Computational Systems Biology of Cancer group exists in the Bioinformatics Unit of Institut Curie since the end of 2004. Now it consists of several permanent researchers, postdocs and PhD students. Since year 2007 the team is an "equipe labellisee par La Ligue Nationale Contre le Cancer". The team is also an antenna of the Institut des Systemes Complexes Paris Ile-de-France, whose Institut Curie is a partner.

The long-term global objective of the group could be formulated as following:

Using Systems Biology approach, understand the design principles of the biological networks involved in cancer, contribute to the development of new strategies for human cancer treatment, propose new target molecules and cancer drugs.

By Systems Biology approach we mean using available or deducible information on molecular structures and interactions taking place in a living cell (at different levels like genome, transcriptome, proteome, regulation networks, epigenetics) and integrating heterogeneous sources of data for creating mechanistical or statistical models of human cancer.

The models constructed are then used to make predictions on tumor evolution and on the perturbations to apply to the system to have it adopt the desired behavior. Our approach is therefore strongly connected to the concepts of robustness, complexity and flexibility in biological networks.

The strategic choice of the principal objective dictates also the tactic choices which have been made by the participants of the Systems Biology project at Institut Curie in their research lines, like

• Go from a concrete biological problem (a dataset or an observed phenomenon which has to be explained) to methods for its solution. In other words, not to look for a dataset which works particularly well for a method invented but try to modify the method or find a better one for solving a concrete problem
• Invest sufficient time into studying "biological" details of the problem under study and trying to find a common language with biologists of Institut Curie
• Think about possible clinical applications of any finding, even in a (reasonable) perspective
• Think of the connection of a personal project with one or several hallmarks of cancer (Self-sufficiency in growth signals, Insensitivity to anti-growth signals, Limitless replicating potential, Evading apoptosis, Sustained angiogenesis and Tissue invasion and metastasis); it makes it important to constantly collect information and modeling approaches on (in the order of priority for our group) a) functioning of the human cell cycle; b) apoptosis pathway; c) cell invasion; d) angiogenesis.

The present activities of the Systems Biology group are well aligned with this general goal and present steps towards it.

• External SITCON and internal EWING projects are directly associated with the objective since they are devoted to the study of a concrete cancer model with the goal to give understanding on how the chimeric oncogene EWS/FLI-1 regulates cell proliferation and apoptosis; this understanding requires integration of heterogeneous data collected for this model and promises to give possible targets for intervention into the treatment of Ewing cancer (and, probably, sarcomas in general).
• The RB-pathway project allows to understand in details one of the most important pathways involved in the regulation of cell cycle and frequently showing anomalies in cancer cells; creating a predictive model of this pathway will certainly contribute to the long-term objective
• The BiNoM project is devoted to creation of a software platform to be able to collect and manipulate the pathway data for our own purposes and to present it to biologists; an important point here is the full control on the implementation of the platform which will allow us to adopt any up-to-date method of pathway data analysis and integrate this platform into already existing infrastructure developed in the Bioinformatics Service of Institut Curie. BiNoM is build on top of the Cytoscape open source software.
• Biological network reverse engineering project should help in completing missing parts in our knowledge on biological networks or provide predictions on new molecular interactions that have to be tested experimentally.
• KernelChip project serves as a promising link between the activity of the Systems Biology group and the BioStatistics group in our unit, since it allows to include the information about molecular interactions into a statistical analysis of high-throughput data
• Nfkb pathway modeling collaborative project allows to have experience in modeling cell signaling with use of modern methods for dynamical systems analysis; some problems arising in this project (like context-dependent functioning of the pathway or possibility to encode a regulatory information in response dynamics) are biologically interesting
• In the Model reduction project we develop techniques for reducing complexity of modeling biological networks; it includes methods for hierarchical decomposition of the network into modules and methods for reducing multi-dimensional system dynamics to simpler, low-dimensional dynamics description
• Studying dynamical properties of network motifs is a project attempting to get insights about general principles of biological network organization and their connection to evolution.

The projects of the group are supported by the funding agencies listed below that we acknowledge: