Modelling of complex systems

The goal that we proposed in this context is the identification of efficient algorithms to solve biological problems that require the processing of large amounts of data. These algorithms can be used for the characterization of proteins, for applications in the sequencing of the DNA of new bodies, in the study and derivation of haplotypes from genotypes, in recognition of signals functional genomic sequences in the analysis of gene expression data ,in the automatic classification of families of proteins.

Furthermore, thanks to the mathematical and statistical component of this project, it'll be possible the development of new methodologies for the analysis of complex biological systems and to tackle problems related to bio-inspired and high parallelism calculations. The information component will develop applications for the study of codes with the aim of shaping the transmission of DNA's information.

In these sectors the principal used techniques are: discrete programming problems in the derivation of haplotypes, logic programming, generations of rules, BSS/ICA, clustering for gene expression data classification and analysis problems as a result of DNA microarray's and haplotypes and genotypes data experiments, pattern discovery for the discovery of DNA polymorphisms on and the inference of haplotypes, blind statistical techniques for electrophoresis time series models; assembly of DNA fragments in the shotgun sequencing, survey on family models useful for the formal description of complex biological systems. On these issues is important the integration of knowledge of statistical information technology and applications based on data generated in Life Sciences and Medicine Departments.