MeMo (Metabolomic Modelling) project page
The enormous flood of omics data brings the need for well-designed and -curated databases, which can store, handle and disseminate large amounts of data efficiently and readily lend themselves to data mining techniques used to extract hidden patterns from the data. The extracted facts can be further explored in simulation-based analyses providing predictions to be tested in vivo/vitro. Such a framework consisting of modelling, machine learning and simulation can help achieve the systems biology objective (to understand the way in which the heterogeneous parts of biological systems combine to form the whole).
The first step is to develop and standardise the omics models and populate them with curated data. The proteomics field has significantly advanced in such efforts: HUPO has driven the Protein Standards Initiative in order to develop an exchange standard for proteomics data (MIAPE) based on the PEDRo schema. Similarly, MAGE-ML represents an emerging standard for transcriptomics. Some attempts have been made in metabolomics, e.g. in ArMet, a data model for plant metabolomics.
We developed MeMo as a similar approach for yeast metabolomics with an emphasis on metabolomic footprinting as a strategy for functional genomics.