Program

Breakthrough technologies shift the frontiers of science. My presentation will give an overview of several emerging technologies in life sciences. A special focus will be placed on the opportunities these technologies offer for the bioinformatics field. Convergence of several scientific disciplines is generating entire new fields where bioinformatics may be a key enabler to facilitate technology diffusion.

The number of sequenced genomes of representatives within the green lineage is rapidly increasing. Consequently, comparative sequence analysis has significantly altered our view on the complexity of genome organization, gene function and regulatory pathways. To explore all this genome information, a centralized infrastructure is required where all data generated by different sequencing initiatives is integrated and combined with advanced methods for data mining. Here, we describe PLAZA, an online platform for plant comparative genomics (http://bioinformatics.psb.ugent.be/plaza/). This resource integrates structural and functional annotation of published plant genomes together with a large set of interactive tools to study gene function and gene and genome evolution. Pre-computed data sets cover homologous gene families, multiple sequence alignments, phylogenetic trees, intra-species whole-genome dotplots and genomic colinearity between species. Through the integration of high confidence Gene Ontology annotations and tree-based orthology between related species, thousands of genes lacking any functional description were functionally annotated. Advanced query systems, as well as multiple interactive visualization tools, are available through a user-friendly and intuitive web interface. In addition, detailed documentation and tutorials introduce the different tools while the workbench provides an efficient means to analyze user-defined gene sets through PLAZA's interface. In conclusion, PLAZA provides a comprehensible and up-to-date research environment to aid researchers in the exploration of genome information within the green plant lineage.

Since almost 10 years, topological analysis of different large-scale biological networks (metabolic reactions, protein interactions, transcriptional regulation) highlighted some recurrent properties: power law distribution of degree, scale-freeness, small-world, which have been proposed to confer functional advantages such as robustness to environmental changes, tolerance to random mutations. Stochastic generative models inspired different scenarios to explain the growth of interaction networks during evolution. The power law and the associated properties appeared so ubiquitous in complex networks that they were qualified of “universal laws”. However, these properties are no longer observed when the data are subjected to statistical tests: in most cases, the data do not fit the expected theoretical models, and the cases of good fitting merely result from sampling artefacts or improper data representation. The field of network biology seems to be founded on a series of myths, i.e. widely believed but false ideas. The weaknesses of these foundations should however not be considered as a failure for the entire domain. Network analysis provides a powerful frame for understanding the function and evolution of biological processes, provided it is brought to an appropriate level of description, by focussing on smaller functional modules and establishing the link between their topological properties and their dynamical behaviour.

Apoptosis is an important physiological process crucially involved in the development and homeostasis of multi-cellular organisms. Although the major signalling pathways leading from the extrinsic induction to the execution of apoptosis have been unravelled, a detailed mechanistic understanding of the complex underlying network and the signal crosstalk remains elusive. A systems biology approach allows to combine diverse data into mathematical models to perform predictive simulations and testing of quantitative and dynamical hypotheses. The modelling process furthermore reveals theoretical and computational challenges.

We combined genetic perturbations, gene expression profiling, genome-wide prediction of motif clusters, and comparative genomics, to identify interactions between transcription factors and their genomic targets and unveil the gene regulatory network underlying retinal differentiation in Drosophila melanogaster.

Array CGH was used to identify recurrent copy number alterations (RCNA) characteristic of either BRCA1-related or sporadic ovarian cancer. After pre-processing, both groups of patients were modeled using a recurrent Hidden Markov Model to detect RCNA. RCNA with a probability higher than 80% were called. After removing RCNA present in both groups, the genes present in the remaining RCNA were investigated for enrichment of pathways from external databases. More RCNA were observed in the BRCA1 group and they display more losses than gains compared to the sporadic group. When focusing on the type of RCNA, no significant difference in length was seen for the gains, but there was a statistically significant difference for the losses. In the sporadic group, a great proportion of the altered regions contain genes known to have a function in cell adhesion and complement activation whereas the BRCA1 samples are characterized by alterations in the HOX genes, metalloproteinases, tumor suppressor genes and the estrogen-signaling pathways. We conclude that BRCA1 ovarian tumors present a different type, number and length of RCNA; a huge amount of the genome is lost, resulting in important genomic instability. Moreover, important biological pathways are altered differentially when compared to the sporadic group.

Leaning on a qualitative (logical) dynamical approach, we are developing computational algorithms and software tools to model, analyse and simulate regulatory networks controlling fundamental cellular processes (proliferation, differentiation and programmed death), in different organisms (yeast, drosophila, mammals).

Learning biological networks from experimental data and other information sources raises several issues in machine learning according the level of abstraction chosen to describe the interactions between molecular components and the number of these components. Unsupervised linear generative models are generally used to provide a rough estimation of the interaction graph from large scale data. When the graph generally at a smaller scale is partially known, supervised learning approaches are used to complete it. Finally, when focusing only a few components (genes), nonlinear dynamical models can be handled. After reporting briefly our last results in the three themes, I would like to emphasize supervised approaches that extract logical rules that “explain” observed regulatory interactions. I describe a series of numerical experiments involving Markov logic networks that combine generative models and logical clauses. The problem we addressed is the following: starting from a gene regulatory network obtained from information retrieval (Ingenuity) on a set of human genes of skin cells, we infer logical rules characterizing the concept of regulation (seen as a logical predicate). Experimental data as well as GO terms, gene positions on chromosomes were encoded into first order clauses. Inductive Logic Programming (Aleph) was used to find a set of potential rules that conclude on the predicate “regulate”. Then from this set of rules, a Markov Logic Network (Alchemy) was trained using penalized likelihood maximization and evaluated very positively according AUC criteria. Moreover, ranking the rules according their importance in the final decision allows to provide a set of possible characterizations for the concept of regulation. First rules now analyzed by the biologist who provided the experimental dataset (Marie-Anne Debily, CEA Evry) seem to suggest new potential regulators.