ARN (Algorithms for the identification of genetic Regulatory Networks) is a research project coordinated by INESC-ID, which aims at the development of methods that will partially automate the study, identification and modeling of mechanisms found in many living organisms that control gene expression. The task of the XLDB team is to develop information integration techniques for automatically identifying gene regulations from Gene Ontology and BioLiterature.
The central goal of ARN is the development of methods that will partially automate the identification of mechanisms that control gene expression. Cellular processes are regulated by interactions between various types of molecules such as proteins, DNA, RNA and metabolites. Among these, the interactions between proteins and the interactions between transcription factors and their target genes play a prominent role, controlling the activity of proteins and the expression levels of genes. A significant number of such interactions has been revealed recently by means of high-throughput technologies. Moreover, recent discoveries have highlighted the regulatory roles of small functional RNA motifs in the control of gene expression. This work aims at obtaining first a better understanding of the biochemistry of molecular recognition and then accurately introducing this understanding into the mathematical models used for the inference procedure. By putting all these interactions together, we will build a network of interactions and thus describe the circuitry responsible for a variety of cellular processes.
Funded by: FCT (PTDC/EIA/67722/2006)
Project Award Amount: 100000.00 €
Start Date: 2007-11-01
Duration: 36 months
Catia Machado, GREAT: Gene Regulation Evaluation Tool Master Thesis, University of Lisbon, Faculty of Sciences, July 2009.
Catia Machado, Hugo Bastos, Francisco Couto, GREAT: Gene Regulation EvAluation Tool.Proceedings of the 3rd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB'09) p. 930-933, 2009. Lecture Notes in Computer Science, Springer Berlin / Heidelberg.
Hugo Bastos, Catia Machado, Ana T. Freitas, Francisco Couto, Retrieving Relevant Documents from BioLiterature for the Curation of Yeast Gene Regulatory Networks.First Portuguese Forum on Computational Biology (as a poster) IGC, 2008.
Francisco Couto, Tiago Grego, Catia Pesquita, Hugo Bastos, R. Torres, P. Sanchez, L. Pascual, C. Blaschke, Identifying Bioentity Recognition Errors of Rule-Based Text-Mining Systems.IEEE Third International Conference on Digital Information Management (ICDIM) 2008.