Research project P6/25 (Research action P6)
During the last decade, biology has faced a technological revolution with the development of high-throughput methods for the characterization of genome sequences, transcriptome, and proteome, giving rise to unprecedented amounts of biological data. However, this technological revolution is only beginning to result in a scientific revolution, and we are still far from understanding the function of any genome, even for the simplest and best-characterized model organisms. Beyond the problems of the relatively low quality of high-throughput data, an important obstacle is the inherent complexity of biological processes, whose emerging properties cannot be understood from the simple sum of individual component properties. The field of dynamical modeling has a long tradition of analyzing complex systems, but has generally been restricted to small networks with parameters characterized at a high resolution. The integration of genome, transcriptome, and proteome data into dynamical models of biological processes represents one of the main challenges of current bioinformatics. The aim of this network is to bring together expertise ranging from high-throughput data analysis and reverse engineering to dynamical modeling with the goal of bridging the gap between the domains and serve as an enabler for systems biology. The approaches and software tools developed by the respective teams will be combined for an integrative analysis of a few carefully selected model systems.
Benefits of the network include (1) sharing of methods and complementary experience between groups traditionally involved in the complementary fields of genome annotation, analysis of omics data, and dynamical modeling; (2) development of a conceptual and practical (software) framework for the integration of genomics, bioinformatics, and modeling approaches; and (3) strengthening of the position and visibility of Belgium in this active research domain. Furthermore, the network will be leveraged to increase the quality of the training of our Ph.D. students and postdoctoral researchers.
The project is organized along 5 workpackages.
WP1. Annotation and comparative genomics
The aim is to analyze the structure and evolution of genomes and use this information to identify regulatory interactions among several genes. The work will be organized along a first track focusing on genome structure and evolution (annotation; organization and comparative genomics; evolution) and a second track aiming at the identification of regulatory interactions, in particular by developing regulatory sequence analysis tools for the identification of cis-regulation elements and regulatory functions of microRNAs.
WP2. High-throughput data analysis
High-throughput genomics and proteomics create huge amounts of data that need to be analyzed to extract biologically meaningful information. Specific methods and algorithms will be developed for the analysis of large amounts of data from genotyping, transcriptomics, proteomics, interactome and metabolome analyses to identify or localize genes, proteins, metabolites involved in specific phenotypes or cellular processes of interest.
WP3. Data integration
High-throughput methods are an appropriate starting point to identify genes or proteins that belong to a pathway, process, or disease of interest – for example, in the analysis of regulatory networks or in medical applications. However, these methods often have a high number of false positives and false negatives. Data integration can mitigate these difficulties by overlapping different data types to eliminate inconsistencies. The research teams will in particular develop gene prioritization methods to integrate multiple data sources to identify the best candidate proteins or genes associated with disease or biological processes of interest. They will also develop methods to simultaneously analyze coupled systems biology data sets, at the level of the transcriptome, proteome, metabolome, or interactome with the goal of identifying candidate components of biological networks.
WP4. Dynamical modeling and simulation of networks
A first goal will be to analyze and compare the use of deterministic vs. stochastic models as well as continuous time vs. discrete time ones for the modeling and simulation of biological networks. Particular focus will be on regulatory networks underlying circadian rhythms. A complementary focus will be the link between the topology of regulatory networks and their dynamical properties, as well as metabolic pathway inference methods. Threshold behavior and oscillatory phenomena in metabolic networks involving phosphorylation-dephosphorylation cascades will be investigated, with a focus on the network of cyclin-dependent kinases controlling the cell cycle.
WP5. Bridging the gap between bioinformatics and modeling
There is a deep mismatch between the analyses of high-throughput data and the quantitative or qualitative modeling of cellular processes. Even with proper data integration, the gap remains large, also because precise modeling is only feasible for well-characterized systems. Little research has been done on methods to bridge this gap. A significant objective of the project will be to progress in this context, and all partners will focus on developing methods towards this goal. Several directions will be envisaged towards this challenging objective, among them integrated refinement procedures, where the current model is tightly matched to available genomic and high-throughput data to detect candidates for extension of the model .
European partners
Two European partners (Denis Thieffry, Université de la Méditerranée – Aix-Marseille II, INSERM ERM 206 –TAGC, France & Florence d’Alché-Buc, Laboratoire de Méthodes Informatiques - Équipe BioInfo – Génopole, France) will be tightly integrated into the IAP network. These two high-profile teams have been selected because their expertise strongly complements that of the network members and because active collaborations with them already exist.
Relationship with existing IAP networks
K.U.Leuven and ULg were part of the IAP network on Systems and Control. This IAP network was a rich feeding ground for the new research topic of bioinformatics.
Link to experimental biological research
The proposed network is not centred on a single specific biological system or process and no wet-bench work is directly involved. However, based on continuous interaction with molecular biologists, it was shown that, across many organisms and varied biological problems, some data analysis challenges frequently reoccur. Making an abstraction of the specificities of the biological problem and translating it into a more generalized formulation leads to a more generic solution. This solution can be fine-tuned to the specific case at hand. It is exactly at this level of abstraction that this network will be active. Instead of spending lots of efforts in building up similar expertises in each research group, members want to share their knowledge at the bioinformatics and algorithmic level to leverage research output. The members are however fully aware of the importance of wet-bench work in providing authentic biological and biotechnological issues, in generating coherent sets of data and in validating computational models of biological systems and as a key to doing modeling work that is truly relevant to biology. Also, the development of data-driven bioinformatics methods for the exploitation of high-throughput genomic or proteomic experimental data obviously needs high quality data sets for investigation and validation. The network members have strong ties to experimentalists that guarantee that the methodologies developed in this network will flow through to wet-bench work or will dispose of the relevant data sets at their input. The outcome of this proposal should be viewed within the larger research strategies of each of the research partners: working towards an approach that is highly integrated with wet-lab research.
All existing funding is situated in this framework and funding for wet-lab support is guaranteed within running projects:
1. VIB-U.Gent is embedded within the Department of Plant Systems Biology and as such involved in numerous projects in fundamental plant research.
2. The K.U.Leuven team coordinates the K.U.Leuven Center of Excellence on Computational Systems Biology, with seven partners, four of which are biological and biomedical research teams (human genetics, endocrinology, diabetes, Salmonella biology). It is also a core partner of the ProMeta (Proteomics and Metabolomics) Facility of the K.U.Leuven.
3. The ULg partner is embedded within the Grappe Interdisciplinaire de Génoproteomique Appliquée (GIGA), which is a Center of Excellence within the ULg bringing together the top research groups in molecular biology and genetics of the university.
4. The ULB partner is member of several research networks funded by the European Union, the Région Wallonne and the Communauté Française de Belgique, involving tight collaborations between experimental and theoretical biologists.
