Proteomics is a rather recent research area coming from a much older origin: protein analysis. This direction deals with the elucidation of molecular properties of individual proteins like amino acid sequence, 3D structure and biological activity. In contrast with that, the objective of Proteomics consists in the investigation of the entirety of all proteins in a biological sample at a given point in time and under the corresponding actually defined conditions. The term “proteome” has been coined for that target about 10 years ago.

Most frequently, proteomes of homogeneous cell populations (from cell lines) and of biological tissues, incl. human ones are analysed in order to obtain answers for questions about regulation of physiological pathways and, of course, for emergence, therapy and prevention of diseases.

The achievements of genomics up to now, gene mapping and sequencing and further steps in genome analysis are far from being sufficient for understanding the molecular basis of physiological and pathological life processes. The impact of the genes resides to the major part in the proteins encoded by them. The essential facts for understanding normal and not normal biological processes are therefore hidden in the composition of proteomes.

Genomes are static building instructions; proteomes on the other hand are highly dynamic depending on the actual biological situation and environmental influences. Caterpillar and butterfly own the same proteome. What makes them looking so different is first of all the composition of their proteomes!

More specifically, proteomics stands on the experimental level at the first hand for qualitative and quantitative determination of all proteins in cells and tissues as a snap shot under exactly defined conditions. The tremendous importance of proteomics as an analytical tool is achieved by its potential to identify within a mixture of 10 000 and more proteins of a typical cellular proteome individual proteins which may be associated with changes in cell phenotype (occurring e. g. with growth, adaptation processes or disease). This enables in an outstanding way subsequent studies in order to reveal if it is possible to detect a causative reason based on the observed correlation.