MeMo describes the framework for metabolomics experiments in terms of the following seven components:

• Admin. Administrative data about an experimental framework (e.g. protocols, personnel, instruments).
• Biological sample. Description of a biological source (e.g. genotype, phenotype, biological sample).
• Chemical sample. Description of a sample prepared for instrumental analysis.
• Instrumental analysis. The results (e.g. mass spectrum) of analyzing a sample using an analytical instrument.
• Data processing. Identification of metabolites based on the results of instrumental analysis.
• Data analysis. Computational analysis of the analytical data and the associated metadata.
• Background knowledge. The background knowledge explicitly stored in the DB in a machine-readable form (e.g. gene classification, compound properties such as molecular weight, structure, reactions, etc.).

The experimental framework for metabolomics (described by the package called Wet experiments) can be divided into three components: biological, chemical and analytical. Biological sample is extracted from a biological source studied. It is used to produce a chemical sample to be used in instrumental analysis.

To process the vast amounts of metabolomics data additional analyses need to be performed in silico to extract knowledge. Therefore, a separate package is used to model Dry experiments, which covers the processing of raw analytical data (DataProcessing) and their computational analysis (DataAnalysis). Finally, the background knowledge package is used to cover all data coming from external sources covering different aspects of biological/chemical knowledge, but also the information inferred from the experimental data using different types of data analysis methods.

Use the menu on the left to obtain more information on specific packages and their classes.