A CASE tool for the modeling of methods and information systems

MODSI is a multi-models tool for information systems modeling. A modeling process in MODSI can be driven according to three different approaches: informal, semi-formal and formal. The MODSI tool is therefore based on the linked usage of these three modeling approaches. It can be employed at two different levels: the meta-modeling of a method and the modeling of an information system.In this paper we start presenting different types of modeling by making an analysis of their particular features. Then, we introduce the meta-model defined in our tool, as well as the tool functional architecture. Finally, we describe and illustrate the various usage levels of this tool.

Probing the Specificity of Binding to the Major Nuclear Localization Sequence-binding Site of Importin-alpha Using Oriented Peptide Library Screening

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The generation and utilization of a cancer-oriented representation of the human transcriptome by using expressed sequence tags

Whereas genome sequencing defines the genetic potential of an organism, transcript sequencing defines the utilization of this potential and links the genome with most areas of biology. To exploit the information within the human genome in the fight against cancer, we have deposited some two million expressed sequence tags (ESTs) from human tumors and their corresponding normal tissues in the public databases. The data currently define approximate to23,500 genes, of which only approximate to1,250 are still represented only by ESTs. Examination of the EST coverage of known cancer-related (CR) genes reveals that <1% do not have corresponding ESTs, indicating that the representation of genes associated with commonly studied tumors is high. The careful recording of the origin of all ESTs we have produced has enabled detailed definition of where the genes they represent are expressed in the human body. More than 100,000 ESTs are available for seven tissues, indicating a surprising variability of gene usage that has led to the discovery of a significant number of genes with restricted expression, and that may thus be therapeutically useful. The ESTs also reveal novel nonsynonymous germline variants (although the one-pass nature of the data necessitates careful validation) and many alternatively spliced transcripts. Although widely exploited by the scientific community, vindicating our totally open source policy, the EST data generated still provide extensive information that remains to be systematically explored, and that may further facilitate progress toward both the understanding and treatment of human cancers.