Our research activities  are centered around a number of projects, loosely grouped around the themes of Computational Biology, Statistical Data Analysis, Bioinformatics, Sequence Analysis,  and Mathematical Modeling of complex biological phenomena.

Systems Computational Biology and Data Analysis

Cancer Research

One of the most interesting problems in Cancer Research concerns the evolution of a tumor from its initial early stages. Issues of heterogeneity and timing all enter the picture and make the reconstruction cancer progression models a complex endeavor that must also take into account the differences between individual tumors and large ensemble data sets. 

We have developed a number of tools and algorithms in the past years that address these issues. First there are tools and algorithms that use Suppes’ Probabilistic Causality Networks to infer a cancer progression model; these algorithms are collected in the TRONCO library. Second there are studies on simulation of cancer evolution in relation to its clonal makeup; the CABERNET tool being the latest tool being published.


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.