This research addresses the use of computational techniques for the modelling, simulation and prediction of complex biological systems.
In this context the bioinformatics research of Gianluca Bontempi’s team focuses on the use of machine learning techniques for the classification of microarray data in breast cancer and diabetes and the inference of genomic networks. In particular the team obtained signiﬁcant results in the context of the following projects:
- prognostication of breast cancer patients using gene expression proﬁling (in collaboration with the group of Dr. C. Sotiriou in Bordet Hospital and the group of Pr. J. Quackenbush in Harvard Dana Farber).
The research presented an original methodology dealing speciﬁcally with the analysis of microarray and survival data in order to build prognostic models and provide an honest estimation of their performance. The approach used for signature extraction consists of a set of original methods for feature transformation, feature selection and prediction model building. A novel statistical framework was presented for performance assessment and comparison of risk prediction models. Such interdisciplinary contributions have brought new insights in biological processes critical to a patient’s clinical outcome and have been published both in top bioinformatics journals such as Bioinformatics, Genome Biol, PNAS, and BMC Genomics, and clinical journals such as Nature Medicine, Lancet Oncology, J Natl Cancer Inst , J Clin Oncology, Clin Cancer Res and Breast Cancer Research.
2. Inference of complex networks for expression data (in collaboration with Bordet Hospital, Erasme and CSAIL MIT) :
We developed computationally efﬁcient and theoretically founded techniques for inferring large networks of dependencies from expression measures. We proposed a set of information theoretic approaches which rely on the estimation of mutual information and conditional mutual information from data in order to measure the statistical dependence between genes expression. These techniques have been published in bioinformatics journals such as BMC Bioinformatics, EURASIP and were adopted in the context of the Drosophila Model Organism Encyclopedia Of DNA Elements project (modENCODE) consortium then leading to a Science publication where P.E. Meyer appears as co-ﬁrst author. Also, packages for feature selection and network inference have been made available in the R/Bioconductor framework
- Identifying and analyzing the information processing capacity of proteins :
This approach quantifies the allosteric/cooperative effects induced by peptide-binding, which are essential for protein function
and are gradually considered to be ageneral property for every protein inside the cell. In simple terms the developed method is able to identify how information flows through the structures of the proteins. The insights obtained from this work have both theoretical and medical impact. Currently, the computational predictions are being validated using NMR relaxation experiments, biophysical analysis and in vivo experimentation.
 T. Lenaerts, J. Ferkinghoff-Borg, J. Schymkowitz, and F. Rousseau. Information theoretical quantification of cooperativity in signalling complexes. BMC Syst Biol, 3:9, 2009.
 T. Lenaerts, J. Ferkinghoff-Borg, F. Stricher, L. Serrano, J. Schymkowitz, and F. Rousseau. Quantifying information transfer by protein domains: Analysis of the Fyn SH2 domain structure. BMC Structural Biology, 8:43, 2008.
 T. Lenaerts, J. Schymkowitz, and F. Rousseau. Protein domains as information processing units. Curr Protein Pept Sci, 10(2):133–145, 2009.
2. Understanding the evolutionary dynamics of chronic myeloid leukaemia (CML):
As far as the second CB topic is concerned, the research focusses on the analyses of diseases like CML through the use of a mathematical model of the hematopoietic system
. Together with an international team, we examine the response dynamics of CML to treatment with tyrosine kinase inhibitors (TKI)
like Imatinib and Nilotinib, providing in this way insight into clinical data [4-6]. One recent important contribution showed that stochastic effects at the level of the stem cell pool and early progenitors leads to the loss of the original cancer cell that drives the disease . This result has raised the interest of clinicians since implies that TKI might be capable of curing a patient, which is up to now still considered to be impossible.
 T. Lenaerts, J.M. Pacheco, A. Traulsen, and D. Dingli. Tyrosine kinase inhibitor therapy can cure chronic myeloid leukemia without hitting leukemic stem cells. Haematologica, 95(6):900-907. 2009.
 T. Lenaerts, F. Castagnetti, A. Traulsen, J.M. Pacheco andG. Rosti, and D. Dingli. Explaining the in vivo and in vitro differences in leukemia therapy. Cell Cycle, 10:1540-1544 , 2011.
 D. Dingli, A. Traulsen, T. Lenaerts, and J.M. Pacheco. Evolutionary dynamics of chronic myeloid leukemia. Genes & Cancer, 1(4):309-315. 2010.