Biology is the field of sciences that is increasing day by day; it is in the midst of experimental change. The discipline of Metabolomics is moving from being a data poor science to a data rich science. Besides transcriptomics and proteomics, Metabolomics has emerged as third major path of functional genomics in the field of sciences. Just as genomics is the omics for DNA sequence analysis, proteomics is the omics approach to understand the structure and function of protein in cell, Metabolomics is the omics approach to understand cell and systems biology level, combined with information obtained on transcriptome and proteome, this would lead to nearly complete molecular picture of cell, its environmental state, effect of external condition on cell's product at a given time. 
Protein is very dynamic in nature and thus changes very rapidly in accordance with its external environment, this is the reason that after completion of gene sequencing projects, scientific interest is shifting to the investigation of the proteome and metabolome. Broadly speaking, the metabolome can be considered as the dynamic complement of metabolites formed by or found within a cell type, tissue, body fluid or organism. 
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Under a given set of conditions, Metabolomics study the global metabolite profiles in a system (cell, tissue, or organism). Metabolites are the small organic, chemical molecules present in the cell. Due to the diverse chemical nature of metabolites, the analysis of the metabolome become challenging. Metabolites are the result of the interaction of the genome with its environment and are not merely the end product of gene expression but also form part of the regulatory system in an integrated manner. Metabolite profiling studies are the basis of Metabolomics, but now it is becoming a popular field of study which is rapidly expanding. 
Metabolomics is the new 'Omics' joining genomics, transcriptomics and proteomics to understanding system biology. It is a large-scale study of all metabolites present in cell, tissue or organs usually by high throughput screening. [2,3,4] Metabolomics identify and quantify the complete set of metabolites present in a cell or tissue at a particular set of time and conditions. It is a key aspect to phenotype hence, describing the distribution of metabolites is next logical step in elaboration of functional genomics and may be the best and most direct measure of cellular morphology. [5,6]
Metabolomics is comprised of two words: Metabolome and Omics.
Metabolome or Small Molecule inventory (SMI) is defined by entire complement of low molecular weight, non-peptide metabolite with in a cell or tissue or organism at a particular physiological rate. It defines metabolic phenotype thus is an important biochemical manifestation and useful tool for functional genomics. Another definition states that metabolome consists only of those native small molecules (definable non polymeric compound) that are participant in general metabolite reactions and that are required for maintenance, growth and normal function of a cell.
"Omics" technologies are based on comprehensive biochemical and molecular characterization of an organism, tissue or cell type. Omics is a high-through put screening based on biochemical and molecular characterization of an organ, tissue, or cell type [7,8]. Metabolomics represents the logical progression from large-scale analysis of RNA and proteins at the systems level .
Metabolomics deals with the quantification of all or a substantial fraction of all metabolites within a biological sample and simultaneously identifying and quantifying their respective classes of biomolecules such as mRNAs, proteins and metabolites. While the genome is representative of what might be proteome is and what it is expressed; it is the metabolome that represent the current status of the cell or tissue. To understand the basic metabolism and chemistry of metabolites, biochemical pathways should be first understood . Measurement of metabolite provides basic information about biological response to physiological or environmental changes and thus improves the understanding of cellular biochemistry as networks of metabolite feedback regulate gene and protein expression and mediate signal between organisms. Metabolomics allows a shift from hypothesis driven research to the analysis of system-wide responses, especially when it is integrated with other profiling technologies.
At the analytical level Metabolomics rely on comprehensive profiling of large number of gene expression products known as transcriptomics, proteomics and metabolomics.
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Metabolomics is a direct approach to reveal the function of genes involved in metabolic processes and gene-to-metabolite networks. It offers a quick way to elucidate the function of novel genes and play important role in future plant, nutrition and health, drug toxicity etc. Metabolism is the key aspect of phenotype, hence describing the distribution of metabolites in next logical step in elaboration of functional genomics. It is useful wherever an assessment of change in metabolite concentration is needed. In order to elucidate an unknown gene function, genetic alteration is introduced in system by analyzing phenotyping effect of such a mutation i.e. by analyzing the metabolome functions may be assigned to respective gene .
Metabolites are the result of interaction of system's genome with its environment and are not merely end product of gene expression but also from part of regulatory system in an integrated manner and thus can define biochemical and phenotype of a cell or tissue. Thus its quantitative and qualitative measurement can provide a broad view of biochemical status of organism; that can be used to monitor and assess gene function.
Exhaustive work has been done on genomics, proteomics and transcriptomics, which allowed establishing global and quantitating mRNA expression profile of cells and tissues in species for which the sequence of all genes is known. Now question which arises is why Metabolomics when transcriptome, genome and proteome are so popular?
Probable reason for this may be: any change in transcriptome and proteome due to increase in RNA do not always correspondence to alteration in biochemical phenotype and increase mRNA do not always correlated with increased protein level. This can be better understood that Translated protein may or may not be enzymatically active; thus it can be said that transcriptome and proteome do not correspondence to alteration in biochemical phenotype. Identification of mRNA and protein is indirect and yield only limited information.
Another reason might be: If quantification of metabolite is known then long process like to know DNA and protein sequence, micro array, 2-D-Gel, Electrophoresis need not to be done. Thus, it is inferred that metabolome provide the most functional information of Omics technology.
Unlike transcripts and proteome, metabolite shares no direct link with genetic code and is instead products of concerted action of many networks of enzymatic reactions in cell and tissue. As such, metabolites do not readily tend themselves to universal methods for analysis and characterization.
Metabolome data has twin advantage in systematic analysis of gene function; that metabolites are functional cellular entities that vary with physiological content and also the number of metabolites is far fewer than the number of genes or gene product. For this reason, Metabolomics requires the exploitation of knowledge of experimentally characterized gene in elucidation of function of unstudied gene. This may be achieved by comparing the change in cells metabolite profile that is produced by deleting a gene of unknown function with a library of such profiles generated by individually deleting genes of unknown function. Strategies for identifying the function of unknown genes on the basis of metabolomic data have been proposed. Silent phenotypes can be revealed by significant changes in concentration of intercellular metabolites. FANCY approach is capable of revealing the function of gene that does not participate directly in metabolism or its control. An advantage of FANCY approach is that it assigns cellular rather than molecular function.
Metabolite phenotypes are used as the basis of discriminating between plants of different genotypes or treated plants. Metabolic composition of a cell or tissue influences the phenotype and it is the most appropriate choice for functional genomics and to use the fluxes between metabolites as the basis for defining a metabolic phenotype is a matter for debate but there is increasing evidence, for example from investigations of transgenic plants that metabolomic analysis is a useful phenotyping tool. Moreover, the value of a metabolic phenotype, however
defined, is greatly increased by the possibility of correlating the data with the system-wide analysis of gene expression and protein content.
The major challenge faced by metabolomics is unable to comprehensively profile of all metabolites. Plants have enormous biochemical diversity, which is estimated to exceed 200,000 different metabolites and therefore large-scale comprehensive metabolite profiling meets its greater challenge. Metabolites are not linear polymers composed of a defined set of monomeric units but rather constitute a structurally diverse collection of molecule with widely varied chemical and physical properties.
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The chemical nature of metabolites ranges from ionic, inorganic species to hydrophilic carbohydrate, hydrophilic lipids and complex natural products. The chemical diversity and complexity of metabolome makes it extremely challenge to profile all of metabolome simultaneously. To find changes in metabolic network that are functionally correlated with the physiological and developmental phenotype of the cell, tissue or organism is the bottleneck of metabolomics.
If one general extraction and analytical system is used it is likely that many metabolites will remain in plant matrix and will not be profiled. Analytical variance (the coefficient of variance or relative standard deviation that is directly related to experimental approach), Biological variance (arises from quantitative variation in metabolite levels between plants of same species grown under identical or as near as possible identical conditions), Dynamic range (concentration boundaries of an analytical determination over which instrumental response as a function of analyte concentration is linear) represent the major limitations of resolution of Metabolomics approach.
Metabolome analysis can be roughly grouped in to four categories, which require different methodologies for validation of results. For the study of primary effects of any alteration, analysis can be restricted to a particular metabolite or enzyme that would be directly affected by abiotic or biotic perturbation. This technique is called metabolite target analysis and is mainly used for screening purpose. Sophisticated methods for the extractions, sample preparation, sample clean ups, and internal references may be used, making it much more precise than other methods . Metabolic fingerprinting classifies samples according to their biological relevance and origin and used for functional genomics, plant breeding and various diagnostic purposes. In order to study the number of compounds belonging to a selected biochemical pathway, metabolite profiling is employed. The term metabolite profiling was coined by Horning and Horning in 1970, defined as 'quantitative and qualitative analysis of complex mixtures of physiological origin'. It has been employed for the analysis of lipids , isoprenoids , saponins , carotenoids , steroids and acids . Only crude sample fractionation and clean-up steps are carried out . Next step in metabolome analysis is to determine metabolic snapshots in a broad and comprehensive way, widely known as metabolomics. In this, both sample preparation and data acquisition aimed at including all class of compounds, with high recovery and experimental robustness and reproducibility.
Metabolomics has been developing as an important functional genomic tool. For continued maturation of it, following objectives need to be achieved:
- Improved comprehensive coverage of plant metabolome.
- Facilitation of comparison of results between laboratory and experiments
- Enhancement of integration of metabolomics data with other functional genomic strategies.
Application of Metabolomics
Since the metabolome is closely tied to the genotype of an organism, its physiology and its environment (what the organism eats or breathes), metabolomics offers a unique opportunity to look at genotype-phenotype as well as genotype-envirotype relationships. Metabolomics is increasingly being used in a variety of health applications including pharmacology, pre-clinical drug trials, toxicology, transplant monitoring, newborn screening and clinical chemistry. However, a key limitation to metabolomics is the fact that the human metabolome is not at all well characterized.
The Human Metabolome Project (HMP)
Unlike the situation in genomics, where the human genome is now fully sequenced and freely accessible, metabolomics is not nearly as developed. There are approximately 2900 endogenous or common metabolites that are detectable in the human body. Not all of these metabolites can be found in any given tissue or bio-fluid. This is because different tissues/bio-fluids serve different functions or have different metabolic roles. To date, the HMP has identified and quantified (i.e. determined the normal concentration ranges for) 309 metabolites in CSF, 1122 metabolites in serum, 458 metabolites in urine and approximately 300 metabolites in other tissues and bio-fluids. Clearly more concentration data would be desirable and this is one of the long term goals of the HMP and other affiliated metabolomic projects around the world.
The Human Metabolome Project is a $7.5 million Genome Canada funded project launched in January 2005. The purpose of the project is to facilitate metabolomics research through several objectives to improve disease identification, prognosis and monitoring; provide insight into drug metabolism and toxicology; provide a linkage between the human metabolome and the human genome; and to develop software tools for metabolomics.Â
The project mandate is to identify, quantify, catalogue and store all metabolites that can potentially be found in human tissues and bio-fluids at concentrations greater than one micromolar. This data will be freely accessible in an electronic format to all researchers through the Human Metabolome Database. In addition, all compounds will be publicly available through Human Metabolome Library.Â
Already more than 800 compounds have been identified and by end of this year it is expected that more than 1400 metabolites will have been identified, quantified and archived into web-accessible databases and stored in -80Â°C freezers. However, the Human Metabolome Project is only mandated to provide chemical data and chemical compounds to the scientific community. It does not have the funding or the resources to use these "raw materials" for disease identification and characterization. Indeed the intent of the Human Metabolome Project is to be an enabler of future metabolomic research, just as the Human Genome Project has been an enabler of current genomic research.
Exploring disease through metabolomics.
Metabolomics approaches provide an analysis of changing metabolite levels in biological samples. In the past decade, technical advances have spurred the application of metabolomics in a variety of diverse research areas spanning basic, biomedical, and clinical sciences. In particular, improvements in instrumentation, data analysis software, and the development of metabolite databases have accelerated the measurement and identification of metabolites. Metabolomics approaches have been applied to a number of important problems, which include the discovery of biomarkers as well as mechanistic studies aimed at discovering metabolites or metabolic pathways that regulate cellular and physiological processes. By providing access to a portion of biomolecular space not covered by other profiling approaches (e.g., proteomics and genomics), metabolomics offers unique insights into small molecule regulation and signaling in biology. In the following review, we look at the integration of metabolomics approaches in different areas of basic and biomedical research, and try to point out the areas in which these approaches have enriched our understanding of cellular and physiological biology, especially within the context of pathways linked to disease.
Applications of metabolomics in drug discovery and development
Metabolomics is a relatively new field of 'omics' technology that is primarily concerned with the global or system-wide characterization of small molecule metabolites using technologies such as nuclear magnetic resonance, liquid chromatography and/or mass spectrometry. Its unique focus on small molecules and the physiological effects of small molecules aligns the field of metabolomics very closely with the aims and interests of many researchers in the pharmaceutical industry. Because of its conceptual and technical overlap with many aspects of pharmaceutical research, metabolomics is now finding applications that span almost the full length of the drug discovery and development pipeline, from lead compound discovery to post-approval drug surveillance. This review explores some of the most interesting or significant applications of metabolomics as they relate to pharmaceutical research and development. Specific examples are given that show how metabolomics can be used to facilitate lead compound discovery, to improve biomarker identification (for monitoring disease status and drug efficacy) and to monitor drug metabolism and toxicity. Other applications are also discussed, including the use of metabolomics to facilitate clinical trial testing and to improve post-approval drug monitoring. These examples show that metabolomics potentially offer drug researchers and drug regulators an effective, inexpensive route to addressing many of the riskier or more expensive issues associated with the discovery, development and monitoring of drug products.
Metabolomics and Global Systems Biology
''Systems biology'' is a term that has a relatively recent origin and currently means many different things to different investigators. The ideas encompassing the term systems biology have arisen as a result of the development of the ''omics'' technologies such as genomics, proteomics or metabonomics/ metabolomics.
In these fields of study large amounts of quantitative (or semi-quantitative) data are being derived, at a variety of levels of bio-molecular organization, from genes through proteins down to metabolites. One of the expectations of systems biologists is that, in some way, such data can be integrated to give a holistic picture of the state of the ''system'' that provides insights that are not available by other, more directed, methods, ultimately enabling a more fundamental understanding of biology to be obtained via networks of interactions at the molecular level.
This may, or may not, be a realistic ambition but, successful or not, such work may greatly aid in efforts to deliver the ''Personalized Healthcare Solutions'' so desired by the practitioners of 21st century medicine. Such therapeutic approaches, tailored to the exact biology (or biological state) of an individual, clearly require methods of patient evaluation that enable the clinician to select
the most appropriate combinations of drugs, dosages and treatment regimens before commencing therapy. In an ideal world this process would maximize therapeutic benefit and minimize adverse drug events. Attempts at this type of sub-classification of individuals (patient stratification) are beginning to be performed and are currently most often attempted using some particular genetic feature.
Moving away from disease, such concepts could easily be extended to more general lifestyle paradigms aimed at minimizing the propensity of an individual, found to have gene-level risk factors, to acquire a disease later in life by optimizing lifestyle (nutrition and exercise, etc.). Given the current cost of providing such detailed information on an individual it is difficult to believe that personalized medicine will be delivered via a systems biology approach in the near future (at least to large populations). However, this does not mean that systems approaches may not be valuable in identifying better diagnostics and, paradoxically, many of the insights that will illuminate ''personalized medicine'' may well come from omics-based epidemiological studies of populations. If it is taken as a given that the state of any biological system, be it cell, organ or whole organism, is a function of a combination of factors such as genotype, physiological state (e.g. age), disease state, nutritional state, environment (both current and historical), etc. The complexity faced by such investigations is clearly enormous. It is arguable that metabonomics, because it measures the outputs of the system rather than potential outcomes, offers the most practical approach to measuring global system activity via accessing the metabolic profiles that are determined by these combinations of genetic and environmental factors. This set of assumptions provides the basis for the discussion of the use of global metabolic profiling in systems approaches. [1, 2]
Metabolomics in pharmaceutical research and development: metabolites, mechanisms and pathways.
In recent years, quantitative metabolomics has played increasingly important roles in pharmaceutical research and development. Metabolic profiling of biofluids and tissues can provide a panoramic view of abundance changes in endogenous metabolites to complement transcriptomics and proteomics in monitoring cellular responses to perturbations such as diseases and drug treatments. Precise identification and accurate quantification of metabolites facilitate downstream pathway and network analysis using software tools for the discovery of clinically accessible and minimally invasive biomarkers of drug efficacy and toxicity. Metabolite abundance profiles are also indicative of biochemical phenotypes, which can be used to identify novel quantitative trait loci in genome-wide association studies. This review summarizes recent experimental and computational efforts to improve the metabolomics technology as well as progress towards in-depth integration of metabolomics with other disparate 'omics datasets to build mechanistic models in the form of detailed and testable hypotheses.
It is well known that significant metabolic change take place as cells are transformed from normal to malignant. A significant role in cancer initiation and progression is attributed to changes in RNA and protein expression levels and regulation. However, changes in small molecules also provide important mechanistic insights into cancer development.
There is a strong body of evidence supporting the important role of metabolic regulation in cancer. Malignant cells undergo significant changes in metabolism including a redistribution of metabolic networks. These metabolic changes result in different metabolic landscapes in cancer cells versus normal cells.
Metabolomics, as a global approach, is especially useful in identifying overall metabolic changes associated with a particular biological process and finding the most affected metabolic networks. Moreover, metabolomics provides an additional layer of information that can be linked with transcriptomics and proteomics data to obtain a comprehensive view of a biological system. Metabolomics is a relatively new field in genomics research but it is gaining broader recognition in the cancer community.
Most cancer metabolomics studies to date have been done using metabolic fingerprinting or profiling with NMR spectroscopy of tissue extracts or in vivo magnetic resonance spectroscopy. Using NMR spectroscopy techniques it is possible to differentiate several tumor types in humans and in animal models. But while techniques based on magnetic resonance have the advantage of being non-invasive, they have low sensitivity and cannot detect molecules at low concentrations. Mass spectrometry methods provide advantage of higher sensitivity and are more appropriate for in vitro studies similar to transcriptomics and proteomics, metabolomics generates large amounts of data. Metabolomics experiments generate a large volume of specialized data that are complex and multi-dimensional. Storing, organizing and retrieving the data and associated metadata requires properly designed databases. The analysis of these data sets is equally challenging and new analysis algorithms are still being developed.
Multivariate statistical analysis of the metabolomics data in many cases utilizes the same approaches as the analysis of other genomic data. However, metabolomics has unique bioinformatics needs in addition to others common in microarray or proteomics data due to the fact that it is generated by multiple analytical platforms and requires extensive data pre-processing. Major areas where developments in data analysis techniques are crucial for further progress of metabolomics include: data and information management, raw analytical data processing, metabolomics standards and ontology, statistical analysis and data mining, data integration, and mathematical modeling of metabolic networks within the framework of systems
Plants are of pivotal importance to sustain life on Earth because they supply oxygen, food, energy, medicines, industrial materials and many valuable metabolites. Plant metabolomics is a huge analytical challenge as despite typical plant genomes containing 20,000-50,000 genes there are currently estimated 50,000 identified metabolites with this number set to rise to 200,000 . These plants metabolites are synthesized and accumulated by the networks of proteins encoded in the genome of each plant. Due to its possibility off making economical worthwhile discoveries, plants have been the subject of many metabolomics research programs. It has been applied in plant biology by analysis of differences between plant species, genotypes or ecotypes . It helps us to gain insight in the cellular regulation of plant biosynthetic network and to link changes in metabolite levels to differences in gene expression and protein production.
One of the first applications of the approach was to genotype Arabidopsis thaliana leaf extracts. However, even after the completion of the genome sequencing of Arabidopsis  and rice  function of these genes and networks of gene-to-metabolite are largely unknown. To reveal the function of genes involved in metabolic processes and gene-to-metabolite analysis is shown to be an innovative way for targeted metabolite analysis is shown to be an innovative way for identification of gene function for specific product accumulation in plants , . Metabolomics can provide research a new tool to identify the functions of unknown genes in Arabidopsis and other plants. Understanding plant metabolism could lead to the engineering of the higher quality food or material producing plants.