the day, we performed a GFP transformation on E. coli to practice the protocol. 05.31.2017 On Wednesday, we had our first Human Practices meeting with Dr. Umberger, the Purdue Polytechnic Associate Dean for Engagement who teaches a class on the FDA regulatory procedures. She discussed with us some considerations we need to make in ...
Reactome is pathway database which provides intuitive bioinformatics tools for the visualisation, interpretation and analysis of pathway knowledge.
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J:190524 Shalom-Barak T, Zhang X, Chu T, Timothy Schaiff W, Reddy JK, Xu J, Sadovsky Y, Barak Y, Placental PPARgamma regulates spatiotemporally diverse genes and a unique metabolic network. Dev Biol. 2012 Dec 1;372(1):143-55 ...
Flow typing offers an alternative to the traditional Hindley-Milner approach to type inference. A key distinction is that variables may h...
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Enzymes What Are They? Thousands of enzymes are working every second to build and regenerate the body. They are constantly produced in the body and depend upon
import rpc class Monitor(rpc.Client): ## operation result handlers ## def handle_assign_aliases(self, oper, attrs): arch = attrs[info][arch] self.process_tag_add(arch:{}.format(arch), oper[process_id]) def handle_camera_query(self, oper, attrs): if attrs[cameras]: self.process_tag_add(has:camera, oper[process_id]) else: self.process_tag_remove(has:camera, oper[process_id]) def handle_audio_query(self, oper, attrs): if attrs[devices]: self.process_tag_add(has:audio, oper[process_id]) else: self.process_tag_remove(has:audio, oper[process_id]) ## event handlers ## def on_process_added(self, event): proc_id = event[data][id] # queue some recon operations self.operation_execute(recon, assign_aliases, proc_id) self.operation_execute(recon, audio_query, proc_id) self.operation_execute(recon, camera_query, proc_id) def on_operation_updated(self, event): oper_id = event[data][id] oper = self.operation_get(oper_id) # filter if oper[state] != finished: ...
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Masclaux-Daubresse C, Reisdorf-Cren M, Pageau K, Lelandais M, Grandjean O, Kronenberger J, Valadier MH, Feraud M, Jouglet T, Suzuki ...
Masclaux-Daubresse C, Reisdorf-Cren M, Pageau K, Lelandais M, Grandjean O, Kronenberger J, Valadier MH, Feraud M, Jouglet T, Suzuki ...
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Bacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy,
BACKGROUND: Infections with Salmonella cause significant morbidity and mortality worldwide. Replication of Salmonella typhimurium inside its host cell is a model system for studying the pathogenesis of intracellular bacterial infections. Genome-scale modeling of bacterial metabolic networks provides a powerful tool to identify and analyze pathways required for successful intracellular replication during host-pathogen interaction.. RESULTS: We have developed and validated a genome-scale metabolic network of Salmonella typhimurium LT2 (iRR1083). This model accounts for 1,083 genes that encode proteins catalyzing 1,087 unique metabolic and transport reactions in the bacterium. We employed flux balance analysis and in silico gene essentiality analysis to investigate growth under a wide range of conditions that mimic in vitro and host cell environments. Gene expression profiling of S. typhimurium isolated from macrophage cell lines was used to constrain the model to predict metabolic pathways that ...
Steady-state (13)C metabolic flux analysis (MFA) is currently the experimental method of choice for generating flux maps of the compartmented network of primary metabolism in heterotrophic and mixotrophic plant tissues. While statistically robust protocols for the application of steady-state MFA to plant tissues have been developed by several research groups, the implementation of the method is still far from routine. The effort required to produce a flux map is more than justified by the information that it contains about the metabolic phenotype of the system, but it remains the case that steady-state MFA is both analytically and computationally demanding. This article provides an overview of principles that underpin the implementation of steady-state MFA, focusing on the definition of the metabolic network responsible for redistribution of the label, experimental considerations relating to data collection, the modelling process that allows a set of metabolic fluxes to be deduced from the labelling
Metabolic flux analysis (MFA) plays a central role in metabolic engineering and systems biology [1]. Metabolic fluxes most closely reflect the underlying metabolic phenotype, whereas other omics approaches only yield a sense of metabolic capacities (transcriptomics/proteomics) or thermodynamic driving forces (metabolomics). Metabolic flux analysis is particular important in rational strain engineering, where we specifically seek to manipulate the metabolic phenotype.. Due to the high complexity of the examined metabolic network, flux analysis typically involves the use of a stoichiometric model, in which the metabolic reactions available to the cell are parameterized before the fluxes are estimated from experimental data [2]. State-of-art flux analysis today includes the use of stable isotopes to overcome problems such as incomplete resolution of important cellular pathways or the need to rely on stoichiometric parameters with high uncertainty such as ATP yield (Yx/ATP) or P/O ratio which are ...
TY - JOUR. T1 - Computational Flux Balance Analysis Predicts that Stimulation of Energy Metabolism in Astrocytes and their Metabolic Interactions with Neurons Depend on Uptake of K+ Rather than Glutamate. AU - DiNuzzo, Mauro. AU - Giove, Federico. AU - Maraviglia, Bruno. AU - Mangia, Silvia. PY - 2017/1/1. Y1 - 2017/1/1. N2 - Brain activity involves essential functional and metabolic interactions between neurons and astrocytes. The importance of astrocytic functions to neuronal signaling is supported by many experiments reporting high rates of energy consumption and oxidative metabolism in these glial cells. In the brain, almost all energy is consumed by the Na+/K+ ATPase, which hydrolyzes 1 ATP to move 3 Na+ outside and 2 K+ inside the cells. Astrocytes are commonly thought to be primarily involved in transmitter glutamate cycling, a mechanism that however only accounts for few % of brain energy utilization. In order to examine the participation of astrocytic energy metabolism in brain ion ...
Implementing light‐regulated constraints and basic environmental exchange constraints (Supplementary Table S6) yielded photoautotrophic, heterotrophic, and mixotrophic models from iRC1080. We simulated various growth conditions (Supplementary Table S7) and all gene knockouts for which phenotypes have been published and are assessable in our network (Supplementary Table S8) to validate the predictive ability of the models. All 30 validations involving environmental parameters displayed very close agreement with experimental results (Supplementary Table S7). Of particular note is the ability of our photosynthetic model in sunlight to accurately recapitulate O2‐PAR (photosynthetically active radiation) energy conversion efficiency, predicting an efficiency of 2% compared with the experimental result (Greenbaum, 1988) of 1.3-4.5%. Of the 14 gene knockouts simulated, 7 were partially or completely validated relative to experimental results (Supplementary Table S8). The unconfirmed gene knockout ...
Genome-Scale reconstruction of the transcriptional and translational machinery in Escherichia coli: A knowledge-database and its mathematical formulation", 1st Bioinfo Expo, San Diego, February 2009, Ines Thiele, Neema Jamshidi, Ronan M.T. Fleming, and Bernhard Ø. Palsson. "Genome-Scale reconstruction of the transcriptional and tr anslational machinery in Escherichia coli: A knowledge-database and its mathematical formulation", 16th Annual International Conference on Microbial Genomes, Lake Arrowhead, September 2008, Ines Thiele, Neema Jamshidi, Ronan M.T. Fleming, and Bernhard Ø. Palsson. "From Network to Function: Systematic annotation of gene function using metabolic network reconstructions.", Workshop on the Biological Annotation of Novel Proteins, San Diego, March 2008, Ines Thiele and Bernhard Ø. Palsson.. "Genome-Scale reconstruction of the transcriptional and translational machinery in Escherichia coli: A knowledge-database and its mathematical formulation", ICSB 2007, Long Beach, ...
Drug side effects cause a significant clinical and economic burden. However, mechanisms of drug action underlying side effect pathogenesis remain largely unknown. Here, we integrate pharmacogenomic and clinical data with a human metabolic network and find that non-pharmacokinetic metabolic pathways dysregulated by drugs are linked to the development of side effects. We show such dysregulated metabolic pathways contain genes with sequence variants affecting side effect incidence, play established roles in pathophysiology, have significantly altered activity in corresponding diseases, are susceptible to metabolic inhibitors and are effective targets for therapeutic nutrient supplementation. Our results indicate that metabolic dysregulation represents a common mechanism underlying side effect pathogenesis that is distinct from the role of metabolism in drug clearance. We suggest that elucidating the relationships between the cellular response to drugs, genetic variation of patients and cell ...
Yeasts degrade glucose through different metabolic pathways, where the choice of the pathway is dependent on the nature of the limitation in the various substrates. When oxygen is limiting in addn. to glucose, yeasts often grow according to a mixt. of oxidative and reductive metab. Oxygen may be limiting either by supply or by inherent biol. restrictions such as the respiratory bottleneck in Saccharomyces cerevisiae or by both. A unified model incorporating both supply and biol. limitations is proposed for the quant. prediction of growth rates, consumption and prodn. rates, as well as key metabolite concns. during mixed oxidoreductive metab. occurring as a result of such oxygen limitations. This simple unstructured model can be applied to different yeast strains while at the same time requiring a min. no. of measured parameters. Estimators are utilized in order to predict the presence of supply-side or biol. limitations. The values of these estimators also characterize the relative importance of ...
In this article, we combine multi-level profiling methods with bioinformatic and theoretical modeling approaches to characterize the molecular repertoire of C. reinhardtii under reference conditions. We analyzed and integrated (i) a combination of database resources, such as existing genome annotations from JGI v3.1, EST collections, six-frame translation of the genomic sequence, protein domain scanning, and pathway annotation information; (ii) systematic high-resolution shotgun proteomics for high-throughput protein identification; (iii) systematic metabolite profiling and projection of identified metabolites to the reconstructed metabolic draft network in Chlamydomonas on the basis of existing gene annotation; and (iv) structural modeling of the reconstructed metabolic network to identify minimum extension pathways on the basis of the presence of identified metabolites.. MapMan classification of the predicted Chlamydomonas protein set and comparison with other organisms yielded information for ...
We analyzed the carbon fluxes in the central metabolism ofGeobacter metallireducens strain GS-15 using 13C isotopomer modeling.Acetate labeled in the 1st or 2nd position was the sole carbon source,and Fe-NTA was the sole terminal electron acceptor. The measured labeledacetate uptake rate was 21 mmol/gdw/h in the exponential growth phase.The resulting isotope labeling pattern of amino acids allowed an accuratedetermination of the in vivo global metabolic reaction rates (fluxes)through the central metabolic pathways using a computational isotopomermodel. The tracer experiments showed that G. metallireducens containedcomplete biosynthesis pathways for essential metabolism, and this strainmight also have an unusual isoleucine biosynthesis route (usingacetyl-CoA and pyruvate as the precursors). The model indicated that over90 percent of the acetate was completely oxidized to CO2 via a completetricarboxylic acid (TCA) cycle while reducing iron. Pyruvate carboxylaseand phosphoenolpyruvate carboxykinase were
Read "Integrated bioinformatics to decipher the ascorbic acid metabolic network in tomato, Plant Molecular Biology" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips.
Division of Cardiovascular Medicine Professor of Clinical Medicine State University of New York Downstate Medical Center Brooklyn, NY, ...
The utility of flux balance models depends on the extent to which realistic flux distributions can be predicted from them. While the biomass constraint has proved effective in predicting fluxes in microbes (Feist and Palsson, 2010), it is less clear whether this constraint provides sufficient bounds for flux prediction in more complex organisms such as plants. Even though it has been previously shown that there is significant agreement in the flux predictions for heterotrophic plant metabolism and those estimated by 13C-MFA (Williams et al., 2010; Hay and Schwender, 2011), certain fluxes, such as those in the central pathways of glycolysis, the TCA cycle, and particularly the OPPP, were not well matched.. The main issue is that these central pathways, in addition to providing carbon skeletons for the synthesis of biomass components, are also the main routes for energy transformation. Although synthesis of biomass consumes energy, there are other substantial energy drains in the cell, including ...
Introduction to methods for modeling and analyzing biological networks such as genetic regulatory networks, metabolic networks, and signal transduction networks. A particular emphasis will be given to methods inspired by models used by engineers for circuit analysis. Other topics include: stochastic analysis using Monte Carlo methods, differential equation models, Bayesian network models, flux balance analysis, learning methods, pathway databases, and synthesized gene ...
Within bioprocesses, organic compounds are converted by either isolated enzymes or whole-cell biocatalysts. Biotransformations can be differentiated into enzymatic and metabolic bioconversions. A bioprocess is based on a biological catalyst that is used to conduct a chemical reaction leading to a defined product. In the early times of bioprocess engineering, water was used principally for the reaction medium, because it was assumed that a higher stability and activity can be reached in the natural medium of enzymes. Metabolic bioconversions need the metabolic system of living and growing microorganisms, e.g., bacteria, yeasts, or fungi. A goal of systems biology is predictive metabolic engineering, where genes within metabolic pathways are purposefully amplified or deleted based on the consideration of the metabolic network as an entirety. Microreaction technology is an interdisciplinary field combining natural science and engineering. Bioprocess engineering must focus on downstream processing in
The Csr system was recently demonstrated to be a major controller of upper glycolysis fluxes (16), but its involvement in metabolic adaptation is less clear in the literature. CsrA is known to positively regulate glycolytic genes and negatively regulate gluconeogenic genes (15, 16). A study of the BarA/UvrY two-component system during the metabolic switch suggested that the Csr system is crucial for efficient adaptation between different metabolic pathways (2). Here, we showed that gene expression in the CCM (glycolysis, gluconeogenesis, the pentose phosphate pathway, and the tricarboxylic acid cycle) did not present strong discrepancies between the Csr system mutants during the acetate consumption phase, in deep contrast to the situation during glucose consumption. It will be awkward to totally rule out any control of these genes by CsrA, since regulation could be at the posttranscriptional level. The control by CsrA could also be counterbalanced by its higher sequestration by CsrB, since the ...
Kotte, Oliver; Volkmer, Benjamin; Radzikowski, Jakub L; Heinemann, Matthias (2014). Phenotypic bistability in Escherichia colis central carbon metabolism. Molecular Systems Biology, 10:736.. Zampar, Guillermo G; Kümmel, Anne; Ewald, Jennifer; Jol, Stefan; Niebel, Bastian; Picotti, Paola; Aebersold, Ruedi; Sauer, Uwe; Zamboni, Nicola; Heinemann, Matthias (2013). Temporal system-level organization of the switch from glycolytic to gluconeogenic operation in yeast. Molecular Systems Biology, 9:online.. Jol, Stefan J; Kümmel, Anne; Terzer, Marco; Stelling, Jörg; Heinemann, Matthias (2012). System-Level Insights into Yeast Metabolism by Thermodynamic Analysis of Elementary Flux Modes. PLoS Computational Biology, 8(3):e1002415.. Huberts, Daphne H E W; Niebel, Bastian; Heinemann, Matthias (2011). A flux-sensing mechanism could regulate the switch between respiration and fermentation. FEMS Yeast Research, 12(2):118-128.. Costenoble, Roeland; Picotti, Paola; Reiter, Lukas; Stallmach, Robert; ...
The effect can be explained; as the yeast being facultative anaerobes can produce energy using two different metabolic pathways. While the oxygen concentration is low, the product of glycolysis, pyruvate, is turned into ethanol and carbon dioxide, and the energy production efficiency is low (2 moles of ATP per mole of glucose). If the oxygen concentration grows, pyruvate is converted to acetyl CoA that can be used in the citric acid cycle, which increases the efficiency to 32 moles of ATP per mole of glucose. Therefore, about 16 times as much glucose must be consumed anaerobically as aerobically to yield the same amount of ATP.[2]. Under anaerobic conditions, the rate of glucose metabolism is faster, but the amount of ATP produced (as already mentioned) is smaller. When exposed to aerobic conditions, the ATP and Citrate production increases and the rate of glycolysis slows, because the ATP and citrate produced act as allosteric inhibitors for phosphofructokinase 1, the third enzyme in the ...
Tuberculosis is a notorious disease responsible for the deaths of 1.4 million people worldwide. A third of the worlds population is infected with Mycobacterium tuberculosis, the bacterium causing the disease. The increase of multi drug-resistant strains worsens the situation, and the World Health Organization has declared tuberculosis to be a global emergency. The bacterium envelopes itself with a unique set of very long-chain lipids that play an important role in virulence and drug resistance. Therefore enzymes involved in lipid metabolism are putative drug targets. To allow entry into different metabolic pathways and transmembrane transport, fatty acids have to be activated. This is done primarily by fatty acyl-CoA synthetases (ACSs). We identified an ACS possibly involved in the bacteriums virulence and solved its structure. Structural interpretation combined with previously reported data gives us insights into the details of its function. This enzyme is known to harbor lipid substrates ...
L-arginine and L-citrulline are chemically related amino acids, and they are frequently converted back and forth in your cells. However, the conversion from L-arginine to L-citrulline requires a different metabolic pathway than the conversion from L-citrulline to L-arginine. The conversion of citrulline to arginine utilizes a pathway that first produces ornithine. The arginine-to-citrulline conversion is more direct and produces a molecule of nitric oxide. One of nitric oxides many effects is to dilate your arteries, which improves blood flow to exercising muscles. It is believed that supplementation with L-citrulline increases arginine synthesis, which in turn enhances nitric oxide production.. Considerations. Supplementation with L-citrulline malate could improve exercise performance through two mechanisms. By facilitating removal of ammonia from exercising muscles, citrulline enhances cellular metabolism, improves energy production and extends exercise times. By increasing nitric oxide ...
Montana State University. My work here is part of a broader ongoing careful study of Escherichia coli metabolism intended to improve predictive interpretation of biological networks. In this position I worked with a doctoral student providing my expertise in microbiology and proteomics. This project includes characterization of overflow metabolism, protein expression and biomass composition of cells grown in chemostats under iron, carbon and nitrogen limitation. In addition, I worked with another graduate student on efforts to improve the labs computational capability for the calculation of the elementary flux mode set for biological networks. Several papers from this work are in preparation. I also managed lab inventory by ordering supplies. Dr. Philip Stewart, Center for Biofilm Engineering June 2007 - June 2009 ...
The structure of N-linked glycosylation is a very important quality attribute for therapeutic monoclonal antibodies. Different carbon sources in cell culture media, such as mannose and galactose, have been reported to have different influences on the glycosylation patterns. Accurate prediction and control of the glycosylation profile are important for the process development of mammalian cell cultures. In this study, a mathematical model, that we named Glycan Residues Balance Analysis (GReBA), was developed based on the concept of Elementary Flux Mode (EFM), and used to predict the glycosylation profile for steady state cell cultures. Experiments were carried out in pseudo-perfusion cultivation of antibody producing Chinese Hamster Ovary (CHO) cells with various concentrations and combinations of glucose, mannose and galactose. Cultivation of CHO cells with mannose or the combinations of mannose and galactose resulted in decreased lactate and ammonium production, and more matured glycosylation ...
Deluxe Revised RECON® RPG - RECON is set in a fictional world that parallels that of 20th Century Earth and focuses on the realistic military combat
Author SummaryCellular systems comprise many diverse components and component interactions spanning signal transduction, transcriptional regulation, and metabolism. Although signaling, metabolic, and regulatory activities are often investigated independently of one another, there is growing evidence that considerable interplay occurs among them, and that the malfunctioning of this interplay is associated with disease. The computational analysis of integrated networks has been challenging because of the varying time scales involved as well as the sheer magnitude of such systems (e.g., the numbers of rate constants involved). To this end, we developed a novel computational framework called integrated dynamic flux balance analysis (idFBA) that generates quantitative, dynamic predictions of species concentrations spanning signaling, regulatory, and metabolic processes. idFBA extends an existing approach called flux balance analysis (FBA) in that it couples
MetaCyc has been designed for multiple types of uses. It is often used as an extensive online encyclopedia of metabolism. In addition, MetaCyc is used as a reference data set for computationally predicting the metabolic network of organisms from their sequenced genomes; it has been used to perform pathway predictions for thousands of organisms, including those in the BioCyc Database Collection. MetaCyc is also used in metabolic engineering and metabolomics research. MetaCyc includes mini reviews for pathways and enzymes that provide background information as well as relevant literature references. It also provides extensive data on individual enzymes, describing their subunit structure, cofactors, activators and inhibitors, substrate specificity, and, when available, kinetic constants. MetaCyc data on metabolites includes chemical structures, predicted Standard energy of formation, and links to external databases. Reactions in MetaCyc are presented in a visual display that includes the ...
Scientists from Pacific Northwest National Laboratory (PNNL) and the University of California-San Diego (UCSD) have completed the most advanced genome-scale metabolic model for a microbe in the domain Archaea, one of the three domains of life on Earth.
Herrgard, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., Bluthgen, N., Borger, S., Costenoble, R., Heinemann, M., Hucka, M., Le Novere, N., Li, P., Liebermeister, W., Mo, M. L., Oliveira, A. P., Petranovic, D., Pettifer, S., Simeonidis, E., Smallbone, K., Spasic, I., Weichart, D., Brent, R., Broomhead, D. S., Westerhoff, H. V., Kirdar, B., Penttila, M., Klipp, E., Palsson, B. O., Sauer, U., Oliver, S. G., Mendes, P., Nielsen, J., Kell, D. B. (2008). "A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology." Nat Biotechnol 26:1155-1160.18846089 ...
Herrgard, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., Bluthgen, N., Borger, S., Costenoble, R., Heinemann, M., Hucka, M., Le Novere, N., Li, P., Liebermeister, W., Mo, M. L., Oliveira, A. P., Petranovic, D., Pettifer, S., Simeonidis, E., Smallbone, K., Spasic, I., Weichart, D., Brent, R., Broomhead, D. S., Westerhoff, H. V., Kirdar, B., Penttila, M., Klipp, E., Palsson, B. O., Sauer, U., Oliver, S. G., Mendes, P., Nielsen, J., Kell, D. B. (2008). "A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology." Nat Biotechnol 26:1155-1160.18846089 ...
The explosive growth of microbiome research has yielded great quantities of data. These data provide us with many answers, but raise just as many questions. 16S rDNA-the backbone of microbiome analyses-allows us to assess α-diversity, β-diversity, and microbe-microbe associations, which characterize the overall properties of an ecosystem. However, we are still unable to use 16S rDNA data to directly assess the microbe-microbe and microbe-environment interactions that determine the broader ecology of that system. Thus, properties such as competition, cooperation, and nutrient conditions remain insufficiently analyzed. Here, we apply predictive community metabolic models of microbes identified with 16S rDNA data to probe the ecology of microbial communities. We developed a methodology for the large-scale assessment of microbial metabolic interactions (MMinte) from 16S rDNA data. MMinte assesses the relative growth rates of interacting pairs of organisms within a community metabolic network and whether
Table 18a. Drug Interactions Between Protease Inhibitors and Other Drugs. This table provides known or predicted information regarding PK interactions between PIs and non-ARV drugs. When information is available, interactions for specific PK-boosted (with either RTV or COBI) and unboosted ATV are listed separately. The term "All PIs" refers to both unboosted ATV and PIs boosted with either RTV or COBI, except the PIs noted below. For interactions between ARV agents and for dosing recommendations, refer to Tables 18c, 19a, and 19b. Recommendations for managing a particular drug interaction may differ depending on whether a new ARV drug is being initiated in a patient on a stable concomitant medication or if a new concomitant medication is being initiated in a patient on a stable ARV regimen. The magnitude and significance of drug interactions are difficult to predict when several drugs with competing metabolic pathways are prescribed concomitantly. Note: Fosamprenavir (FPV), indinavir (IDV), ...