The analysis of biological systems relies more and more on computational and mathematical methods. The goals of such analysis are multifarious; among the most important ones is the discovery of the biochemical and genetic machinery responsible for pathology development, its control and, possibly, elimination. Such discoveries also rely on an understanding of the spatio-temporal development of biological phenomena, their cause (often “mutations”) and their effects on different scales.

The RetroNet project intends to address this problem and others by:

• Sharing data and knowledge needed for a new integrative research approach in medicine,
• Sharing or jointly develop multiscale models, simulators and analysis tools, with particular attention to the development of Colon Rectal Cancer (CRC) and some of its metastatic effects
• Creating the prototype of a collaborative environment supporting research in this highly interdisciplinary field, by leveraging the experience matured from of previous FP6 experiences .

The RetroNet project concentrates on the development and tuning of algorithms for detecting of emerging behavior from cells ensembles, by searching, analysing and formulating hypotheses of various feedback cycles in biological systems.

The approach will leverage several Control-Theoretic concepts, especially the notions of state-estimation and control-policy learning as implicit drivers of biological behavior selection. The emerging-behavior detection algorithms will consider the content of Pathway and Models Databases and knowledge directly gained from clinicians and biologists running bio-banks or wet-laboratory focussed research.