Biosciences are experiencing a profound change driven by the introduction of new analytical methods. The epistemological culture in biology and medicine is not sufficient to face this challenge. The theme of the GENSIPS 2007 meeting, genomic signal processing and statistics for functional genomics and systems biology, is an indirect acknowledgement of this fact, since the meeting primarily addresses non-biologists. A new area of science is emerging and it needs a serious and sustained input from other scientific disciplines.

Today, many of the emerging methods provide high throughput data primarily for exploratory research. The measurement data is scattered into multiple separate data repositories managed by public data providers, universities, and private companies. The consequences of this data isolation process can be detrimental to biological and medical research.

It is generally accepted that a component-centric approach to biology, albeit necessary, is not sufficient. When it comes to understanding complex biological systems such as organisms and their responses to stimuli, more integrative research approaches are necessary. However, analyses relying on the simultaneous use of large measurement sets from different data domains have been badly hampered because of data dissemination problems. Moreover, development of data mining tools and other analytical methodologies should be done where the large data masses are available to sincerely address the complex challenges of systems biology research projects.

The integration of relevant biological data, the use of an adequate data schema with wide coverage and application of a systems theoretical formalism have all three been recognised as bottlenecks in many large research projects. Eventually, also the lack of methodology sharing must be addressed, so that resources can be harnessed for problem solving instead of redundant method development.

MEDICEL has developed a new type of SW research infrastructure to provide universities, drug companies and other research organisations a more efficient environment for the integration and further processing of biological data.