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Metabolomics/Introduction to Metabolomics

Back to Book Table of Contents: Metabolomics Next chapter: Metabolites
 * 1) History
 * 2) Relationship to Traditional Metabolism

The New World of Metabolomics
In the world of biology and biochemistry there are many tiers of function. There is the genome, which is the underlying blueprint for the workings of our cells. From the genome arises the proteome; the factories, building blocks and workhorses of the cell and the organism. But neither of these is enough to truly understand the workings of biological systems.

Cells and organisms have far more in them than just proteins and DNA. Metabolites are the organic chemical compounds that either start off the reactions within biology or act as intermediates, changing or being incorporated into each reaction along a metabolic pathway. The grand total of these compounds and their interactions throughout a cell or a species is the Metabolome. Traditionally metabolism was our way of understanding the Metabolome. It was measured in small-scale pathways through a reductionist approach. The idea was that the sum of the parts could be used to truly understand the whole. But the Metabolome can’t effectively be found through the sum of the parts, especially in the context of systems biology and metabolic engineering.

Newer tiers like Phenomics (the study of the set of phenotypes expressed by a cell) and new disciplines like Nutrigenomics (the study of relationship between nutrition and genetic function) depend heavily on comprehending the Metabolome in a large-scale fashion.

Metabolomics is the solution to this problem. A comprehensive, systems biology conscious approach to understanding the Metabolome in its full scope. Metabolomics seeks to avoid reductionism and apply high throughput analysis methods on metabolic levels in the cell. It will revolutionize fields like metabolic engineering and increase our knowledge of biological function phenomenally.

Recommended Reading & Sources
Introduction: Metabolomics

Peer-Reviewed Article #1:
What is metabolomics all about?

Roessner U, Bowne J. BioTechniques Special Issue (2009) 46:363–365

Reviewer: Arooj I

Main Focus

 * The main focus of “What is metabolic all about?” by Ute Roessner and Jairus Bowne, is to describe various techniques (pathway mapping, metabolite fingerprinting, comparative overlays and etc.) which develop metabolomics to better understand the organization and roles of organisms in the environment. There were four species used in a study to determine the metabolite profiles. These species were; moss Physcomitrella patens, the model plant Arabidopsis thaliana, and the crop plants Hordeum vulgare L. and Triticum aestivum L. These comparisons were done with techniques such as cluster analysis, DiMS, and target analysis.

New Terms
http://www.ajronline.org/cgi/content/full/185/2/299,http://en.wikipedia.org/wiki/Multivariate_statistics < http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TCW-3TVXPC7-3&_user=47004&_coverDate=09%2F01%2F1998&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_acct=C000005018&_version=1&_urlVersion=0&_userid=47004&md5=6ad860575d4269aa5de1da98a652c927>
 * New Term 1: Multi-variant Method: This is a statistical method based on multivariate statistics. Multivariate statistics is a statistical analysis of more than one statistical variable at a time. This technique can be used to detect the effects of all the variables in a trade study.
 * New Term 2: Genetic Perturbation: This is a small change in a physical system. In a genetic perturbation is a biological change such as a mutation in an organism. http://www.ask.com/web?q=dictionary%3A+perturbation&content=ahdict|61997&o=10616&l=dir
 * New Term 3: Metabolome: A metabolome is a complete set of small-molecule metabolites such as metabolic intermediates, hormones and other signaling molecules, and secondary metabolites which can be found within a biological sample, such as a single organism.
 * New Term 4: Transcriptomics: This is the study of a transcriptome. A transcriptome is the complete set of RNA transcripts produced by the genome at any one time. http://www.ask.com/bar?q=What+is+Transcriptomics&page=1&qsrc=2417&dm=all&ab=0&u=http%3A%2F%2Fgenome.wellcome.ac.uk%2Fdoc_WTD020758.html&sg=M6I18%2Fh0XH0FoWssUi2tSOVf6%2FiU%2Fnjmbi36hiDRfOM%3D&tsp=1265598042743
 * New Term 5: Target Analysis: This analysis involves using one particular analytical technique for the determining and quantifying a small set of known metabolites. http://www.jointcommission.org/NR/rdonlyres/2B5D26DA-6BD1-48FD-A7FA-F9BBCBA49A9D/0/ORYX_Target_Analysis.pdf
 * New Term 6: Metabolic Fingerprinting: A mass profile of the sample of interest is generated and then compared in a large sample population to screen for differences between the samples. http://www.biotechniques.com/multimedia/archive/00044/BTN_A_000113133_O_44653a.pdf
 * New Term 7: Direct Infusion Electrospray Mass Spectrometry (DiMS): In this technique, crude extracts of subspecies are taken and directly injected to a mass spectrometer to obtain mass profiles. These mass profiles can then be used to study the metabolite differences between the subspecies of a class.  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC92589/
 * New Term 8: Hierarchical Cluster Analysis: This is a cluster analysis technique which arranges the clusters in an order of hierarchy. The two main categories for the hierarchical arrangements are agglomerative and divisive. Cluster analysis is the assignment of a set of observations into clusters in which the observations have some relevance in relation. http://www.statistics.com/resources/glossary/h/hclusteran.php, http://www.clustan.com/hierarchical_cluster_analysis.html

Summary
Metabolomics in the last twenty years has made gigantic progress in sequencing a number of different organisms and has made large investments in developing analytical approaches for the study of different cell products. These ‘omics approaches such as transcriptomics, metabolomics and etc. are essential in the study of organisms responding to their environments. Metabolomics helps understand the physiological state of an organism. Metabolites act as the language of metabolomics. Only a few technologies have been employed such as mass spectrometry with chromatographic techniques like NMR to analyze these metabolites. Due to the complexity of a metabolome, it is difficult to employ a single technique for analysis. Therefore a number of approaches have to be established for extraction of the information and interpreting it in a biological context. Detection, quantification and identification can be accomplished through cluster analysis, pathway mapping and etc.

One application used in the study of metabolomics is target analysis, which helps in determination and quantification of a few known metabolites. Another application is metabolite profiling which analyzes large set of compounds using important tools like GC-MS. Metabolomics helps quantify as many metabolites as possible and metabolic fingerprinting helps differentiate between these samples.

Most of the research in metabolomics is based on the construction of phylogenetic trees. Primary metabolites are used to determine nutritional and growth status. Secondary metabolites function as markers for taxonomy and phylogenetics. Direct infusion ectecrospray mass spectrometry was a very helpful tool in determining different fungal species. When compared to the phenotypic tree, more than 80% of the species could be classified based on their mass profile. To determine mechanisms for adaptation and tolerance of plants to abiotic stresses revealed that a number of responses are plant specific or are common between stresses and/or plants.

A study to compare levels of metabolites in four different species using metabolomic techniques demonstrated that the metabolite profiles of the species were very different. Classifications such as these demonstrate metabolomics as an important tool for the identification and classification or organisms. Studies in metabolomics would be pretty helpful for the basic principle of life and its evolution. The drawbacks of metabolomics could be that it only measures a small portion of all elements in a biological system. In the future, study of metabolomics such as the response of various organisms to different environments at the genetic, transcript, protein and metabolite levels would strengthen are knowledge of the biology framework in different life systems. It would also help us understand the function, organization and roles of organisms in nature.

Relevance to a Traditional Metabolism Course
The course about metabolism, teaches students about the chemical reactions occurring in an organism such as citric acid cycle, glycolysis and gluconeogenesis. From reading this article, it was found that metabolomics is a study of the chemical footprints left behind by the cellular processes organisms carry out in everyday life. Metabolomics in the article is described by various techniques used to statistically analyze these footprints. The footprints in the article are believed to be called metabolites. These metabolites are studied in subspecies of a class using techniques such as metabolic fingerprinting, DiMS and hierarchical cluster analysis. Most of these techniques are new analytical approaches, allowing biochemists broaden there research goals in metabolism.

Web Resource: Introduction to Metabolomics
Introduction to Metabolomics

[http://plantsci.arabidopsis.info/pg/barratt.pdf Barrett D. Introduction to Metabolomics Powerpoint File. Centre for Analytical Bioscience School of Pharmacy, The University of Nottingam]

Reviewer: Steve S.

Main Focus
A summary of the basic principles of Metabolomics. Serves as a basic introduction to various experimental and analytical techniques of use in the field.

New Terms

 * Genomics: The Study of whole genomes of organisms. Focuses on sequencing and analyzing entire genomes of organisms. (source: http://epa.gov/osa/spc/pdfs/genomics.pdf)
 * Transcriptomics: The Study of gene transcripts. This science involves the study of all types of RNA produced from DNA, mRNA, rRNA, tRNA, and non-coding RNA. The transcript can be from one, or a whole population of cells. (source: http://www.systemsbiology.nl/datgen/transcriptomics/transcriptomics.html)
 * Proteomics: The Study of the final protein products of an organism, population of cells, or individual cell.  Various techniques are used to isolate, identify, and study the proteins in a proteome.(source: http://www.ncbi.nlm.nih.gov/pubmed/9740045)
 * Metabolomics: A field of study regarding the various products and reactions that take place within an organism to achieve its various needs.  Involves the study of both metabolites, as well as metabolic pathways.  Closely associated with the field of proteomics. (source: http://www.bmrb.wisc.edu/metabolomics/)
 * Metabolites: Products, as well as intermediate molecules, involved in metabolism. (source: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1904337/?tool=pubmed)
 * Metabolic Pathway: A set of chemical reactions inside a cell. Through series of intermediate steps,a metabolic pathway provides needed metabolic products.  (source: http://www2.ufp.pt/~pedros/bq/integration.htm)
 * Chromatography: Various laboratory techniqes which involve separating components of a mixture. Include Adsorption, Partition, Ion-exchange, Exclusion, and Affinity chromatography techniques. (source: http://www.iupac.org/publications/pac/1993/pdf/6504x0819.pdf)
 * NMR analysis: Analysis of data aquired through measurement of Nuclear Magnetic Resonance. NMR is a phenomenon that certain nuclei in molecules aquire when exposed to a static magnetic field. This effect is only observed in nuclei that have a property called spin. Through analysis of the level of NMR, information about the contents of a sample can be obtained.  (source: http://www.cis.rit.edu/htbooks/nmr/inside.htm)
 * Metabolomic Fingerprinting: A metabolomic fingerprint is an instantaneous measurement of the biochemistry of a cell under certain conditions. By observing the changes in metabolome under different conditions, these conditions can be diagnosed before typical symptoms occur.   (source: http://dbkgroup.org/dave_files/Pharmacogenomics.pdf)
 * Mass Spectrometry: The determination of chemistry of a molecule by breaking the molecule down into component ions.  These ions are then separated according to their mass, and their charge.  The output of mass spectometry is a count of ions of certain masses/charges. (source: http://www.cem.msu.edu/~reusch/VirtualText/Spectrpy/MassSpec/masspec1.htm)

Summary
Biology can be viewed as information processing on four interconnected levels. Each of these levels of information has an associated field of study. The flow of information starts at DNA, to RNA, to proteins, and finally metabolites. These are studied as the fields of Genomics, Transcriptomics, Proteomics, and Metabolomics. Metabolic Pathways are interconnected sets of chemical reactions. These pathways are complex, and difficult to study. However, many benefits can be gleaned from the study of metabolomics, and metabolites. Many metabolites are very highly conserved molecules. This means that the study of one organism can often yield results applicable to a wide range of organisms. Metabolomes can show influences of both genetics and environmental impacts. This allows metabolic diagnostics to have great power, as regardless of the source of a disease, it can be detected. Metabolites can be analyzed and measured through many techniqes. The main techniques include Mass Spectometry, NMR analysis, and chromatography. There are four levels on which metabolomes can be viewed. From most to least specific, these are Metabolite Target Analysis - measuring specific amounts of a single metabolite, Metabolic Profiling - viewing the abundance of related groups of compounds, metabolomics - the study of all metabolites in a cell, and Metabolic Fingerprinting - simple classification of metabolomes into important categories. There are many contributing factors to the difficulty in studying metabolomes. Rather than processing static information, of specific bits, such as genetics - which are made up of 4 base pairs, or proteins - made up of 20 Amino Acids. In contract, metabolism involves the interactions, over time, of thousands and thousands of different molecules. The products measured for metabolomes can vary in concentration from as low as nanomolar, to micromolar concentrations. Data from metabolomic analysis requires further analysis in order to interpret. The two main forms of these analysis is Principal Component Analysis. Despite these drawbacks, the benefits available from this analysis make it a productive field of study.

Relevance to a Traditional Metabolism Course
This article is a power point presentation, which provides an overview of Metabolomics. It serves nicely as a broad introduction to the techniques available for Metabolomics, as well as the purpose behind these techniques, their strengths, and weaknesses. There is a brief overview as to the role of metabolites, and their position in metabolic pathways - this is mostly presented as a review, however, and is most helpful to one already familiar with the concepts. This article is appropriate for giving an overview, or for a broad review, and does not go in depth in any given topic.


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Metabolomics/Metabolites

Back to Previous Chapter: Introduction to Metabolomics Next chapter: Hormones
 * 1) Carbohydrates
 * 2) Lipids
 * 3) Amino Acids
 * 4) Nucleotides

Table of Contents

 * 1) /Carbohydrates/
 * 2) /Lipids/
 * 3) /Amino Acids/
 * 4) /Nucleotides/

Metabolites
Metabolites are organic compounds that are starting materials/intermediates in metabolism pathways. Metabolites are small simple structures absorbed in a diet. They include vitamins and essential amino acids. They can be used to construct more complex molecules, or they can be broken down into simpler ones. Intermediary metabolites may be synthesized from other metabolites and often release chemical energy. For example, glucose, can be synthesized via gluconeogenesis (an anabolic reaction) to form starch or glycogen, and can be broken down during glycolysis (catabolic reaction) to obtain chemical energy. End products of metabolism are excreted from the organism. Urea, for example, is an end product of protein degradation in man. Carbon dioxide is usually thought of as an end product of carbohydrate, protein, and fat degradation in aerobic organisms, although technically, carbon dioxide, as carbonic acid, can also participate in the biosynthesis of some substances, particularly in plants. Catabolism and Anabolism Catabolism: degradative metabolism involving the release of energy and resulting in the breakdown of complex materials (as proteins or lipids) within the organism. Anabolism: the constructive part of metabolism concerned especially with macromolecular synthesis.

Catabolism: Anabolism:

KEGG Pathways
GenomeNet is a resource database developed by the Kyoto University Bioinformatics Center dedicated to provide computational devices to aid the study on the genome are various areas in biomedical sciences. The database provide tons of diversified maps of various metabolic pathways in humans and in plants. In addition, topics such as disease and drugs research are also mentioned and discussed in details with visual aids. Gene sequencing and analysis are also hugely emphasized at GenomeNet that provides many bioinformatic tools to in studying the genome sequence motif, phylogenetic research, and everything else you want to know about genes. One of the huge contribution from GenomeNet is providing KEGG maps and pathways that served as visual aids for studying of Matabolomics.

Kyoto Encyclopedia of Genes and Genomes or KEGG, is a database of biological systems which consists of genes, proteins, chemical building blocks, molecular pathways, hierarchies and relationships between various biological objects. KEGG links genomes to biological systems and maps pathways together to see the interactive cycle and the metabolites involved. You can also search through the KEGG website by entering a specific organism.

Primary Metabolites: Below are links that show the different pathways of all Primary metabolites. Primary metabolites encompass reactions involving compounds which are formed as part of the normal anabolic and catabolic processes. These processes take in the cells of the organism.

New Vocabulary: 1.	Orthologs, or orthologous genes, are genes in different species that are similar to each other because they originated from a common ancestor. 2.	Ohnologous genes are paralogous genes that have originated by a process of whole-genome duplication. 3.	Paralogs: a gene in an organism that is duplicated to occupy two different positions in the same genome, making the two copies paralogous. 4.	Xenologs: Homologs resulting from horizontal gene transfer between two. Xenologs can have different functions, if the new environment is vastly different for the horizontally moving gene. In general, though, xenologs typically have similar function in both organism 5.	Gametology: term which denotes the relationship between homologous genes on nonrecombining, opposite sex chromosomes. Gametologs result from the origination of genetic sex determination and barriers to recombination between sex chromosomes

Relevance: This website database relates to our course work and text book by compiling all of the metabolism pathways. It defines metabolites, characterizes them in their role they play in any pathway and gives us a clear view on the relationship it has with organism as a whole. This database can be used for research to further develop our understanding in the world of Metabolomics which will in turn help us understand and maybe even cure different illnesses.

MetaCyc
MetaCyc is a database for over 900 primary and secondary metabolic pathways from various organisms; it also contains associated compounds, enzymes, and genes. Similary to KEGG, MetaCryc database aid the studying of Metabolomics by providing visual aid of many metabolic pathways.

The exciting aspect of MetaCyc pathways is the detail explanation of the pathways. You can view all metabolites and their subclasses. (It also allows you to see all metabolites in relationship to each other and their hierarchy.) New vocabulary: 1.	Prostaglandin is any member of a group of lipid compounds that are derived enzymatically from fatty acids and have important functions in the animal body. Every prostaglandin contains 20 carbon atoms, including a 5-carbon ring. They are mediators and have a variety of strong physiological effects; although they are technically hormones, they are rarely classified as such. 2.	Cyclooxygenases: Prostaglandins are produced following the sequential oxidation of AA, DGLA or EPA by cyclooxygenases (COX-1 and COX-2) and terminal prostaglandin synthesis. 3.	Pseudo compound: is not necessarily a physical compound. For example light, or a mis match of DNA base pairs, could be considered pseudo compounds. 4.	Superatoms: are clusters of atoms that seem to exhibit some of the properties of elemental atoms. 5.	Taxon (plural taxa), or taxonomic unit, is a name designating an organism or group of organisms.

Relevance: This web site database relates to our course work and textbook in the same way the KEGG web page is. MetaCyc however allows for a user friendly specific search based on the organism. It also looks for closely on the genome.

The Human Metabolome Database
Human Metabolome Database is the most complete collection of human metabolite and human metabolism data in the world. It contains records for more than 2180 metabolites. It contains every metabolite and where you can find them (biofluids, tissue etc) it gives the molecular structure, weight and IUPC. The HMDB also contains a collection of experimental metabolite concentration data compiled from hundreds of mass spectra and Nuclear Magnetic resonance metabolomic analyses performed on urine, blood and cerebrospinal fluid samples. The HMDB contains compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, biofluid concentrations, disease associations, pathway information, enzyme data, gene sequence data, SNP and mutation data as well as extensive links to images, references and other public databases. Another aspect of the HMDB is Biofluid search which allows you to look at normal and abnormal concentrations of different metabolites for 7 different biofluids.

Universe-review.ca is a link that gives a brief summary on different essential metabolites. It goes through the structure of the molecule and their role in various metabolic pathways. Biosynth Chemistry & Biology is another site with specific information on Lipids. New Vocabulary: 1.	CAS registry numbers are unique numerical identifiers for chemical compounds, polymers, biological sequences, mixtures and alloys. They are also referred to as CAS numbers, CAS RNs or CAS #s 2.	Biofluid: encompasses any fluid found in the body, HMDB includes: Amniotic Fluid, Bile, Blood, Breast Milk, CSF, Feces, Lymph, Menses, Mucus, Saliva, Sebum, Semen, Sweat, Synovial Fluid, Urine, Vitreous Humour and Vomit. 3.	Sebum: oily substances are secreted by Sebaceous glands called sebum and the debris of dead fat-producing cells. 4.	Simplified molecular input line entry specification or SMILES: is a specification for unambiguously describing the structure of chemical molecules using short ASCII strings. SMILES strings can be imported by most molecule editors for conversion back into two-dimensional drawings or three-dimensional models of the molecules. 5.	American Standard Code for Information Interchange (ASCII), character encoding based on the English alphabet. ASCII codes represent text in computers, communications equipment, and other devices that work with text Relevance: While the past two data base are more specific for metabolite pathways, HMDB focuses on the metabolite composition and chemistry/ chemical interaction. This is relevant because HMDB really breaks down each metabolite to the molecular level. HMDB also allows for purchasing of these compounds.

http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1694820.com

Summary: The main point of this peer review article is to discuss the glycolysis process, which results in the formation of ATP as the main fuel in the anaerobic eukaryote, Monocercomonoides. Both ATP and Glucose are metabolites that relate directly to glycolysis and would be included in the organism's metabalome. Since anaerobic eukaryotes lack a complete tricarboxylic acid cycle, they can only extract few molecule of ATP per molecule of glucose. This is opposite of human cells, which makes this article important as it discusses alternate versions of enzymes in glycolysis. In anaerobic eukaryotes, enzymes such as pyruvate orthophosphate dikinase and pyrophosphate-fructose 6-phosphate phosphotransferase were examined through horizontal gene transfers from the bacteria in question to other eukaryotes. Horizontal gene transfers are processes in which an organism transfers genetic material to another cell that is not its offspring. Analysis of the glycolytic pathway of Monocercomonoides resulted in identification of both single-gene trees and conserved HGT events that provide evidence for the monophyly of protists known as Ecavata. Shared horizontal gene transfer events between oxymonads and both Giardia and Trichomonas further supported this relationship. This relationship becomes an important resource as it focuses on using glucose metabolites of Monocercomonoides to determine its lineage.

Terms: Horizontal Gene Transfer- process in which an organism transfers genetic material to another cell that is not its offspring Monophyly- a group of organisms that includes an ancestral species and all of its descendants. Oxymonad- a group of flagellated protozoa found exclusively in the intestines of termites and other wood-eating insects. Diplomonads- a group of flagellates, most of which are parasitic. Also classified as Protists. Walking primers-is a sequencing method for sequencing large DNA fragments (between 1,3 and 7 kilobases) that works by dividing long sequences into several consecutive short ones. Phylogenetic analyses- Analysis of the evolutionary connections between traits (for example, sequences) Taxa- Any organism or group of organisms of the same taxonomic rank; for example, members of an order, family, genus, or species.

Relevance: How does this information relate to the information that you have studied in this course to date? This resource is very closely related to our current coursework. As one would recall we had to memorize the complete glycolysis pathway, which is exactly what this article covered. It focused on the enzymes involved in the pathway, which are some that I have memorized. It also reviewed the process through an anaerobic prospective, which sheds light on how glycolysis can be altered but still produce ATP. This is very interesting as we mainly focused on just the one type of glycolysis.

Institute for Analytical Sciences
http://www.isas.de/english/menu-top/research/research-departments/metabolomics/

Summary: ISAS’s main focus is to create analytical methods that will be able to answer questions about a metabolites specificity, selectivity, and dynamic range. They want to be able to identify a metabolom in a single step (ex: from a sample of air breathed out by a person being able to identify dangerous metabolites that would signal cancer was present in the lungs). Another goal of ISAS is to detect differences at the single cell level that come from mutations and the varying environments that each cell is in.

Terms: Biogenic- necessary for the maintenance of life processes (http://www.thefreedictionary.com/biogenic) Mycotoxins-A toxin produced by a fungus. The term is usually reserved for fungal metabolites that are toxic to man and/or animals and are produced by molds growing on foodstuffs (e.g., aflatoxins, ergot alkaloids). (http://www.plantpath.cornell.edu/glossary/Defs_M.htm)

Microfluidics- Microfluidics is the science of designing, manufacturing, and formulating devices and processes that deal with volumes of fluid on the order of nanoliters or picoliters. The devices themselves have dimensions ranging from millimeters down to micrometers. (http://whatis.techtarget.com/definition/0,,sid9_gci526632,00.html)

Hyphal- a long, branching filamentous cell of a fungus, and also of Actinobacteria. In fungi, hyphae are the main mode of vegetative growth, and are collectively called a mycelium. (http://en.wikipedia.org/wiki/Hyphal)

Amperometric- relating to or being a chemical titration in which the measurement of the electric current flowing under an applied potential difference between two electrodes in a solution is used for detecting the end point (http://www.webster.com/dictionary/amperometric)

Voltammetry- is a category of electroanalytical methods used in analytical chemistry and various industrial processes. In voltammetry, information about an analyte is obtained by measuring the current as the potential is varied. (http://en.wikipedia.org/wiki/Voltammetry)

Chronocoulometry- The study of electrode surface properties, such as surface area. (http://www.answers.com/topic/chronocoulometry?cat=technology)

Cytostatic- inhibiting or suppressing cellular growth and multiplication (http://medical-dictionary.thefreedictionary.com/cytostatic)

Relevance: A lot of the techniques that ISAS uses so far are similar to subjects we have studied in the course. ISAS uses nuclear magnetic resonance spectrometry to evaluate intracellular and extracellular metabolites in human bowel tissues in an attempt to identify metabolites created by cancer cells. In Chapter 4 we learned that NMR can show the dynamic part of a components structure (folding, conformational alterations, and interactions with different molecules). Through the use of this method people could diagnosed with colorectal cancer earlier and through a less invasive method than before. Since cancer cells have mutated genes and create different metabolites than the colorectal cells normally would, a method being able to identify these products would be very helpful. In chapter 3 we learned about Mass Spec methods including MALDI MS, ESI MS, and MS/MS. ISAS uses ion mobility mass spectrometry in a lot of their projects including mold identification, analysis of breath to check for diabetes and lung cancer. Ion Mass Spec (IMS) detects low amounts of chemicals by using a homogenous electric field to see differences in migration of the gas phase ions. Where proteomics and genomics can show general drug treatments for problems, the Single Cell Laboratory research could be used to create specific drugs for each individual person’s unique cells and how their metabolome might be different.

Learn More
Additional Articles:

1)A metabolome study of the steady-state relation between central metabolism, amino acid biosynthesis and penicillin production in Penicillium chrysogenum

2)A critique of the molecular target-based drug discovery paradigm based on principles of metabolic control: Advantages of pathway-based discovery

3)Metabolomics - the way Forward

Images The following links are images, structures and diagrams of each previously mentioned metabolite. - Carbohydrate structures. – PubChem images for Carbohydrate. - PubChem images for Lipids. - Lipid - PubChem Amino Acids - All Amino Acids (formula and diagram) - Nucleotide PubChem - The big picture - Nucleotides – 4 Nucleotides - Co-factors PubChem PubChem Glycan

Metabolites Index
=Articles and Web Pages for Review and Inclusion=

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Metabolomics/Metabolites


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Metabolomics/Analytical Methods

Back to Previous Chapter: Hormones Next chapter: Computational Modeling of Metabolic Control
 * 1) NMR
 * 2) Mass Spectrometry
 * 3) Sample Preparation
 * 4) Fluids
 * 5) Tissues
 * 6) Statistical Methods

Analytical Methods Introduction
Metabolomics is a field where the discovery of specific cellular metabolites is essential to the field. Without the tools and methods to discover the metabolites, metabolite profiles and cellular processes unique to organisms cannot be discovered. Methods for discovering metabolomes range from technical chemical methods such as NMR to statistical analysis.

The Sumner Group
The Sumner Group is involved in the research of plant metabolomics. This is challenging because of 3 primary limitations. Profiling the metabolome is difficult because of its chemical diversity. Genomic and proteomic variance are limited by the basic components (nucleotides and amino acids), whereas metabolomes lack any sort of consistency. Another problem is dynamic range, which deals with measurement tolerance and sensitivity. The third problem is analytical and biological variance, which deals with deviations in measurement and in composition. The Sumner group is developing a way to get around these problems by dividing metabolites into different groups for analysis. The analytical techniques they develop are used to research disease in plants, and also to develop computational model organisms of plants.

Terms

transcriptome – the set of all transcripts produced by either a single cell or an entire organism principal component analysis – a statistical technique used to separate data into classes hierarchical clustering – a method to determine relationships or similarity between data sets. GC/MS (gas chromatography coupled to mass spectrometry ) - a way to profile large numbers of metabolites by separation (GS) and then measurement (MS) HPLC (high performance liquid chromatography) – a technique to separate both volatile and nonvolatile elements. Coupled to MS, it is another way to profile a large number of metabolites.

Relevance

The research of this group looks at metabolic regulation and its relationship to diseases in plants. This course looks at metabolic regulation, and problems within a pathway that can lead to disease. The area that is more relevant is the group's efforts at creating a model organism. The comprehensive understanding of an organism's metabolic regulation, depending on environmental conditions, relates to the regulatory mechanisms covered in class thus far.

Three Dimensional Cellular Microarray for High-Throughput Toxicology Assays
Three-dimensional cellular microarray for high-throughput toxicology assays

The main purpose of this paper was to develop a miniaturized 3D cell-culture array (DataChip) that would screen drug candidates for toxicity of a particular drug at the early stages of drug development. This is important due to amount of research put into drug development in today's world. The DataChip will hopefully select for drugs that cause adverse effects, remove them from development and help bring more drugs into the development process. This DataChip will be able to rapidly identify metabolic activation or deactivation of xenobiotics before they ever enter a human being. Xenobiotics are metabolized in the liver and in most cases, the liver is adversely affected by drugs. The DataChip’s aim is to significantly reduce the number of cases of liver failure due to xenobiotic metabolism, which is the leading cause of liver failure.

Terms:

Xenobiotics – chemical which is found in an organism but which is not normally produced or expected to be present in it

Microarray – biological assay

Alginate - viscous gum that is abundant in the cell walls of brown algae

P450 isoforms – (CYP1A2, CYP2D6, and CYP3A4) enzymes that are a part of the mixed-function oxidase system

IC50 values - or the half maximal inhibitory concentration

Hep3B cells – Human hepatoma cells

Fluorogenic - a process in which fluorescence is generated

Ketoconazole - synthetic antifungal drug used to prevent and treat skin and fungal infections

Relevance: In class, we were discussing different metabolic pathways in the human body and the different products they produce. We were also learning how all these processes are linked to one another. This paper talks about new ways to detect harmful side effects caused by the metabolism of a substance. Xenobiotics are broken down in the liver. Glycolysis and gluconeogenesis both take place in the liver as well. If the liver if damaged by xenobiotic metabolism, then glycolysis and gluconeogenesis would also be disrupted. This paper describes other metabolic process that occurs in the body and the possible side effects of the metabolites.

Recommended Reading
High Resolution Separations and Improved Ion Production and Transmission in Metabolomics

Metabolomics involves the detection and quantification of as many sample components as reasonably possible in order to identify compounds that can be used to characterize the samples under study. In electrospray ionization, ions are produced for analysis my mass spectrometry (MS). When applying this technique to metabolomics, it is important that metabolome sample constituents are efficiently separated prior to ion production, so that ionization suppression is minimized. This will ensure an extensive dynamic range of the measurement, as well as coverage of the metabolome. Measurement sensitivity may be increased by optimizing the MS inlet and interface.

Advancements in front end liquid chromatography (LC) separations, electrospray ionization and ion transmission efficiency have increased the sensitivity of liquid chromatography-mass spectrometry (LC-MS)-based measurements. An extended and more reproducible coverage of the metabolome has resulted in the detection of larger numbers of features characterized by accurately measured masses and retention times. Ionization efficiency has been improved with small i.d. columns that provide lower flow rates. This has led to greater sensitivity and better quantification. However, longer columns that provide high separation peak capacities produce lengthy analysis times that are not amenable in the analysis of large numbers of samples, which are required for statistically significant metabolomics studies. Alternative and complementary approaches, such as ionization electrospray-mass spectrometry (IMS-MS) or liquid chromatography-ionization microspray-mass spectrometry (LC-IMS-MS) are in development. These methods would provide higher throughput, which would allow for a moderately high coverage analysis of several hundred samples over the duration of a few days.

Molecular formula and METLIN Personal Metabolite Database matching applied to the identification of compounds generated by LC/TOF-MS

In an effort to simplify and streamline compound identification from metabolomics data generated by liquid chromatography time-of-flight mass spectrometry, software has been developed for constructing Personalized Metabolite Databases with content from over 15,000 compounds pulled from the public METLIN database. Extra functionalities have been added that permit the addition of user-defined retention times as an orthogonal searchable parameter; and allow interfacing to separate software, a Molecular Formula Generator (MFG), that facilitates reliable interpretation of any database matches from the accurate mass spectral data. In an effort to assess the utility of this identification strategy, retention times have been added to a subset of masses in this database that represent a mixture of 78 synthetic urine standards. The mixture was analyzed and screened against this METLIN urine database, which resulted in 46 accurate mass and retention time matches. Human urine samples had been analyzed under the same analytical conditions and were screened against this database. Another 374 had an accurate mass match to the database, where 163 of those masses had the highest MFG score. What’s more, MFG calculated a formula for an additional 849 ions that had no database matches. These results indicate that the METLIN Personal Metabolite database and MFG software offer an exemplary strategy for confirming the formula of database matches. If no database match is found, possible formulas are suggested.

Since a mass determination cannot assign elemental composition with absolute certainty, it has been suggested to complement database assignment of high mass accuracy data with other techniques such as isotope ratios and RT. It has been demonstrated that the METLIN Personal Metabolite Database software can be used to assign the correct elemental compositions for a set of urine metabolite standards. Including RT as a separate, orthogonal variable will allow for rapid, positive identification of the temporally resolved masses. Combining MFG capacity with mass and RT database matching is expected to increase the confidence with which both known and unknown compounds are assigned a correct elemental composition.

Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry

Fourier Transform Cyclotron Resonance Mass Spectrometry (FTICR-MS) offers unparalleled mass resolution, mass accuracy, and superb detection sensitivity. With these features, FTICR-MS is capable of becoming a powerful technique for high-throughput metabolomics analysis. This study examines properties of ultrahigh-field 12-Tesla (12T) in order to identify and quantify human plasma metabolites, and for the untargeted metabolic fingerprinting of inbred-strain mouse serum by direct infusion (DI). Rational elemental compositions (incorporated unlimited C, H, N and O, and a maximum of two S, three P, two Na, and one K per formula) of approximately 250 out of 570 metabolite features were detected in a 3-min infusion analysis of aqueous extract of human plasma, and were able to identify more than 100 metabolites. Isotopically-labeled internal standards were used to obtain high-quality calibration curves for the absolute quantitation of choline with sub-pmol sensitivity. This required 500 times less sample than previous LC/MS analyses. Optimal serum dilution conditions allowed for chemical compounds that were spiked into mouse serum as metabolite mimics to produce a linear response that spanned over a 600-fold concentration range. DI/FTICR-MS analysis was conducted on serum from 26 mice from 2 inbred strains, with and without acute trichloroethylene (TCE) treatment. This method had also been extended to the metabolomic fingerprinting of serum samples from 49 mice from 5 inbred strains that were involved in an acute alcohol toxicity study, with positive and negative electrospray ionization (ESI). It was demonstrated that more upwards of 400 metabolites could be profiled within 24 hrs, when applying DI/FTICR-MS to these samples.

Ultrahigh-field FTICR-MS has proven to be a potentially invaluable tool for qualitative metabolomics. The superior mass resolution facilitates the detection of hundreds of metabolites in complex samples in the DI mode. The mass accuracy frequently allows the identification of low-mass metabolites that are based solely on mass. This is accomplished by generating elemental compositions and then searching available metabolome databases.

Ultrahigh-field FTICR-MS can also be used to rapidly generate differential metabolite profiles in samples taken from large studies. However, DI/FTICR-MS is not comprehensive and therefore will not likely eliminate the need for chromatographic separations to achieve thorough metabolome coverage and to differentiate metabolite isomers. Software is currently being developed to enhance the potential of FTICR-MS for high-throughput metabolomics. This software would facilitate analyses by automatic detection of differentially-abundant metabolites, followed by their automated identification via elemental composition determination and metabolome database searching.

=Articles and Web Pages for Review and Inclusion=

Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements

Metabolomics approach for determining growth-specific metabolites based on Fourier transform ion cyclotron resonance mass spectrometry

LC-MS-based metabolomics in drug metabolism

title: Metabolomics/Analytical Methods


text:
Metabolomics/Computational Modeling of Metabolic Control

Back to Previous Chapter: Analytical Methods Next chapter: Databases

=Computational Modeling of Metabolic Control=

Metabolomics is a systems biology look at the interactions of metabolic pathways within an organism. Computational tools are useful and necessary to model these complex interactions and predict outcomes of perturbations of the system. A computational model of mitochondria and electrophysiological metabolism has been established, and applied to data collected from analyses conducted on cardiac mitochondria and phosphate metabolites in striated muscle, in vivo. The model is based on kinetic and thermodynamic details of reaction mechanisms of biochemical species. Building such an elaborate and kinetically encompassing model necessitated a vast collection of quantitative data from respiring mitochondria under specific closely monitored conditions. These details were then catalogued along with their results. Further validation of the model resulted from in vitro data measurements from cardiac muscle and in vivo measurements from skeletal muscle. The model was capable of predicting the roles of NAD and ADP in tricarboxylic acid cycle dehydrogenase regulation, and determined that NAD was a more significant regulator. The model was also capable predicting the effects of cytosolic pH fluctuation. Specifically, the model determined that decreases in the pH resulted in mitochondrial membrane potential reduction, which in turn decreased the rate of ATP synthesis.

Current research suggests that a vast quantity of independent data collected from experiments on in vivo and ex vivo systems would provide a comprehensive model of mitochondria metabolism. Established predictions based upon this model indicated the mitochondrial redox state as a primary regulator of tricarboxylic acid cycle flux. This model also reinforces results from similarly conducted research supporting the determination of inorganic phosphate strongly affecting the mitochondria redox state. Specifically, it was determined that inorganic phosphate influences the tricarboxylic acid cycle as a substrate and a cofactor. The current model also determined that mitochondrial ATP synthesis is directly dependent upon ADP and inorganic phosphate activation of oxidative phosphorylation, as well as NAD and inorganic phosphate activation of the tricarboxylic acid cycle.

A future prospect for this model is expected to involve analyzing calcium regulation of mitochondrial energetics. This will require including the established roles of Ca2+ in regulating pyruvate dehydrogenase, isocitrate dehydrogenase, and -ketoglutarate dehydrogenase within the model.

References: http://www.jbc.org/cgi/content/full/282/34/24525

E-Cell2 Simulation System
Available for download at http://www.e-cell.org

Main Focus:

The E-Cell2 simulation system is a publicly available simulation system for modeling, simulation and analysis of complex, heterogeneous and multi-scale systems like the cell. Within this package, a computational tool for mitochondrial systems biology has been included. The model was developed by integrating enzyme kinetics studies previously published in the literature. Instead of looking at pathways individually, the model integrates the respiratory chain, the TCA cycle, fatty acid B-oxidation, and metabolite transport systems to allow an observation of the dynamic behavior of the mitochondria as a whole. The model should allow investigators to evaluate the influence of metabolite treatments on the organelle as a whole. This model is flexible and allows the incorporation of other models created by independent researchers. E-Cell is implemented in C++ computer language.

Additional literature about the model: http://bioinformatics.oxfordjournals.org/cgi/reprint/20/11/1795

New Terms:

 * Lineweaver-Burk plots: Also known as a double reciprocal plot, this is a graphical representation of the Lineweaver-Burk equation of enzyme kinetics. This method was developed before nonlinear regression was available to fit curve data into straight lines.  The plot is used to provide a graphical analysis of the Michaelis-Menten equation and describe values for Vmax and KM.

 * Michaelis-Menten equation: This equation describes the relationship between the rate of substrate conversion by an enzyme to the concentration of the substrate. The equation is given below, where Km is the Michaelis constant, V is the rate of conversion, Vmax is the maximum rate of conversion, and [S] is the substrate concentration.

 * Fourth-order Runge-Kutta method: This is a method of numerically (approximately) integrating ordinary differential equations. It uses a trial step at the midpoint of an interval to cancel our lower-order error terms. The method is reasonably simple and robust, making it a good candidate for numerical solutions of differential equations. The method was developed by the German mathematicians C. Rung and M.W.Kutta.

 * TCA Cycle: Abbreviation for the tricarboxylic acid cycle.  This is also commonly referred to as the citric acid cycle or  the Krebs cycle (for its discovered Hans Krebs).

 * Quantitative Modeling: As opposed to experimental modeling which shows the general interactions of systems, quantitative modeling describes the precise concentrations of metabolite flux in and out of systems.  Quantitative modeling has been made possible through mathematical analysis of biochemical models and has become essential to understand the cell at a systems level.

 * Bifurcation: In general, bifurcation is the splitting of a main body into two parts.  In a dynamic system bifurcation is a period of doubling, quadrupling etc.

Connection to Biochemistry Metabolism Course:

The mitochondrial model includes processes we have studied in depth, including fatty acid B-oxidation (chapter 17) and the respiratory chain (chapter 20). It also includes inner-membrane transport system. We have looked at several transport systems, for example the citrate shuttle for acetate out of the mitochondria (chapter21). Another cycle included in the model, the TCA cycle or Citric Acid Cycle (chapter 16) was a large focus of our studies.

Computer Simulation of Metabolism:
Available at: http://www.hort.purdue.edu/cfpesp/models/models.htm

Main Focus:

The site has a good introduction to the utility of computational models for quantitatively analyzing traditional isotopic tracer methods and isotopic kinetic data. Experimental methods using stable isotope and radioisotope tracers to monitor kinetics of intermediates in metabolic pathways can be interpreted using computer routines to give insight into the fluxes and compartmentation of the pools of these metabolites. The site provides a breakdown of an example of a basic iterative computer model used to simulate labeling behavior of non-steady state intermediates. The important distinction is that iterative model can be applied to non-steady-state situations or to a situation with multiple pools of intermediates with different turnover rates, basic kinetic equations cannot.

New Terms:

 * Steady-state: Pools of intermediates and rates of reactions remain constant with time.

 * Non-steady state: Pools of intermediates expand and deplete with time.

 * Pulse-chase labeling kinetics: In this commonly used protocol, a cell sample is exposed to a radio-labeled compound for a brief period of time, referred to as a ‘pulse’. The quantity of labeling can be shown to be a function of the length of exposure time.  After exposure, the sample is washed with a buffer solution.  This removes the isotope.  Next, there is a ‘chase’ step where the sample is incubated with a non-labeled form of the compound.  These experiments are useful for following intra-cellular location of proteins or the transformation of a metabolite into others over a period of time/ through a biological pathway.

 * Effluxes: Refers to the movement of metabolites out of a cell or compartment.

 * Hill Coefficient: This is a measure of cooperativity in a binding process. It was originally worked out for the binding of oxygen to hemoglobin. A hill coefficient of 1 indicates independent binding, a hill coefficient greater than 1 indicates a positive cooperative binding of one ligand facilitates binding of subsequent ligands at other sites on the multimeric receptor complex.

 * GS/GOGAT Cycle: Responsible for glutamate synthesis.  Within this process ammonia is assimilated and recycled. The GS stands for glutamine synthase and GOGAT stands for an NADPH-dependent glutamine:2-oxoglutarate amidotransferase (or glutamate synthase).

Connection to Biochemistry Metabolism Course: Within this site pools of metabolic intermediates and multiple compartments are considered. We have explored the impact of compartmentalization as a form of regulation. For example in the regulation of hexokinase IV (glucokinase) when fructose 6-phosphate concentration in the liver is high, a nuclear binding protein draws hexokinase IV into the nucleus. This prevents it from acting in glycolysis. We have also looked at the significance of pools of activated intermediates (such as Acetly-CoA and succcinly-CoA) and reduced intermediates (such as NADH and NADPH) and the role their transport and availability plays in metabolic reactions.

JWS Online Cellular Systems Modeling
Available at http://jjj.biochem.sun.ac.za/index.html

Query for all organisms metabolic pathway simulations: http://jjj.biochem.sun.ac.za/cgi-bin/processModelSelection.py?organism=All&category=metabolism

Main Focus:

JWS Online is a systems biology tool for simulation of kinetic models from a curated model database. It provides access to a collection of published kinetics models divided into the categories of metabolism, gene expression, translation, cell cycle, enzymology, single transduction, and unknown. The models can be queried by this category and/or by the organism being modeled. Organisms available range from Homo Sapien to Dictyostelium. The models have been compiled from various authors (another query option), and are available for download or can be run within the website. Manuscript details (author, source, description, etc.) are also provided. Java applets are required to run the models. Within the models users can change enzyme parameters, run time simulations or do steady state analysis.

New Terms:

 * Curated Database: A curated database refers to a database that has been created and is maintained with a significant amount of ‘manual’ labor.  Its creators typically monitor and update the content of the sight as appropriate.

 * Enzymology: The branch of chemistry concerned with the properties and actions of enzymes.

 * The Silicon Cell Project: Research initiative that JWS Online is a part of.  The long term goal of this project is “the computation of Life at the cellular level on the basis of the complete genomic, transcriptomic, proteomic, metabolomic and cell-physiomic information that will become available in the forthcoming years”.  Those involved predicted the initiative would take a decade to complete, and it began in 2000. (No word available as to how close they feel they are to completion).

 * Armoracia rausticana: Misspelled within the site (actual spelling Armoracia rusticana), this is the scientific name for the plant horseradish.

 * Entamoeba histolytica: This is a type of anaerobic parasitic protozoan.  It infects primarily humans and other primates, leading  to amoebic dysentery or amoebic liver abscess.

 * Leishmania infantum: This parasite is an intracellular pathogen of  the immune system.   It targets macrophages and dendritic cells, causing the disease Leishmaniasis which affects millions of people worldwide. A more severe and life-threatening form of the illness is caused by a different species, L. donovani.

 * Lactococcus lactic: This is a mesophilic fermentative bacterium which produces lactic acid from sugar (hexose) fermentation. Strains of this bacterium are used in the production of fermented milk products.

Connection to Biochemistry Metabolism Course:

Within this database many different models of metabolic pathways are available. For example, glycolysis pathways from many different authors are offered for E. coli, H. sapiens, L. lactis, S. cerevisiae, sugar cane, and more. By clicking ‘run’ we can see the pathway, for example for glycolysis in S. cervisiae. For our studies it is interesting to then compare that to the glycolysis pathway in humans. The ability to perturb any cofactor, intermediate, or enzyme in the system and see its affect on the entire system reinforces the connections we have been studying all year. For example, increasing ATP will decrease formation of Fructose 1,6-bisphosphate.

GEM System: Automatic Prototyping of Cell-wide Metabolic Pathway Models From Genomes
By: Kazuharu Arakawa,1 Yohei Yamada,1 Kosaku Shinoda,1 Yoichi Nakayama,1 and Masaru Tomita BMC Bioinformatics. 2006; 7: 168. Published online 2006 March 23. doi: 10.1186/1471-2105-7-168. Copyright © 2006 Arakawa et al; licensee BioMed Central Ltd.

Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1435936%20-%20peer%20review%20article

Main Focus:

Being able to model cellular processes on a systems wide level is a labor intensive process. This article introduces a new tool, the Genome-based Modeling (GEM) System, which makes this task accessible. It is a tool that simulates cell-wide metabolic pathways using genomic sequence data and other biological information from public repositories. The genomic sequence data (annotated or unannotated) is used to create a rough metabolic network from publicly available databases and allows for more specific information to be added retrospectively. The system works by matching coding regions of the genome sequence with reaction stoichiometry for qualitative modeling. Next, a cell-wide simulation is created using these reactions quantitatively modeled through kinetic equations. The computer-based model produced allows researchers to simulate and study complex dynamic biological systems, analyzing system-level behavior and testing experimental hypotheses. Most previous models are developed manually and/or focus on small pathways when dealing with dynamic modeling. An Escherichia coli metabolism model developed by the GEM system achieved 100% coverage of the KEGG database model, 92.38% when compared with the EcoCyc database, and 95.06% when compared with the iJR904 genome-scale model.

New Terms:

 * Systems Biology: The study of the integrated and interacting network of genes, proteins, and biochemical reactions in an organism as a whole.

 * Bottom-up Approach: In this context, refers to beginning the process of molecular modeling with a great amount of expert knowledge and experimental data.  This information is combined to create a coherent model.

 * Top-Down Approach: Beginning with a complete model and using it to produce specific information. This is the approach taken by GEM by using genomic data to create a basic metabolic network, adding more specific information later.

 * Bottleneck: This is a process or event which creates a large backup in a system.  Manual modeling of cellular networks has been referred to as a bottleneck in systems biology because it is a slow process that causes a backup in the large amount of information which goes into it.

 * Systems Biology Markup Language (SMBL): A standard format for systems biology information which is computer-readable.  This format is required for input data in to many different simulation software packages.  The automated output of GEM can be concerted to SBML.

 * GLIMMER: This is a system for identifying genes in microbial DNA.  It is used within GEM to annotate genome data when unannotated data is given

 * MetaCyc Database: A database of non-redundant, experimentally elucidated metabolic pathways, containing over 900 pathways from more than 900 different organisms.  The pathways are involved in bot primary and secondary metabolisms, as well as associated compounds, enzymes, and genes. This database is used to check pathways generated using 	GEM.  http://metacyc.org/

 * COG: A database tool for genome-scale analysis of protein functions and evolution.  GEM 	uses this resource to help annotate genomic data.

 * EcoCyc: This is a scientific database for the bacterium Escherichia coli K-12 MG 1655.  The project performs a literature-based curation of the entire genome, and of transcriptional regulation, transporters, and metabolic pathways.  http://ecocyc.org/

 * iJR904 genome-scale model: A complete, chemical description of E. coli metabolism. It includes 904 genes and 931 unique biochemical reactions.

Connection to Biochemistry Metabolism Course:

We have discussed how homeostasis within living cells is maintained in a “steady state dynamic”. This refers to the idea that while the gross composition of the cell stays the same over time, the concentrations of materials in the cell are constantly changing in response to changes in the environment and the needs of the organism. The GEM systems tool is a way of modeling the dynamic interactions within the cell which correspond to these changes. This is powerful because, while understanding metabolic pathways is helpful, being able to simulate them and experimentally model their actions under set conditions is extremely useful in research.

In class and the text we have also been exposed to the KEGG pathway database. This database models an integrated system of pathways, including the relationships of these pathways to genomic data, chemical building blocks of substances, and networks of interactions and relationships. GEM uses KEGG as a standard reference. By stating that their tool achieves 100% coverage when compared with KEGG, they are stating that their tool was able to identify all of the relationships that the KEGG database dose. Since KEGG is a well accepted resource for this type of information, this comparison signifies that their tools produces significant results.

Computational Model of In Vivo Human Energy Metabolism During Semistarvation and Refeeding
By: Kevin D. Hall Am J Physiol Endocrinol Metab 291: E23-E37, 2006. First published January 31, 2006; doi:10.1152/ajpendo.00523.2005 0193-1849/06

Available at http://ajpendo.physiology.org/cgi/content/full/291/1/E23

Main Focus:

This paper describes a mathematical model that relates dietary marco-nutrient intake to computed whole body expenditure, de novo lipogenesis, gluconeogenesis, and turnover and oxidation of carbohydrate, fat, and protein. Using the classic Minnesota human starvation experiment and published in vivo human data the model simulates measured body weight and fat mass changes during semi-starvation and refeeding. In conjunction with this, unmeasured metabolic fluxes underlying these body composition changes were predicted. The study also looks at body composition in terms of fat and lean tissue in relation to the in vivo metabolic fluxes that regulate this composition.

New Terms:

 * Minnesota Human Starvation Experiment: Performed at the University of Minnesota from November 1944 to December 1945, this experiment is renowned for its comprehensive set of careful measurements taken over an extended duration of precisely controlled feeding.  The goal of the experiment was to investigate the physiological and physiological effects of severe and prolonged dietary restriction and the effectiveness of dietary rehabilitation strategies.

 * Lipolysis: The breakdown of lipids stored in the cell, or the hydrolysis of triacylglycereides to free fatty acids from fats.

 * Proteolysis: The breakdown of proteins in the cell.

 * PI: Protein intake rate in kcal/day

 * ProtOx: Rate of protein oxidation in kcal/day

 * FI: Fat intake rate in kcal/day

 * FatOx: Rate of fat oxidation in kcal/day

 * BCM: Body Cell Mass (in grams)

 * CI: Carbohydrate intake rate in kcal/day

 * CarbOx: Rate of carbohydrate oxidation in kcal/day

 * RMR: Resting metabolic rate in kcal/day

 * ProtOx: Rate of protein oxidation in kcal/day

 * Basal metabolic rate (BMR): The amount of energy normally required by an individual at rest in a neutrally temperature environment.

Connection to Biochemistry Metabolism Course:

As we have studied, the model showed connections between the daily content of body protein, glycogen, and fat and the daily average rates of proteolysis, glycolysis/ gluconeogenesis, and lipolysis, respectively. Based on what we have learned, in the starved state with a lack of these inputs (protein, glycogen, fat) we would expect a increase in proteolysis, a decrease in gluconeogenesis and an increase in glylcolysis, and an increase in lipolysis. This is exactly what the model showed in the semi-starved state. In the fed state, or refeeding state of the model, we see the opposite of these conditions.

An Enzyme Mechanism Language for the Mathematical Modeling of Metabolic Pathways
By: Chin-Rang Yang 1,3, Bruce E. Shapiro 4, Eric D. Mjolsness 2,3 and G. Wesley Hatfield 1,3,* Bioinformatics 2005 21(6):774-780; doi:10.1093/bioinformatics/bti068 Available at http://bioinformatics.oxfordjournals.org/cgi/content/full/21/6/774

Main Focus:

The goal of this project was to create mathematical models of common mechanisms that occur in metabolic pathways and other biological processes. The authors have developed kMech, a language extension of Cellerator, that models a suite of enzyme mechanisms. These enzyme mechanisms can be used within mathematical modeling of enzyme-related pathways. The reaction mechanisms are based on mass action kinetics and each generates a set of elementary reactions that are then translated into ordinary differential equations and association-dissociation reactions that can be solved by Mathematica. The simulations modeled by kMech procude graphic outputs. The program can be executed by Mathematica software installed in a Microssoft Windows, MacOS or Linux operating system. The advantage of kMech over traditional enzyme modeling approaches is that its reaction mechanisms incorporate multiple substrates, products, and regulatory mechanisms. It is a user-friendly tool which allows biologists to model biochemical pathway without knowledge of the underlying mathematics.

New Terms:

 * Cellerator: A tool for generating reaction network models of cellular processes

 * Mathematica: A widely used commercial computer algebra system that integrates numeric and symbolic computational engines with a graphical output and a programming language.

 * Mass Action Kinetics: Mass action kinetics states that the rate of a reaction if equal to the product of a rate constant (k) times the concentration of the substrate (S), also referred to as the mass.

 * Systems Biology Markup Language (SBML): A standard format for systems biology information that is computer-readable. This format is required for input data in to many different simulation software packages.

 * Steady-state Velocity Equations: Equations for enzyme modeling that set the derivatives of the concentrations of each reactant in the model to zero over time. This simplifies a set of non-linear differential equations to linear algebraic equations.

Connection to Biochemistry Metabolism Course:

The design of kMech allows it to model many single and multiple substrate enzyme mechanisms such as those we have studied in class, including feedback inhibition by allosteric, competitive and non-competitive mechanisms. These regulation mechanisms have been a focus of our course. One example of a model we have studied that falls into the category of allosteric inhibition is fructose 1,6-bisphosphatases inhibition by fructose 2,6-bisphossphate and AMP.

Articles and Web Pages for Review and Inclusion
Peer-Reviewed Article #1:

Computational Modeling of Cancer Cachexia

'''Curr Opin Clin Nutr Metab Care. 2008 May; 11(3): 214–221. '''

Main Focus
 * Identify the main focus of the resource. Possible answers include specific organisms, database design, intergration of information, but there are many more possibilities as well.

New Terms
 * New Term 1: Definition. (source: http://)
 * New Term 2: Definition. (source: http://)
 * New Term 3: Definition. (source: http://)
 * New Term 4: Definition. (source: http://)
 * New Term 5: Definition. (source: http://)
 * New Term 6: Definition. (source: http://)
 * New Term 7: Definition. (source: http://)
 * New Term 8: Definition. (source: http://)
 * New Term 9: Definition. (source: http://)
 * New Term 10: Definition. (source: http://)

Summary
 * Enter your article summary here. Please note that the punctuation is critical at the start (and sometimes at the end) of each entry. It should be 300-500 words. What are the main points of the article? What questions were they trying to answer? Did they find a clear answer? If so, what was it? If not, what did they find or what ideas are in tension in their findings?

Relevance to a Traditional Metabolism Course
 * Enter a 100-150 word description of how the material in this article connects to a traditional metabolism course. Does the article relate to particular pathways (e.g., glycolysis, the citric acid cycle, steroid synthesis, etc.) or to regulatory mechanisms, energetics, location, integration of pathways? Does it talk about new analytical approaches or ideas? Does the article show connections to the human genome project (or other genome projects)?


text:
Metabolomics/Databases

Back to Previous Chapter: Computational Modeling of Metabolic Control Next chapter: Applications

=Overview=

The vast amount of metabolomic information harvested using high-throughput techniques has necessitated an effective means of storage to organize, disseminate, and facilitate analysis and annotation. This need has driven the development of databases as a repository of metabolomic data being produced. Data housed in these databases covers the wide-spectrum of research being done in the metabolomic world from NMR spectra to metabolic pathway substrates and products.

Metabolomic database serve a primary purpose or organizing information on the large catalog of metabolites that are encountered in metabolism pathways. There are many different databases that exist on the World Wide Web and house a wide variety of information covering a large variety of organisms.

Biological Magnetic Resonance Data Bank
The Biological Magnetic Resonance Data Bank (BMRB) focuses on quantitative data generated by spectroscopic investigations of biological macromolecules. It has links to search engines such as PubChem, that connect to recent articles and new data. It also links to projects and other databases that are all related to Metabolomics and Metabonomics. This database focuses on the NMR research aspect of metabolites discovery and what roles they play in metabolism. BMRB offers a large list of different known compounds and the information associated to it.

Terms:
 * Metabonomics (verses Metabolomics): The words 'Metabolomics' and 'Metabonomics' are often used interchangeably, though a consensus is beginning to develop as to the specific meaning of each. The goals of Metabolomics are to catalog and quantify the myriad small molecules found in biological fluids under different conditions. Metabonomics is the study of how the metabolic profile of a complex biological system changes in response to stresses like disease, toxic exposure, or dietary change
 * Metabolites: low molecular weight molecules.
 * Diamagnetism: A weak repulsion from a magnetic field. It is a form of magnetism that is only exhibited by a substance in the presence of an externally applied magnetic field. It results from changes in the orbital motion of electrons. Applying a magnetic field creates a magnetic force on a moving electron in the form of F = Qv × B. This force changes the centripetal force on the electron, causing it to either speed up or slow down in its orbital motion. This changed electron speed modifies the magnetic moment of the orbital in a direction opposing the external field
 * Calmodulin: An example of calcium binding regulatory proteins in intracellular signaling pathways. It is highly conserved and abundant in all eukaryotic cells. As a signaling protein, Calmodulin's function is to bind calcium ions and then bind a target protein, affecting its activity. It affects processes ranging from neurotransmitter release to membrane protein organization
 * Heuristic: A method to help solve a problem, commonly informal. It is particularly used for a method that often rapidly leads to a solution that is usually reasonably close to the best possible answer.

Relevance: This information relates to what we have studied in class because we have been studying metabolism and the metabolites involved. This resource is simply a collection of all the accountable knowledge that exists. The field of metabolomics is growing and with the help of NMR spectroscopy more compounds and metabolites will be discovered along with their functionality. The information studied in class forms the foundation for this knowledge

Metabolomics: Resources, Reagents, and Kits for Metabolomic Analysis
The Sigma-Alderich database provides access to a number of metabolomic kits and reagents, as well as a number of resources, including information on cell signaling pathways, enzyme structures/functions/specificities, animations, charts, and an online library. This site also provides links to other resources.

Terms:
 * Cytokines: a group of proteins and peptides used in organisms as signaling compounds. Consists mainly of small water-soluble proteins and glycoproteins. Also play a central role in the immune system.
 * Metabolome: a collection of all the metabolic products and intermediates found in an organism.
 * Angiopoietin: protein growth factors that promote the formation of new blood vessels. Only four identified angiopoietins: Ang1, Ang2, Ang3, and Ang4.
 * Phosphoproteomics: a type of proteomics involved in identifying, cataloging, and characterizing proteins containing a phosphate group as post-translational modification.

Relevance: This website shows cell signaling and other metabolic pathways (including glycolysis) in an animated, in-depth way. This site also provides a search feature to find pathways related to molecules of your choosing.

Madison Metabolomics Consortium Database
The Madison Metabolomics Consortium Database contains metabolites determined through NMR and MS. It contains information with the main focus on Arabidopsis thaliana, but also refers to many different species. The database also contains information on the presence of metabolites under several different physiological conditions, their structures in 2D and 3D, and links to related resource sources and other databases.

Terms:
 * Nuclear magnetic resonance spectroscopy (NMR): technique using nuclear magnetic resonance to determine structural information about a molecule.
 * Mass spectroscopy (MS): technique that uses the mass-to-charge ratio of ions to determine the composition of a sample.
 * Arabidopsis thaliana: the thale cress, a species of plant with a small genome and rapid life cycle that is a model organism in the lab.
 * chemoinformatics: the use of computer and informational techniques, applied to a range of problems in the field of chemistry
 * Chemical shift: relevant to NMR, chemical shift describes the dependence of nuclear magnetic energy levels on the electronic environment of a molecule.

Relevance: How does this information relate to the information that has been studied in this course to date? Using this website, it is possible to enter a molecule of interest into the search engine and obtain links that will lead to a list of pathways in which that molecule participates. Doing this for glucose, two pathways with which were covered in class: starch degradation (aka glycolysis) and glycogen degradation were displayed.

MetaCyc
The main focus of the MetaCyc Database is to collect and display information on experimentally studied pathways from a variety of organisms. Pathways are divided into five categories: biosynthesis, degradation/utilization/assimilation, detoxification, generation of precursor metabolites and energy, and Super-Pathways. Clicking on any of these will open, in outline format, more specific categories. This eventually leads to individual Metabolomes that are described graphically. There is also descriptions with details about their history and connected pathways. The database can also be browsed by compounds and reactions, though these sections tend to be less detailed.

MetaCyc allows anyone to submit newly identified pathways, but they unsurprisingly demand detailed, experimentally proven data which is closely examined before any additions are curated.

Terms:
 * Superatom: A cluster of atoms that has the same behavior as elemental atoms.
 * Prostaglandins: A group of lipid compounds found in a wide variety of tissues that are synthesized from essential fatty acids. Cells have several receptors for prostaglandins that lead to actions ranging from smooth muscle constriction to increasing spinal neurons' sensitivity to pain.

Relevance: MetaCyc is closely related to the material that we have been learning about in class because it is a comprehensive database that covers many of the same pathways, such as glycolysis I (http://biocyc.org/META/NEW-IMAGE?type=PATHWAY&object=GLYCOLYSIS)

The Scripps Center for Mass Spectrometry: Metabolomics Science Webpage
The main focus of the Scripps Center for Mass Spectrometry is to provide a user-friendly websites for scientist in the field of Metabolomics. They provide general information on analytical tools, timelines of Metabolomics history, Metabolomic events held around the world, databases of metabolic systems, as well as bioinformatics software.

Terms:
 * Pathophysiology: the physiology of abnormal or diseased organisms or their parts; the functional changes associated with a disease or syndrome.
 * Lipidomics: deals with Lipids studying not only their structures, but also functions and modifications occurring during physiological and pathological conditions.
 * Exdogenous: of or noting the metabolic assimilation of proteins or other metabolites, the elimination of nitrogenous catabolites being in direct proportion to the amount of metabolites taken in.
 * Ernobiotic: a chemical or substance that is foreign to an organism or biological system.
 * Paraccetamol: The generic name for a common nonprescription medication useful in the treatment of mild pain or fever.
 * GC-MS: gas chromatography mass spectrometry
 * CE-MS: capillary electrophoresis mass spectrometry
 * FT-IR: Fourier transform infrared spectrometry

Relevance: This Website relates to the information that we have been studying in class because it is it full of information about pathways and numerous databases. One such database is the KEGG Pathway Database, which contains all the pathways that are involved in metabolism. It shows such pathways as Glycolysis, Gluconeogenesis, Citrate cycle, pentose phosphate pathway, glactose metabolism, pyruvate metabolism, and hundreds more. Click here to check out they glycolysis pathway -> http://www.genome.jp/kegg/pathway/map/map00010.html This website does a good job of showing how all the pathways are interconnected into one another.

The Human Metabolome Database
The Human Metabolome Database is an extremely comprehensive, free electronic database that gives a detailed overview of human metabolites divided into chemical, clinical, and molecular biology/biochemistry data.

Terms:
 * Human Metabolome Project: The HMB is an ambitious Canadian project begun over 	3 years ago with the ultimate goal of “identifying, quantifying and cataloging” every 		metabolite detectable in human tissue at concentrations greater than 1 micromolar.
 * Biofluids: A biological fluid such as urine, blood or sweat. In this database, metabolites can be categorized by their biofluid localization.
 * Chemical Class: A broad term used to categorize organic and inorganic chemicals based on common characteristics into groups such as amines and carbohydrates. The database 		can be browsed by chemical class.
 * Metabocard: The individual datasheets for metabolites in this database are called metabocards.  Each one contains a detailed description, over 90 categories of data, and 		cited sources.  An example metabocard for citric acid can be found at: 	http://hmdb.ca/scripts/show_card.cgi?METABOCARD=HMDB00094.txt
 * TOCSY: Total correlation spectroscopy in which magnetization through chemical 	bonds of adjacent protons and protons connected by adjacent protons is visualized.   An

Relevance: The Human Metabolome Database is connected to our coursework by 	its 	extremely thorough amount of data on all of the metabolites that we've been studying. Reaction intermediates and products such as glucose, 3-phosphoglycerate, and citrate can all be 	looked up and everything from the 3d structure to associated disorders are provided.

KNApSAcK
KNApSAcK is a Java application that presents an interactive display of biochemical 	information that can be searched by organism or metabolite name. KNApSAcK focuses 	primarily on the origin and mass spectra of particular metabolites.

Terms: Relevance: KNApsAcK connects to our coursework because it allows for 	comparison of 	metabolites important to different organisms. One example search that was	attempted was to see 	the metabolites shared by cyanobacteria and plants for photosynthesis.
 * JRE: The Java Runtime Environment is a set of free software programs that are used by 		many internet developers to run java programs and scripts on users' computers.
 * Mw +- margin: A search parameter of KNApSAcK that allows a user to search for 			metabolites within a gram range of a set number.  For example, searching for MW: 100 			with a margin of 2 would return all metabolites with a molecular weight between 98 and 		102 grams.
 * Phylum: The fourth taxonomic rank for classifying organisms, between kingdom and 			class. Cyanobacteria is one phylum.  The database allows searches based on any 			specifying any taxonomic rank, although the higher ones take significant time to load.
 * m/z: The mass-to-charge ratio, a physical quantity that is used in the detailed 				examination of charged particles. It is a key aspect of mass spectrometry studies and the 		database focuses heavily on this data.

BRENDA
The BRENDA developers boast that it is the main internet repository of 	functional enzyme data of the scientific community. An extremely robust system, it allows for 	searching of more than 4000 enzymes and provides comprehensive information on each of 	them, including indispensable reaction diagrams.

Terms:
 * ECTree: A term for the outline organization BRENDA uses to characterize related 			enzymes. An example image of an ECTree for Oxidoreductases from the user manual:
 * TaxTree: TaxTree is the interactive display used by BRENDA to search for organisms 			by taxonomy. Once an organism or taxonomy designation is chosen, all of the enzymes 			in the database linked to it are displayed.
 * Substructure Search: The substructure search function allows a user to actually draw 			part of the enzyme structure in skeletal formula. All enzymes containing the component 		drawn are returned.
 * EC Explorer: A search function that allows the user to access enzyme information by a 		several criteria including common name, reaction, and even history.
 * Systematic Name: A style of naming enzymes controlled by the Enzyme condition. 			The enzyme is categorized by four numbers classifying its main class, subclass, sub-subclass and serial number which are all separated by periods.

Relevance: Information on this database is reinforces what was covered in class. Material covered in class also is the foundation for the material on this database

Reactome
The Reactome is a collaboration between Cold Spring Harbor Laboratory, The European Bioinformatics Institute, and the Gene Oncology Consortium to provide a curated database that catalogs core pathways and reactions in human biology. The Reactome obtains information from researchers with expertise in their fields and is cross-validated by an Reactome editorial team which references other databases such as the NCBI, Ensembl, and UniProt. Alongside the human pathways and reactions the Reactome also contains inferred data from 22 non-human species including mouse, rat, chicken, puffer fish, worm, fly, yeast, two plants and E.coli.

Current versions of the Reactome allow for searching by keyword but also allow a more visual approach by allowing researchers to view a map of much of the data being housed in the database and allowing reactions to be selected and zoomed in on from the top level.

Terms
 * Skypainter tool: A tool provided by the Reactome which allows a list of proteins or gene identifiers to be uploaded to color one of the reaction or pathway maps generated by the database.
 * Morbid map: A diagram showing chromosome location of genes that are known to be associated with disease.
 * Reactome Author Tool: Desktop application written in Java that is utilized to enter new data into the Reactome. Use a graphical interface to allow for ease in expanding or adding reactions and pathways.
 * BioPAX: an attempt at a common exchange format for biological pathway data.
 * SBML: Systems Biology Markup Language; a computer-readable format representing biochemical reaction networks.
 * PSI-MI: Proteomics Standards Initiative - Molecular Interactions; A standardized format which described molecular interactions.

Relevance: Much of the data housed in the Reactome database covers many of the pathways and reactions we have covered in the course such as the intermediary metabolism and regulator pathways. Like many of the other metabolomics database it can be thought of almost like a textbook containing thousands of entries on metabolism and its associated events.

KEGG Pathway DB
The KEGG Pathway Database is a large part of a collection of smaller databases which comprise the Kyoto Encyclopedia of Genes and Genomes. The Pathway database is known for its extensive collection of metabolic pathways and its handling of their interconnections, as well as other non metabolic cellular interactions. The database does an excellent job of integrating genomic, chemical and systemic functional information into an easily readable format.

Instead of new terms, enjoy this list of subsections of the database.
 * 1.1 Carbohydrate Metabolism
 * 1.2 Energy Metabolism
 * 1.3 Lipid Metabolism
 * 1.4 Nucleotide Metabolism
 * 1.5 Amino Acid Metabolism
 * 1.6 Metabolism of Other Amino Acids
 * 1.7 Glycan Biosynthesis and Metabolism
 * 1.8 Biosynthesis of Polyketides and Nonribosomal Peptides
 * 1.9 Metabolism of Cofactors and Vitamins
 * 1.10 Biosynthesis of Secondary Metabolites
 * 1.11 Xenobiotics Biodegradation and Metabolism

BMRB, MMCD and the Sesame laboratory module
Databases have been recently developed as metabolomics resources. Some of the databases that have been designed as metabolomics resources are intended to assist in MS and NMR analyses of relevant research. Among these particular databases are the BioMagResBank (BMRB), Madison Metabolomics Consortium Database (MMCD) and a module for the Sesame laboratory information management system.

The BMRB comprises of experimental spectral data for over 270 pure compounds. Each molecule entry includes five or six one- and two-dimensional NMR data sets, as well as compound source information, solution conditions, data collection protocol and the NMR pulse sequences. Database entries can be accessed by name, monoisotopic mass and chemical shift. Currently in development is an open access feature to this database that will allow users to contribute their own data, and substantiate the BMRB.

The MMCD consists of information on over 10,000 metabolites that primarily consists of data collected from Arabidopsis metabolites. Users may make queries comprising of MS and/or NMR spectra.

The Sesame laboratory module collects all metabolomics based experimental protocols, background information, and data for a particular study.

Link to article:

http://psb.stanford.edu/psb-online/proceedings/psb07/markley.pdf

CellCircuits: a database of protein network models
General Overview: This article provides a rationale for the development of CellCircuits, an open-	access database that focuses on molecular network models. The database covers models that 	have been derived computationally and posted in published journal articles. The article explains 	that the ultimate goal of the project is to bridge the gap between molecular databases, even 	those with unconfirmed data, and strictly regulated pathway databases. The body of the article 	explores not only the rationale of CellCircuits, but the computational process that went 	into 	developing it and some example results of molecular networks models.

Terms:
 * GO Annotation: GO refers to the Gene Ontology project, which is a system of universal 	descriptions of genes across a broad variety of databases. The developers of CellCircuits have 	used GO to score genes in comparisons across databases.

 * Data Processing Pipeline: A pipeline is a construct used to carry data through threads, scripts, 	and processes through a chain of software elements. CellCircuits uses pipelines to draw text 	information from input models for processing.
 * MySQL: MySQL is a database management system that allows users to easily set up multi-	platform systems of data control and is very popular for internet applications, Wikipedia being 	one example. CellCircuits is built with MySQL.
 * Scoring models: Scoring models is a concept that refers to a system for comparison of two 	sets of data. In  CellCircuits, scoring models are used in conjunction with the GO database to 	compare sets of genes from input models.
 * Perl: Perl is a popular procedural programming language heavily derived from C. The 	primary graphical interface of CellCircuits is written in Perl.

Relevance: This article relates to our coursework because it shows some of the dizzying 	heights of complexity involved in trying to collate the growing body of metabolomics data into 	a usable form for the general science community.

ProMEX: a mass spectral reference database for proteins and protein phosphorylation sites
General Overview – This article explains the development and use of the ProMEX mass spectral 	library database. The goal of the expanding database is to allow users to compare an unknown 	sample to a body of confirmed mass spectra for known proteins. The article explores some of 	the theory and algorithms that go into making that possible.

Terms Threshold: In comparing the mass spectra of an unknown, user provided sample against those 	in the database, the threshold value is point at which mass spectra hits are ignored because they 	are not considered matching.
 * Metadata: A common database term that refers to the overlying information on an object rather 	than discrete points. The ProMEX developers use it to refer to the consensus of experimental 	results and mass spectrometric parameters.
 * AGI Codes: A uniform system of nomenclature to classify genes that was developed in 1999. 	AGI codes reference the organism, chromosome number, gene, and gene ID.  The 	announcement of the original decision to create the system for Arabidopsis genes can be found 	at: http://mips.gsf.de/proj/thal/db/about/agicodes.html
 * LC-MS: Liquid chromatography- mass spectrometry is an uncreatively named data gathering 	process that combines the two techniques to allow for highly sensitive detection of specific 	chemicals. The ProMEX developers use LC-MS to distinguish even closely related samples.
 * CLR: Common Language Runtime is a virtual machine developed by Microsoft. It provides 	an execution environment for software programs on a variety of platforms.  The ProMEX's 	algorithms  for comparing spectra run within the CLR.

Relevance – ProMEX is a relevant resource to our coursework because it shows 	how quickly the field of metabolomics is advancing. Using the search algorithms described in 	this article, users can now identify unknown proteins from experimental data in a quick and 	highly automated process.

Correcting ligands, metabolites, and pathways
General Overview – The authors of this article explain that the goal of their database, Biometa, 	is to provide an example of the need to correct inaccurate pathways and chemical structures. After originally developing this database, they came up with tools to validate the data it 	contained by stereochemistry and stoichiometric outcomes only to find that they had a high 	error rate. The article explains the creation of the database and validation tools and the steps 	they took to make corrections.

Terms
 * Xenobiotics: Chemicals that can be experimentally or clinically detected in an 	organism that 	is not capable of normally producing them, either at all or at the 	concentration 	that they 	appear. The authors of the article use xenobiotics as an example 	of the staggering intricacy of 	metabolomics.
 * Reactants and Products: Although these are not new terms, their use in BioMeta is 	significant because the authors deliberately eschewed use of “substrate,” feeling that it was 	inappropriate for their purposes on the grounds that it can refer to either reactant or product of 	an enzyme and they are only interested in catalyzed reactions.
 * “Fuzzy” Synonyms: To deal with a lack of uniform nomenclature, BioMeta contains 	synonym tables that recognize many common names for compounds or pathways. If an initial 	search can't locate a synonym, it is referred to a table of fuzzy synonyms that strips out non-	alphanumeric characters and capitalizes all letters for a looser, but still automated, comparison.
 * ElemCount: ElemCount is one field used by the BioMeta Compounds data table that covers 	the raw quantity of each element in a compound. Searches can be made with it specifying a 	minimum or maximum number
 * Molfiles: Molfiles are minute structure descriptions of small compounds that can be quickly 	analyzed and validated by the developers' chemical structure software.
 * Canonicalization: The concept of recognizing several synonymous data references as a single 	reference. The validation tools used by the developers make heavy use of canonicalization at 	most steps to reduce repetitive comparisons and false error reports.

Relevance – This article is relevant to our coursework because it explains the logical 	eventuality that with the incredibly vast amount of metabolomics data and speed at which it is 	growing, errors are inevitable. The authors offer some insight into how this problem can be 	corrected and the necessity of doing so. The compound query window from the BioMeta database.

HMDB: The Human Metabolome Database
The Human Metabolome Database (HMDB) was established in 2004 with the explicit aim to catalog the whole metabolome in humans just as the Human Genome project unraveled the mysteries behind our genetic code. This paper covers the information contained in the database, which includes compound description, synonyms, physo-chemical structure, disease association, pathways information, and NMR Spectra and MS spectra among other things; each entry in the database contains 90 entries filled with relevant information. The paper also serves as a design documentation for the database, detailing how it was built with care to allow for efficient searching as well as explaining the quality control and curation of the database.

The HMDB is built upon a MySQL database that serves as the backend to to the graphical web-page interface. Raw text found in the database is translated to HTML via special Perl scripts that also generate links and graphics. The MySQL database is part of a generalized metabolomic LIMS system called MetaboLIMS that utilizes Java to handle input and queries.

The robustness of the database allows researchers to search from many different angles including by chemical structure, BLAST, single and multiple sequences, MS and NMR spectra, and boolean text searches via GLIMPSE.

Terms
 * Biomarker: A biochemical feature that can be used to detect or measure a disease or the effects of a treatment.
 * Medical Informatics: A field of information science that primarily deals with the analysis and distribution of medical data through the use of computers. This data can be applied to different areas of health care and medicine.
 * GLIMPSE: Global Implicit Search; an indexing and query scheme for searching file systems.
 * SimCell: A metabolic simulation software package which models complex metabolic pathways at the cellular level with real-time movies of the enzymatic process.  These movies can also be graphed by the package.
 * Nutrigenomics: The study of how foods interact with genes as to increase the risk factor of chronic disease.

Relevance: The information housed in the HMDB can be traced through all of the coursework that has been covered so far. Many of the metabolites housed in the HMDB were directly discussed in both the textbook and the lectures covered in class. Of course this is a surface connection between the information the text and this paper as the HMDB and other metabolic databases really encompass the majority of the metabolism world as they serve as as a repository under which all past and future research data can be stored.

Toward Pathway Engineering: A New Database of Genetic and Molecular Pathways
The KEGG database was created with the sole purpose of providing a diagram of molecular and genetic interactions to aid in the understanding of biological systems. Its creation was fueled in part by the completion of the Human Genome Project as a way to take this massive amount of information and place it in the proper locations in a system. KEGG is connected to DNA and Protein databases by integration with the tool DBGET, which acts to search across databases.

Terms
 * DBGET: An integrated database retrieval tool to search across databases.
 * GenomeNet: A network that establishes a informatics framework for genome research and related areas.
 * Φx174: Small virus genome consisting of 11 genes; one of the first viruses sequenced.
 * Superfamilies: A classification scheme to group proteins.
 * Boehringer wall chart: A classic biological pathway chart.

Relevance: The KEGG database is just another entry in the long line of databases which sum up much of the metabolic pathway information we have learned in class.

=Articles and Web Pages for Review and Inclusion=

Nutritional Metabolomics Database

A Liquid Chromatography-Mass Spectrometry-Based Metabolome Database for Tomato

Plant Physiology 141:1205-1218 (2006)

title: Metabolomics/Databases