The combination of high-throughput methods of molecular biology with advanced mathematical and computational techniques has propelled the emergent field of systems biology into a position of prominence. Unthinkable only a decade ago, it has become possible to screen and analyze the expression of entire genomes, simultaneously assess large numbers of proteins and their prevalence, and characterize in detail the metabolic state of a cell population. While very important, the focus on comprehensive networks of biological components is only one side of systems biology.

The adaptation of living organisms to their environment is controlled at the molecular level by large and complex networks of genes, mRNAs, proteins, metabolites, and their mutual interactions. In order to understand the overall behavior of an organism, we must complement molecular biology with the dynamic analysis of cellular interaction networks, by constructing mathematical models derived from experimental data, and using simulation tools to predict the behavior of the system under a variety of conditions.

Genetic Programming (GP) is the automated learning of computer programs. Basically a search process, it is capable of solving complex problems by evolving populations of computer programs, using Darwinian evolution and Mendelian genetics as inspiration. GPLAB is a Genetic Programming toolbox for MATLAB.

Typing methods are major tools for the epidemiological characterization of bacterial pathogens, allowing the determination of the clonal relationships between isolates based on their genotypic or phenotypic characteristics. Recent technological advances have resulted in a shift from classical phenotypic typing methods, such as serotyping, biotyping and antibiotic resistance typing, to molecular methods such as restriction fragment length polymorphisms (RFLP), pulsed-field gel electrophoresis (PFGE), and PCR serotyping .

A ferramenta BiGGEsTS - Biclustering Gene Expression Time-Series, tem como objectivo a integração de algoritmos de biclustering para análise de séries temporais de expressão genética. Estes algoritmos abordam o problema de biclustering em dados provenientes de séries temporais de expressão genética de forma directa, isto é, permitem identificar biclusters formados por um conjunto de genes com expressão coerente num subconjunto contíguo dos instantes temporais em análise.

Most of the protein folding methods use information from known proteins to predict protein structure. For homology and fold recognition methods this information is used directly and good results can be obtained if a sufficient similar protein with known structure is found. However, if no such protein is available or for large unmatched regions, ab initio methods can be of great help (specially for small proteins). Our method uses a fragment library and a search technique to create possible structures from which a high scoring set can then be analysed.

This presentation will show the application of data mining techniques, in particular of machine learning, for discovery of knowledge in a protein database. The main problem we address is the determination whether an amino acid is exposed or buried in a protein for five exposition levels: 2%, 10%, 20%, 25% and 30%. First we introduce the baseline classifier for this problem which, although very simple (only takes into account the amino acid type), already achieves good prediction results.

A associação entre a explosão de dados relacionados com genética molecular e o avanço tecnológico a nível de meios informáticos é, presentemente, um desafio para o estatístico na medida em que é requerido um melhoramento dos métodos existentes e o desenvolvimento de métodos de inferência mais eficientes para lidar com dados de natureza tão complexa.

Chromosomes are not randomly folded in a spaguetti-like state in the mammalian cell nucleus, as initially thought, but occupy distinct territories. Recent studies show that these chromosome territories have preferential arrangements in different cell types, which correlate with the kinds of chromosome rearrangements that occur preferentially in each cell type. Evidence for a growing number of long-range interactions between DNA segments in the same or different chromosomes has raised the possibility of a three-dimensional network of genome interactions.

We consider the problem of inferring the structure of a network from co-occurrence data; observations that indicate which nodes occur in a signaling pathway but do not directly reveal node order within the pathway. This problem is motivated by network inference problems arising in computational biology and communication systems, in which it is difficult or impossible to obtain precise time ordering information. Without order information, every permutation of the activated nodes leads to a different feasible solution, resulting in combinatorial explosion of the feasible set.