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Do we have the complete metabolic map for humans? Do we know from the genome what enzimes are expressed and what each enzime does?
As Armatus notes, we don't know ALL human metabolites and the enzymes + metabolic reactions that produce them. Identifying small molecules is hard and metabolism is dynamic, it depends on what is being fed into the system.
The virtual metabolic human (VMH) is an attempt at compiling the information that we do know. You can read their paper here. The human build currently includes (and links together);
- 13 543 metabolic reactions
- 4138 metabolites
- 3695 genes (enzymes)
It also attempts to compile all known metabolic reactions that result from the gut microbiome, nutritional metabolic pathways and genetic diseases that alter metabolism. Everything is linked via stable identifiers which is a real challenge.
The human metabolic database (HMDB) is also very good. It contains more metabolites than the VMH as it includes many partially or poorly characterised metabolites. The HMDB is really useful if you are working to identify metabolites while the VMH focuses on how they connect to one another.
Roche has a pretty amazing map of metabolic pathways in the cell. You can find it here. During my Bachelor degree I contacted Roche and the mailed one to me for free (in a large poster format).
The answer is not yet. The sequencing of the human genome, considering its size (@ 3.2 bbp), and contrary to what's been hyped in media, is not the end of the quest for understanding the human body. On the contrary, it is only the first and the simplest step in the elucidation of the intricate workings of the human body. Even though a lot has been accomplished already, all of those tons of accomplishments still barely scratches the surface.
I can safely estimate that human genome sequencing represents less than 1% of the work that lies ahead, enormous work required to make sense of the whole base pair sequence. Each succeeding step multiplies the complexity exponentially, and so, even considering the exponential advancement in technology, it is again safe to say that at the rate things are going, the complete elucidation of the human metabolome and metabolic pathways cannot be accomplished within the next 50 years.
To get an idea of the complexities involved, one only has to consider the next step after genome sequencing; the next step is to identify all the human genes making up the coding sections of the genome, which account for less than about 2% of the human genome. Most genes do not transcribe into RNA in a straightforward manner; introns (which separate the exons that hold the coding sections), have to be spliced out; then the exons making up the code for the protein must be joined together. All of that accountws for less than 2 % of the human genome. The rest is non-coding DNA, the function of most of which hasn't yet been figured out.
A comprehensive metabolic map for production of bio-based chemicals
IMAGE: Bio-based chemicals production through biological and chemical routes. This metabolic map describes representative chemicals that can be produced either by biological and/or chemical means. Red arrows represent chemical routes and. view more
A KAIST research team completed a metabolic map that charts all available strategies and pathways of chemical reactions that lead to the production of various industrial bio-based chemicals.
The team was led by Distinguished Professor Sang Yup Lee, who has produced high-quality metabolic engineering and systems engineering research for decades, and made the hallmark chemicals map after seven years of studies.
The team presented a very detailed analysis on metabolic engineering for the production of a wide range of industrial chemicals, fuels, and materials. Surveying the current trends in the bio-based production of chemicals in industrial biotechnology, the team thoroughly examined the current status of industrial chemicals produced using biological and/or chemical reactions.
This comprehensive map is expected to serve as a blueprint for the visual and intuitive inspection of biological and/or chemical reactions for the production of interest from renewable resources. The team also compiled an accompanying poster to visually present the synthetic pathways of chemicals in the context of their microbial metabolism.
As metabolic engineering has become increasing powerful in addressing limited fossil resources, climate change, and other environmental issues, the number of microbially produced chemicals using biomass as a carbon source has increased substantially. The sustainable production of industrial chemicals and materials has been explored with micro-organisms as cell factories and renewable nonfood biomass as raw materials for alternative petroleum. The engineering of these micro-organism has increasingly become more efficient and effective with the help of metabolic engineering - a practice of engineering using the metabolism of living organisms to produce a desired metabolite.
With the establishment of systems metabolic engineering - the integration of metabolic engineering with tools and strategies from systems biology, synthetic biology and evolutionary engineering - the speed at which micro-organisms are being engineered has reached an unparalleled pace.
In order to evaluate the current state at which metabolically engineered micro-organisms can produce a large portfolio of industrial chemicals, the team conducted an extensive review of the literature and mapped them out on a poster. This resulting poster, termed the bio-based chemicals map, presents synthetic pathways for industrial chemicals, which consist of biological and/or chemical reactions.
Industrial chemicals and their production routes are presented in the context of central carbon metabolic pathways as these key metabolites serve as precursors for the chemicals to be produced. The resulting biochemical map allows the detection and analysis of optimal synthetic pathways for a given industrial chemical. In addition to the poster, the authors have compiled a list of chemicals that have successfully been produced using micro-organisms and a list of the corresponding companies producing them commercially. This thorough review of the literature and the accompanying analytical summary will be an important resource for researchers interested in the production of chemicals from renewable biomass sources.
Metabolically engineered micro-organisms have already made a huge contribution toward the sustainable production of chemicals using renewable resources. Professor Lee said he wanted a detailed survey of the current state and capacity of bio-based chemicals production.
"We are so excited that this review and poster will expand further discussion on the production of important chemicals through engineered micro-organisms and also combined biological and chemical means in a more sustainable manner," he explained.
This work was supported by the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biofineries from the Ministry of Science and ICT through the National Research Foundation of Korea.
For further information, Distinguished Professor Sang Yup Lee of the Department of Chemical and Biomolecular Engineering at KAIST, Tel: +82-42-350-3930.
Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.
Metabolism And Its Integration (With Diagram)
Hundreds of reactions simultaneously take place in a living cell, in a well-organized and integrated manner. The entire spectrum of chemical reactions, occurring in the living system, is collectively referred to as metabolism.
A metabolic pathway (or metabolic map) constitutes a series of enzymatic reactions to produce specific products. The term metabolite is applied to a substrate or an intermediate or a product in the metabolic reactions.
Introduction to Metabolism:
Metabolism is broadly divided into two categories (Fig. 67.1).
The degradative processes concerned with the breakdown of complex molecules to simpler ones, with a concomitant release of energy.
The biosynthetic reactions involving the formation of complex molecules from simple precursors. A clear demarcation between catabolism and anabolism is rather difficult, since there are several intermediates common to both the processes.
The very purpose of catabolism is to trap the energy of the biomolecules in the form of ATP and to generate the substances (precursors) required for the synthesis of complex molecules. Catabolism occurs in three stages (Fig. 67.2).
1. Conversion of complex molecules into their building blocks:
Polysaccharides are broken down to monosaccharide’s, lipids to free fatty acids and glycerol, and proteins to amino acids.
2. Formation of simple intermediates:
The building blocks produced in stage (1) are degraded to simple intermediates such as pyruvate and acetyl CoA. These intermediates are not readily identifiable as carbohydrates, lipids or proteins. A small quantity of energy (as ATP) is captured in stage 2.
3. Final oxidation of acetyl CoA:
Acetyl CoA is completely oxidized to CO2, liberating NADH and FADH2 that finally get oxidized to release large quantity of energy (as ATP). Krebs cycle (or citric acid cycle) is the common metabolic pathway involved in the final oxidation of all energy-rich molecules. This pathway accepts the carbon compounds (pyruvate, succinate etc.) derived from carbohydrates, lipids or proteins.
For the synthesis of a large variety of complex molecules, the starting materials are relatively few. These include pyruvate, acetyl CoA and the intermediates of citric acid cycle. Besides the availability of precursors, the anabolic reactions are dependent on the supply of energy (as ATP or GTP) and reducing equivalents (as NADPH + H + ).
The anabolic and catabolic pathways are not reversible and operate independently. As such, the metabolic pathways occur in specific cellular locations (mitochondria, microsomes etc.) and are controlled by different regulatory signals.
The terms—intermediary metabolism and energy metabolism—are also in use. Intermediary metabolism refers to the entire range of catabolic and anabolic reactions, not involving nucleic acids. Energy metabolism deals with the metabolic pathways concerned with the storage and liberation of energy.
Types of Metabolic Reactions:
The biochemical reactions are mainly of four types:
3. Rearrangement and isomerization.
4. Make and break of carbon-carbon bonds.
These reactions are catalysed by specific enzymes—more than 2,000 known so far.
Methods Employed to Study Metabolism:
The metabolic reactions do not occur in isolation. They are interdependent and integrated into specific series that constitute metabolic pathways. It is, therefore, not an easy task to study metabolisms. Fortunately, the basic metabolic pathways in most organisms are essentially identical. Several methods are employed to elucidate biochemical reactions and the metabolic pathways.
These experimental approaches may be broadly divided into 3 categories:
1. Use of whole organisms or its components.
2. Utility of metabolic probes.
3. Application of isotopes.
The actual methods employed may be either in vivo (in the living system) or in vitro (in the test tube) or, more frequently, both.
1. Use of whole organism or its components:
(a) Whole organisms: Glucose tolerance test (GTT).
(b) Isolated organs, tissue slices, whole cells, subcellular organelles etc., to elucidate biochemical reactions and metabolic pathways.
2. Utility of metabolic probes:
Two types of metabolic probes are commonly used to trace out biochemical pathways. These are metabolic inhibitors and mutations.
3. Application of isotopes.
Integration of Metabolism:
Metabolism is a continuous process, with thousands of reactions, simultaneously occurring in the living cell. However, biochemists prefer to present metabolism in the form of reactions and metabolic pathways. This is done for the sake of convenience in presentation and understanding. We have learnt the metabolism of carbohydrates, lipids and amino acids. We shall now consider the organism as a whole and integrate the metabolism with particular reference to energy demands of the body organism.
Energy Demand and Supply:
The organisms possess variable energy demands hence the supply (input) is also equally variable. The consumed metabolic fuel may be burnt (oxidized to CO2 and H2O) or stored to meet the energy requirements as per the body needs. ATP serves as the energy currency of the cell in this process (Fig. 67.21).
The humans possess enormous capacity for food consumption. It is estimated that one can consume as much as 100 times his/her basal requirements! Obesity, a disorder of over nutrition mostly prevalent in affluent societies, is primarily a consequence of overconsumption.
Integration of Major Metabolic Pathways of Energy Metabolism:
An overview of the interrelationship between the important metabolic pathways, concerned with fuel metabolism depicted in Fig. 67.22, is briefly described here.
The degradation of glucose to pyruvate (lactate under anaerobic condition) generates 8 ATP. Pyruvate is converted to acetyl CoA.
2. Fatty acid oxidation:
Fatty acids undergo sequential degradation with a release of 2-carbon fragment, namely acetyl CoA. The energy is trapped in the form of NADH and FADH2.
3. Degradation of amino acids:
Amino acids, particularly when consumed in excess than required for protein synthesis, are degraded and utilized to meet the fuel demands of the body. The glucogenic amino acids can serve as precursors for the synthesis of glucose via the formation of pyruvate or intermediates of citric acid cycle. The ketogenic amino acids are the precursors for acetyl CoA.
Acetyl CoA is the key and common metabolite, produced from different fuel sources (carbohydrates, lipids, amino acids). Acetyl CoA enters citric acid cycle and gets oxidized to CO2. Thus, citric acid cycle is the final common metabolic pathway for the oxidation of all foodstuffs. Most of the energy is trapped in the form of NADH and FADH2.
5. Oxidative phosphorylation:
The NADH and FADH2, produced in different metabolic pathways, are finally oxidized in the electron transport chain (ETC). The ETC is coupled with oxidative phosphorylation to generate ATP.
6. Hexose monophosphate shunt:
This pathway is primarily concerned with the liberation of NADPH and ribose sugar. NADPH is utilized for the biosynthesis of several compounds, including fatty acids. Ribose is an essential component of nucleotides and nucleic acids (note—DNA contains deoxyribose).
The synthesis of glucose from non-carbohydrate sources constitutes gluconeogenesis. Several compounds (e.g. pyruvate, glycerol, amino acids) can serve as precursors for gluconeogenesis.
8. Glycogen metabolism:
Glycogen is the storage form of glucose, mostly found in liver and muscle. It is degraded (glycogenolysis) and synthesized (glycogenesis) by independent pathways. Glycogen effectively serves as a fuel reserve to meet body needs, for a brief period (between meals).
Regulation of Metabolic Pathways:
The metabolic pathways, in general, are controlled by four different mechanisms:
1. The availability of substrates
2. Covalent modification of enzymes
4. Regulation of enzyme synthesis.
The details of these regulatory processes are discussed under the individual metabolic pathways.
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A metabolic reconstruction provides a highly mathematical, structured platform on which to understand the systems biology of metabolic pathways within an organism.  The integration of biochemical metabolic pathways with rapidly available, annotated genome sequences has developed what are called genome-scale metabolic models. Simply put, these models correlate metabolic genes with metabolic pathways. In general, the more information about physiology, biochemistry and genetics is available for the target organism, the better the predictive capacity of the reconstructed models. Mechanically speaking, the process of reconstructing prokaryotic and eukaryotic metabolic networks is essentially the same. Having said this, eukaryote reconstructions are typically more challenging because of the size of genomes, coverage of knowledge, and the multitude of cellular compartments.  The first genome-scale metabolic model was generated in 1995 for Haemophilus influenzae.  The first multicellular organism, C. elegans, was reconstructed in 1998.  Since then, many reconstructions have been formed. For a list of reconstructions that have been converted into a model and experimentally validated, see http://sbrg.ucsd.edu/InSilicoOrganisms/OtherOrganisms.
|Organism||Genes in Genome||Genes in Model||Reactions||Metabolites||Date of reconstruction||Reference|
|Haemophilus influenzae||1,775||296||488||343||June 1999|||
|Escherichia coli||4,405||660||627||438||May 2000|||
|Saccharomyces cerevisiae||6,183||708||1,175||584||February 2003|||
|Mus musculus||28,287||473||1220||872||January 2005|||
|Homo sapiens||21,090 ||3,623||3,673||--||January 2007|||
|Mycobacterium tuberculosis||4,402||661||939||828||June 2007|||
|Bacillus subtilis||4,114||844||1,020||988||September 2007|||
|Synechocystis sp. PCC6803||3,221||633||831||704||October 2008|||
|Salmonella typhimurium||4,489||1,083||1,087||774||April 2009|||
|Arabidopsis thaliana||27,379||1,419||1,567||1,748||February 2010|||
Because the timescale for the development of reconstructions is so recent, most reconstructions have been built manually. However, now, there are quite a few resources that allow for the semi-automatic assembly of these reconstructions that are utilized due to the time and effort necessary for a reconstruction. An initial fast reconstruction can be developed automatically using resources like PathoLogic or ERGO in combination with encyclopedias like MetaCyc, and then manually updated by using resources like PathwayTools. These semi-automatic methods allow for a fast draft to be created while allowing the fine tune adjustments required once new experimental data is found. It is only in this manner that the field of metabolic reconstructions will keep up with the ever-increasing numbers of annotated genomes.
- Kyoto Encyclopedia of Genes and Genomes (KEGG): a bioinformatics database containing information on genes, proteins, reactions, and pathways. The ‘KEGG Organisms’ section, which is divided into eukaryotes and prokaryotes, encompasses many organisms for which gene and DNA information can be searched by typing in the enzyme of choice.
- BioCyc, EcoCyc, and MetaCyc: BioCyc Is a collection of 3,000 pathway/genome databases (as of Oct 2013), with each database dedicated to one organism. For example, EcoCyc is a highly detailed bioinformatics database on the genome and metabolic reconstruction of Escherichia coli, including thorough descriptions of E. coli signaling pathways and regulatory network. The EcoCyc database can serve as a paradigm and model for any reconstruction. Additionally, MetaCyc, an encyclopedia of experimentally defined metabolic pathways and enzymes, contains 2,100 metabolic pathways and 11,400 metabolic reactions (Oct 2013).
- ENZYME: An enzyme nomenclature database (part of the ExPASy proteonomics server of the Swiss Institute of Bioinformatics). After searching for a particular enzyme on the database, this resource gives you the reaction that is catalyzed. ENZYME has direct links to other gene/enzyme/literature databases such as KEGG, BRENDA, and PUBMED.
- BRENDA: A comprehensive enzyme database that allows for an enzyme to be searched by name, EC number, or organism.
- BiGG: A knowledge base of biochemically, genetically, and genomically structured genome-scale metabolic network reconstructions.
- metaTIGER: Is a collection of metabolic profiles and phylogenomic information on a taxonomically diverse range of eukaryotes which provides novel facilities for viewing and comparing the metabolic profiles between organisms.
Tools for metabolic modeling Edit
- Pathway Tools: A bioinformatics software package that assists in the construction of pathway/genome databases such as EcoCyc.  Developed by Peter Karp and associates at the SRI International Bioinformatics Research Group, Pathway Tools has several components. Its PathoLogic module takes an annotated genome for an organism and infers probable metabolic reactions and pathways to produce a new pathway/genome database. Its MetaFlux component can generate a quantitative metabolic model from that pathway/genome database using flux-balance analysis. Its Navigator component provides extensive query and visualization tools, such as visualization of metabolites, pathways, and the complete metabolic network.
- ERGO: A subscription-based service developed by Integrated Genomics. It integrates data from every level including genomic, biochemical data, literature, and high-throughput analysis into a comprehensive user friendly network of metabolic and nonmetabolic pathways.
- KEGGtranslator:  an easy-to-use stand-alone application that can visualize and convert KEGG files (KGML formatted XML-files) into multiple output formats. Unlike other translators, KEGGtranslator supports a plethora of output formats, is able to augment the information in translated documents (e.g., MIRIAM annotations) beyond the scope of the KGML document, and amends missing components to fragmentary reactions within the pathway to allow simulations on those. KEGGtranslator converts these files to SBML, BioPAX, SIF, SBGN, SBML with qualitative modeling extension, GML, GraphML, JPG, GIF, LaTeX, etc.
- ModelSEED: An online resource for the analysis, comparison, reconstruction, and curation of genome-scale metabolic models.  Users can submit genome sequences to the RAST annotation system, and the resulting annotation can be automatically piped into the ModelSEED to produce a draft metabolic model. The ModelSEED automatically constructs a network of metabolic reactions, gene-protein-reaction associations for each reaction, and a biomass composition reaction for each genome to produce a model of microbial metabolism that can be simulated using Flux Balance Analysis.
- MetaMerge: algorithm for semi-automatically reconciling a pair of existing metabolic network reconstructions into a single metabolic network model. 
- CoReCo:  algorithm for automatic reconstruction of metabolic models of related species. The first version of the software used KEGG as reaction database to link with the EC number predictions from CoReCo. Its automatic gap filling using atom map of all the reactions produce functional models ready for simulation.
Tools for literature Edit
- PUBMED: This is an online library developed by the National Center for Biotechnology Information, which contains a massive collection of medical journals. Using the link provided by ENZYME, the search can be directed towards the organism of interest, thus recovering literature on the enzyme and its use inside of the organism.
Methodology to draft a reconstruction Edit
A reconstruction is built by compiling data from the resources above. Database tools such as KEGG and BioCyc can be used in conjunction with each other to find all the metabolic genes in the organism of interest. These genes will be compared to closely related organisms that have already developed reconstructions to find homologous genes and reactions. These homologous genes and reactions are carried over from the known reconstructions to form the draft reconstruction of the organism of interest. Tools such as ERGO, Pathway Tools and Model SEED can compile data into pathways to form a network of metabolic and non-metabolic pathways. These networks are then verified and refined before being made into a mathematical simulation. 
The predictive aspect of a metabolic reconstruction hinges on the ability to predict the biochemical reaction catalyzed by a protein using that protein's amino acid sequence as an input, and to infer the structure of a metabolic network based on the predicted set of reactions. A network of enzymes and metabolites is drafted to relate sequences and function. When an uncharacterized protein is found in the genome, its amino acid sequence is first compared to those of previously characterized proteins to search for homology. When a homologous protein is found, the proteins are considered to have a common ancestor and their functions are inferred as being similar. However, the quality of a reconstruction model is dependent on its ability to accurately infer phenotype directly from sequence, so this rough estimation of protein function will not be sufficient. A number of algorithms and bioinformatics resources have been developed for refinement of sequence homology-based assignments of protein functions:
- InParanoid: Identifies eukaryotic orthologs by looking only at in-paralogs.
- CDD: Resource for the annotation of functional units in proteins. Its collection of domain models utilizes 3D structure to provide insights into sequence/structure/function relationships.
- InterPro: Provides functional analysis of proteins by classifying them into families and predicting domains and important sites.
- STRING: Database of known and predicted protein interactions.
Once proteins have been established, more information about the enzyme structure, reactions catalyzed, substrates and products, mechanisms, and more can be acquired from databases such as KEGG, MetaCyc and NC-IUBMB. Accurate metabolic reconstructions require additional information about the reversibility and preferred physiological direction of an enzyme-catalyzed reaction which can come from databases such as BRENDA or MetaCyc database. 
An initial metabolic reconstruction of a genome is typically far from perfect due to the high variability and diversity of microorganisms. Often, metabolic pathway databases such as KEGG and MetaCyc will have "holes", meaning that there is a conversion from a substrate to a product (i.e., an enzymatic activity) for which there is no known protein in the genome that encodes the enzyme that facilitates the catalysis. What can also happen in semi-automatically drafted reconstructions is that some pathways are falsely predicted and don't actually occur in the predicted manner.  Because of this, a systematic verification is made in order to make sure no inconsistencies are present and that all the entries listed are correct and accurate.  Furthermore, previous literature can be researched in order to support any information obtained from one of the many metabolic reaction and genome databases. This provides an added level of assurance for the reconstruction that the enzyme and the reaction it catalyzes do actually occur in the organism.
Enzyme promiscuity and spontaneous chemical reactions can damage metabolites. This metabolite damage, and its repair or pre-emption, create energy costs that need to be incorporated into models. It is likely that many genes of unknown function encode proteins that repair or pre-empt metabolite damage, but most genome-scale metabolic reconstructions only include a fraction of all genes.  
Any new reaction not present in the databases needs to be added to the reconstruction. This is an iterative process that cycles between the experimental phase and the coding phase. As new information is found about the target organism, the model will be adjusted to predict the metabolic and phenotypical output of the cell. The presence or absence of certain reactions of the metabolism will affect the amount of reactants/products that are present for other reactions within the particular pathway. This is because products in one reaction go on to become the reactants for another reaction, i.e. products of one reaction can combine with other proteins or compounds to form new proteins/compounds in the presence of different enzymes or catalysts. 
Francke et al.  provide an excellent example as to why the verification step of the project needs to be performed in significant detail. During a metabolic network reconstruction of Lactobacillus plantarum, the model showed that succinyl-CoA was one of the reactants for a reaction that was a part of the biosynthesis of methionine. However, an understanding of the physiology of the organism would have revealed that due to an incomplete tricarboxylic acid pathway, Lactobacillus plantarum does not actually produce succinyl-CoA, and the correct reactant for that part of the reaction was acetyl-CoA.
Therefore, systematic verification of the initial reconstruction will bring to light several inconsistencies that can adversely affect the final interpretation of the reconstruction, which is to accurately comprehend the molecular mechanisms of the organism. Furthermore, the simulation step also ensures that all the reactions present in the reconstruction are properly balanced. To sum up, a reconstruction that is fully accurate can lead to greater insight about understanding the functioning of the organism of interest. 
A metabolic network can be broken down into a stoichiometric matrix where the rows represent the compounds of the reactions, while the columns of the matrix correspond to the reactions themselves. Stoichiometry is a quantitative relationship between substrates of a chemical reaction. In order to deduce what the metabolic network suggests, recent research has centered on a few approaches, such as extreme pathways, elementary mode analysis,  flux balance analysis, and a number of other constraint-based modeling methods.  
Extreme pathways Edit
Price, Reed, and Papin,  from the Palsson lab, use a method of singular value decomposition (SVD) of extreme pathways in order to understand regulation of a human red blood cell metabolism. Extreme pathways are convex basis vectors that consist of steady state functions of a metabolic network.  For any particular metabolic network, there is always a unique set of extreme pathways available.  Furthermore, Price, Reed, and Papin,  define a constraint-based approach, where through the help of constraints like mass balance and maximum reaction rates, it is possible to develop a ‘solution space’ where all the feasible options fall within. Then, using a kinetic model approach, a single solution that falls within the extreme pathway solution space can be determined.  Therefore, in their study, Price, Reed, and Papin,  use both constraint and kinetic approaches to understand the human red blood cell metabolism. In conclusion, using extreme pathways, the regulatory mechanisms of a metabolic network can be studied in further detail.
Elementary mode analysis Edit
Elementary mode analysis closely matches the approach used by extreme pathways. Similar to extreme pathways, there is always a unique set of elementary modes available for a particular metabolic network.  These are the smallest sub-networks that allow a metabolic reconstruction network to function in steady state.    According to Stelling (2002),  elementary modes can be used to understand cellular objectives for the overall metabolic network. Furthermore, elementary mode analysis takes into account stoichiometrics and thermodynamics when evaluating whether a particular metabolic route or network is feasible and likely for a set of proteins/enzymes. 
Minimal metabolic behaviors (MMBs) Edit
In 2009, Larhlimi and Bockmayr presented a new approach called "minimal metabolic behaviors" for the analysis of metabolic networks.  Like elementary modes or extreme pathways, these are uniquely determined by the network, and yield a complete description of the flux cone. However, the new description is much more compact. In contrast with elementary modes and extreme pathways, which use an inner description based on generating vectors of the flux cone, MMBs are using an outer description of the flux cone. This approach is based on sets of non-negativity constraints. These can be identified with irreversible reactions, and thus have a direct biochemical interpretation. One can characterize a metabolic network by MMBs and the reversible metabolic space.
Flux balance analysis Edit
A different technique to simulate the metabolic network is to perform flux balance analysis. This method uses linear programming, but in contrast to elementary mode analysis and extreme pathways, only a single solution results in the end. Linear programming is usually used to obtain the maximum potential of the objective function that you are looking at, and therefore, when using flux balance analysis, a single solution is found to the optimization problem.  In a flux balance analysis approach, exchange fluxes are assigned to those metabolites that enter or leave the particular network only. Those metabolites that are consumed within the network are not assigned any exchange flux value. Also, the exchange fluxes along with the enzymes can have constraints ranging from a negative to positive value (ex: -10 to 10).
Furthermore, this particular approach can accurately define if the reaction stoichiometry is in line with predictions by providing fluxes for the balanced reactions. Also, flux balance analysis can highlight the most effective and efficient pathway through the network in order to achieve a particular objective function. In addition, gene knockout studies can be performed using flux balance analysis. The enzyme that correlates to the gene that needs to be removed is given a constraint value of 0. Then, the reaction that the particular enzyme catalyzes is completely removed from the analysis.
Dynamic simulation and parameter estimation Edit
In order to perform a dynamic simulation with such a network it is necessary to construct an ordinary differential equation system that describes the rates of change in each metabolite's concentration or amount. To this end, a rate law, i.e., a kinetic equation that determines the rate of reaction based on the concentrations of all reactants is required for each reaction. Software packages that include numerical integrators, such as COPASI or SBMLsimulator, are then able to simulate the system dynamics given an initial condition. Often these rate laws contain kinetic parameters with uncertain values. In many cases it is desired to estimate these parameter values with respect to given time-series data of metabolite concentrations. The system is then supposed to reproduce the given data. For this purpose the distance between the given data set and the result of the simulation, i.e., the numerically or in few cases analytically obtained solution of the differential equation system is computed. The values of the parameters are then estimated to minimize this distance.  One step further, it may be desired to estimate the mathematical structure of the differential equation system because the real rate laws are not known for the reactions within the system under study. To this end, the program SBMLsqueezer allows automatic creation of appropriate rate laws for all reactions with the network. 
Synthetic accessibility Edit
Synthetic accessibility is a simple approach to network simulation whose goal is to predict which metabolic gene knockouts are lethal. The synthetic accessibility approach uses the topology of the metabolic network to calculate the sum of the minimum number of steps needed to traverse the metabolic network graph from the inputs, those metabolites available to the organism from the environment, to the outputs, metabolites needed by the organism to survive. To simulate a gene knockout, the reactions enabled by the gene are removed from the network and the synthetic accessibility metric is recalculated. An increase in the total number of steps is predicted to cause lethality. Wunderlich and Mirny showed this simple, parameter-free approach predicted knockout lethality in E. coli and S. cerevisiae as well as elementary mode analysis and flux balance analysis in a variety of media. 
- Several inconsistencies exist between gene, enzyme, reaction databases, and published literature sources regarding the metabolic information of an organism. A reconstruction is a systematic verification and compilation of data from various sources that takes into account all of the discrepancies.
- The combination of relevant metabolic and genomic information of an organism.
- Metabolic comparisons can be performed between various organisms of the same species as well as between different organisms.
- Analysis of synthetic lethality 
- Predict adaptive evolution outcomes 
- Use in metabolic engineering for high value outputs
Reconstructions and their corresponding models allow the formulation of hypotheses about the presence of certain enzymatic activities and the production of metabolites that can be experimentally tested, complementing the primarily discovery-based approach of traditional microbial biochemistry with hypothesis-driven research.  The results these experiments can uncover novel pathways and metabolic activities and decipher between discrepancies in previous experimental data. Information about the chemical reactions of metabolism and the genetic background of various metabolic properties (sequence to structure to function) can be utilized by genetic engineers to modify organisms to produce high value outputs whether those products be medically relevant like pharmaceuticals high value chemical intermediates such as terpenoids and isoprenoids or biotechnological outputs like biofuels. 
Metabolic network reconstructions and models are used to understand how an organism or parasite functions inside of the host cell. For example, if the parasite serves to compromise the immune system by lysing macrophages, then the goal of metabolic reconstruction/simulation would be to determine the metabolites that are essential to the organism's proliferation inside of macrophages. If the proliferation cycle is inhibited, then the parasite would not continue to evade the host's immune system. A reconstruction model serves as a first step to deciphering the complicated mechanisms surrounding disease. These models can also look at the minimal genes necessary for a cell to maintain virulence. The next step would be to use the predictions and postulates generated from a reconstruction model and apply it to discover novel biological functions such as drug-engineering and drug delivery techniques.
Metabolism of Carbohydrates: 10 Cycles (With Diagram)
This article throws light upon the ten major pathways/cycles of carbohydrate metabolism. The ten pathways/cycles of carbohydrate metabolism are:
(1) Glycolysis (2) Conversion of Pyruvate to Acetyl COA (3) Citric Acid Cycle (4) Gluconeogenesis (5) Glycogen Metabolism (6) Glycogenesis (7) Glycogenolysis (8) Hexose Monophosphate Shunt (9) Glyoxylate Cycle and (10) Photosynthesis.
Carbohydrates are the major source of energy for the living cells. The monosaccharide glucose is the central molecule in carbohydrate metabolism since all the major pathways of carbohydrate metabolism are connected with it (Fig. 67.3).
Glucose is utilized as a source of energy, it is synthesized from non-carbohydrate precursors and stored as glycogen to release glucose as and when the need arises. The other monosaccharide’s important in carbohydrate metabolism are fructose, galactose and mannose.
The fasting blood glucose level in normal humans is 60-100 mg/dl (4.5-5.5 mmol/l) and it is very efficiently maintained at this level.
The outlines of major pathways/cycles of carbohydrate metabolism are described:
Cycle # 1. Glycolysis:
Glycolysis is derived from the Greek words (glycose—sweet or sugar lysis—dissolution). It is a universal pathway in the living cells. Glycolysis is defined as the sequence of reactions converting glucose (or glycogen) to pyruvate or lactate, with the production of ATP (Fig. 67.4).
1. Glycolysis (also known as Embden-Meyerhof pathway) takes place in all cells of the body. The enzymes of this pathway are present in the cytosomal fraction of the cell.
2. Glycolysis occurs in the absence of oxygen (anaerobic) or in the presence of oxygen (aerobic). Lactate is the end product under anaerobic condition. In the aerobic condition, pyruvate is formed, which is then oxidized to CO2 and H2O.
3. Glycolysis is a major pathway for ATP synthesis in tissues lacking mitochondria, e.g. erythrocytes, cornea, lens etc.
4. Glycolysis is very essential for brain which is dependent on glucose for energy. The glucose in brain has to undergo glycolysis before it is oxidized to CO2 and H2O.
5. Glycolysis (anaerobic) may be summarized by the net reaction
Glucose + 2ADP + 2Pi → 2 Lactate + 2ATP
6. Reversal of glycolysis along with the alternate arrangements made at the irreversible steps will result in the synthesis of glucose (gluconeogenesis).
Cycle # 2. Conversion of Pyruvate to Acetyl COA:
Pyruvate is converted to acetyl CoA by oxidative decarboxylation. This is an irreversible reaction, catalysed by a multi-enzyme complex, known as pyruvate dehydrogenase complex (PDH), which is found only in the mitochondria. High concentrations of PDH are found in cardiac muscle and kidney. The enzyme PDH requires five cofactors (coenzymes), namely — TPP, lipoamide, FAD, coenzyme A and NAD + (lipoamide contains lipoic acid linked to ɛ-amino group of lysine).
The overall reaction of PDH is:
Cycle # 3. Citric Acid Cycle:
The citric acid cycle (Krebs cycle or tricarboxylic acid—TCA cycle) is the most important metabolic pathway for the energy supply to the body. About 65-70% of the ATP is synthesized in Krebs cycle. Citric acid cycle essentially involves the oxidation of acetyl CoA to CO2 and H2O.
The citric acid cycle is the final common oxidative pathway for carbohydrates, fats and amino acids. This cycle not only supplies energy but also provides many intermediates required for the synthesis of amino acids, glucose, heme etc. Krebs cycle is the most important central pathway connecting almost all the individual metabolic pathways (either directly or indirectly). The enzymes of TCA cycle are located in mitochondrial matrix, in close proximity to the electron transport chain.
Krebs cycle basically involves the combination of a two carbon acetyl CoA with a four carbon oxaloacetate to produce a six carbon tricarboxylic acid, citrate. In the reactions that follow, the two carbons are oxidized to CO, and oxaloacetate is regenerated and recycled. Oxaloacetate is considered to play a catalytic role in citric acid cycle. The reactions of Krebs cycle are depicted in Fig. 67.5.
Cycle # 4. Gluconeogenesis:
The synthesis of glucose or glycogen from non-carbohydrate compounds is known as gluconeogenesis. The major substrates/precursors for gluconeogenesis are lactate, pyruvate, glucogenic amino acids, propionate and glycerol.
Location of gluconeogenesis:
Gluconeogenesis occurs mainly in the cytosol, although some precursors are produced in the mitochondria. Gluconeogenesis mostly takes place in liver and, to some extent, in kidney matrix (about one-tenth of liver capacity).
Reactions of gluconeogenesis:
Gluconeogenesis closely resembles the reversed pathway of glycolysis, although it is not the complete reversal of glycolysis. Essentially, 3 (out of 10) reactions of glycolysis are irreversible. The seven reactions are common for both glycolysis and gluconeogenesis. The three irreversible steps of glycolysis are catalysed by the enzymes, namely hexokinase, phosphofructokinase and pyruvate kinase.
Cycle # 5. Glycogen Metabolism:
Glycogen is the storage form of glucose in animals, as is starch in plants. It is stored mostly in liver (6-8%) and muscle (1-2%). Due to more muscle mass, the quantity of glycogen in muscle (250 g) is about three times higher than that in the liver (75 g).
Functions of glycogen:
The prime function of liver glycogen is to maintain the blood glucose levels, particularly between meals. Liver glycogen stores increase in a well-fed state which are depleted during fasting. Muscle glycogen serves as a fuel reserve for the supply of ATP during muscle contraction.
Cycle # 6. Glycogenesis:
The synthesis of glycogen from glucose is glycogenesis. Glycogenesis takes place in the cytosol and requires ATP and UTP, besides glucose.
Cycle # 7. Glycogenolysis:
The degradation of stored glycogen in liver and muscle constitutes glycogenolysis. The pathway for the synthesis and degradation of glycogen are not reversible. An independent set of enzymes present in the cytosol carry out glycogenolysis. Glycogen is degraded by breaking α-1, 4- and α-1, 6-glycosidic bonds.
Cycle # 8. Hexose Monophosphate Shunt:
Hexose monophosphate pathway or HMP shunt is also called pentose phosphate pathway or phosphogluconate pathway. This is an alternative pathway to glycolysis and TCA cycle for the oxidation of glucose. However, HMP shunt is more anabolic in nature, since it is concerned with the biosynthesis of NADPH and pentose’s.
Location of the pathway:
The enzymes of HMP shunt are located in the cytosol. The tissues such as liver, adipose tissue, adrenal gland, erythrocytes, testes and lactating mammary gland, are highly active in HMP shunt. Most of these tissues are involved in the biosynthesis of fatty acids and steroids which are dependent on the supply of NADPH.
Reactions of HMP shunt:
The sequence of reactions of HMP shunt is depicted in Fig. 67.6.
Significance of HMP shunt:
HMP shunt is unique in generating two important products—pentose’s and NADPH—needed for the biosynthetic reactions and other functions.
A. Importance of pentose’s:
In the HMP shunt, hexoses are converted into pentose’s, the most important being ribose 5-phosphate. This pentose or its derivatives are useful for the synthesis of nucleic acids (RNA and DNA) and many nucleotides such as ATP, NAD + , FAD and CoA.
B. Importance of NADPH:
1. NADPH is required for the reductive biosynthesis of fatty acids and steroids, hence HMP shunt is more active in the tissues concerned with lipogenesis, e.g. adipose tissue, liver etc.
2. NADPH is used in the synthesis of certain amino acids involving the enzyme glutamate dehydrogenase.
3. There is a continuous production of H2O2 in the living cells which can chemically damage unsaturated lipids, proteins and DNA. This is, however, prevented to a large extent through antioxidant reactions involving NADPH. Glutathione mediated reduction of H2O2 is given hereunder
Glutathione (reduced, GSH) detoxifies H2O2, peroxidase catalyses this reaction. NADPH is responsible for the regeneration of reduced glutathione from the oxidized one.
Cycle # 9. Glyoxylate Cycle:
The animals, including man, cannot carry out the net synthesis of carbohydrate from fat. However, the plants and many microorganisms are equipped with the metabolic machinery—namely the glyoxylate cycle—to convert fat into carbohydrates. This pathway is very significant in the germinating seeds where the stored triacylglycerol (fat) is converted to sugars to meet the energy needs.
The glyoxylate cycle is regarded as an anabolic variant of citric acid cycle and is depicted in Fig. 67.7.
Cycle # 10. Photosynthesis:
The synthesis of carbohydrates in green plants photosynthesis. It is now recognized that photosynthesis primarily involves the process of energy transduction in which light energy is converted into chemical energy (in the form of oxidizable carbon compounds).
It is an established fact that all the energy consumed by the biological systems arises from the solar energy that is trapped in the photosynthesis. The basic equation of photosynthesis is given below.
In the above equation, (CH2O) represents carbohydrate. Photosynthesis in the green plants occurs in the chloroplasts, a specialized organelles. The mechanism of photosynthesis is complex, involving many stages, and participation of various macromolecules and macromolecules.
The role of photosystems:
The initial step in the photosynthesis is the by assimilation of carbon dioxide is referred to as absorption of light by chlorophyll molecules in the chloroplasts. This results in the production of excitation energy which is transferred from one chlorophyll molecule to another, until it is trapped by a reaction center. The light-activated transfer of an electron to an acceptor (photosystems) occurs at the reaction center.
Photosynthesis primarily requires the interactions of two distinct photosystems (I and II). Photosystem I generates a strong reductant that results in the formation of NADPH. Photosystem II produces a strong oxidant that forms O2 from H2O. Further, the generation of ATP occurs as electrons flow from photosystem II to photosystem I (Fig. 67.8). Thus, light is responsible for the flow of electrons from H2O to NADPH with a concomitant generation of ATP.
The Calvin cycle:
The dark phase of photosynthesis is referred to as Calvin cycle. In this cycle, the ATP and NADPH produced in the light reaction (described above) are utilized to convert CO2 to hexoses and other organic compounds (Fig. 67.9). The Calvin cycle starts with a reaction of CO2 and ribulose 1, 5-bisphosphate to form two molecules 3-phosphoglycerate. This 3-phosphoglycerate can be converted to fructose 6-phosphate, glucose 6-phosphate and other carbon compounds.
Virtual metabolic humans, Harvey and Harvetta, novel computational models for personalised medicine
Credit: Science Foundation Ireland (SFI)
We are all unique. Our health is determined by our inherent genetic differences combined with our lifestyles and the environments in which we live. This unique identity means that a "one size fits all" approach is no longer accepted as the best way to manage our individual health. There is a demand for new "personalised" approaches to better manage our health and to target therapies to achieve optimum health outcomes.
By combining and analysing information about our genome, with other clinical and diagnostic information, patterns can be identified that can help to determine our individual risk of developing disease, detect illness earlier and determine the most effective interventions to help improve health, be they medicines, lifestyle choices, or even simple changes in diet.
Researchers, led by Prof Ines Thiele, a Principal Investigator at APC Microbiome Ireland SFI Research Centre, who is based in National University of Ireland, Galway, have developed whole-body computational models—Harvey and Harvetta. These virtual humans represent whole-body metabolism, physiology, diet and the gut microbiome. These new models successfully predict known biomarkers of inherited metabolic diseases and enable exploration of potential metabolic interactions between humans and their gut microbiomes at a personal level.
Precision, or personalised, medicine requires realistic, mechanistic computational models that capture the complexity of the human representing each individual's physiology, dietary habits, metabolism and microbiomes. Molecular biology has yielded great insight into the 'parts list' for human cells, but it remains challenging to integrate these parts into a virtual whole human body. The Virtual Human Physiome project has generated comprehensive computational models about the anatomy and physiology of human organs but has yet to be connected with molecular level processes and their underlying networks of genes, proteins, and biochemical reactions.
Prof Thiele's team tackled this challenge to develop the first whole-body, sex-specific, organ-resolved computational models of human metabolism, which mechanistically connect anatomy and physiology with molecular level metabolic processes. Their study is published today in the prestigious journal Molecular Systems Biology.
Harvey and Harvetta are virtual male and female human metabolic models, respectively, built from literature and data on human metabolism, anatomy and physiology as well as biochemical, metabolomic and proteomic data. They are anatomically interconnected as whole-body metabolic models, comprised of more than 80,000 biochemical reactions distributed over 26 organs and 6 types of blood cell. Moreover, they can be expanded to include gut microbial metabolism. These unique models enable generation of personalised whole-body metabolic models using an individual's physiological, genomic, biochemical and microbiome data.
Whole-body metabolic model
Generating personalised whole-body metabolic models is an interdisciplinary effort. The development of whole-body models of metabolism required the development of novel algorithms and software for constraint-based modelling of high-dimensional biochemical networks. "A whole-body model is generated by starting with a set of anatomically interconnected generic reconstructions of human metabolism", says Assistant Prof Ronan Fleming, a co-author of the study from the Leiden Academic Centre for Drug Research, Leiden University. "This draft model had in excess of 300 thousand dimensions, which was then pared down to approximately 80 thousand organ-specific reactions using efficient algorithms and high-performance computing facilities."
"Harvey and Harvetta will usher in a new era for research into causal host-microbiome relationships and greatly accelerate the development of targeted dietary and microbial intervention strategies" said Prof Ines Thiele, who lead the research. "These models could accelerate insights into pathways involved in sex-specific disease development and progression. Moreover, thanks to the ability to personalize the whole-body metabolic models with clinical, physiological, and omics data, they represent a significant step towards personalised, predictive modelling of dietary and drug interventions and drug toxicity, which lies at the heart of precision medicine."
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Vol 347, Issue 6220
23 January 2015
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By Mathias Uhlén , Linn Fagerberg , Björn M. Hallström , Cecilia Lindskog , Per Oksvold , Adil Mardinoglu , Åsa Sivertsson , Caroline Kampf , Evelina Sjöstedt , Anna Asplund , IngMarie Olsson , Karolina Edlund , Emma Lundberg , Sanjay Navani , Cristina Al-Khalili Szigyarto , Jacob Odeberg , Dijana Djureinovic , Jenny Ottosson Takanen , Sophia Hober , Tove Alm , Per-Henrik Edqvist , Holger Berling , Hanna Tegel , Jan Mulder , Johan Rockberg , Peter Nilsson , Jochen M. Schwenk , Marica Hamsten , Kalle von Feilitzen , Mattias Forsberg , Lukas Persson , Fredric Johansson , Martin Zwahlen , Gunnar von Heijne , Jens Nielsen , Fredrik Pontén
Transcriptomics and immunohistochemistry map protein expression across 32 human tissues.
HumanCyc: Encyclopedia of Human Genes and Metabolism
HumanCyc provides an encyclopedic reference on human metabolic pathways, the human genome, and human metabolites.
HumanCyc is part of the larger BioCyc collection of thousands of Pathway/Genome Databases for sequenced genomes. Click the "Change Current Database" button above to explore the available databases.
HumanCyc provides tools for querying, visualizing, and analyzing the underlying database, and for analyzing omics data:
- Multiple pathway-analysis methods are available for omics and multi-omics datasets including painting data onto pathway diagrams and the metabolic-map diagram
- Store groups of genes and pathways in your account as SmartTables share, analyze, transform those groups
- Search for paths in the metabolic network using the Metabolism &rarr Metabolic Route Search tool
Curation primarily affects information on pathways and corresponding enzyme pages. HumanCyc is a partially curated database and is a work in progress it is incomplete in many respects, and may contain errors.
HumanCyc contains few signaling pathways. It is not yet compartmentalized nor tissue-specific.
For information regarding most recently curated pathways see the Release Notes.
Biochemical Pathway Maps
Following the outstanding success of the two posters for over four decades, and of the electronic version hosted on ExPASy for more than 20 years (1994-2016), Roche has created a new electronic version of Biochemical Pathways.
This is freely accessible to everybody interested such as biochemists, graduate and undergraduate students, teachers and pupils, and allows to explore both Metabolic Pathways and Cellular and Molecular Processes.
The electronic Biochemical Pathways allows the user to search the wall charts with keywords, set focus effects, activate filtering functions and zooming in on the details and elements of interest. Through a simple navigational tool, the digital version has greatly simplified the user experience and ease of navigation.
To access ExPASy's ENZYME Database through the wall charts, simply use the Search function to "activate" the enzyme you are looking for. A list of all matching entries of the "Biochemical Pathways" wall chart will be given. Click on the enzyme name to navigate to the corresponding ENZYME database entry.
More than 700'000 hard copies of the wall charts have been distributed to medical and life-science researchers and students around the world. The Biochemical Pathway posters are available for download/printing : Please do NOT email ExPASy staff with enquiries on this subject.