SENEX: a computer-based representation of cellular signal transduction processes in the central nervous system
| Autor: | Perry L. Miller, Sheldon S. Ball, Vei H. Mah |
|---|---|
| Rok vydání: | 1991 |
| Předmět: |
Central Nervous System
Statistics and Probability Aging Computer science Computational biology Models Biological Biochemistry Cofactor Serine User-Computer Interface Inheritance (object-oriented programming) Neurobiology Artificial Intelligence Animals Humans Computer Simulation Threonine Protein kinase A Molecular Biology Gene Mammals chemistry.chemical_classification Object-oriented programming Class (computer programming) biology business.industry Kinase Representation (systemics) Substrate (chemistry) Object (computer science) Computer Science Applications Computational Mathematics Enzyme Computational Theory and Mathematics chemistry biology.protein Artificial intelligence Signal transduction business Tyrosine kinase Software Function (biology) Signal Transduction |
| Zdroj: | Bioinformatics. 7:175-187 |
| ISSN: | 1460-2059 1367-4803 |
| DOI: | 10.1093/bioinformatics/7.2.175 |
| Popis: | The SENEX project is exploring knowledge representation in the neurobiology of ageing through object-oriented programming. SENEX is built from a classification structure of biologic entities and significant relationships among them. For example, an enzyme is an entity and an enzymatic reaction is a relationship among enzyme, cofactor(s), substrate(s) and product(s). There are currently 2600 classes of entities and 50 classes of relationships in SENEX. The class structure serves several functions. One function is to interrelate general and specific categories of molecular and morphologic entities. For example, tyrosine kinase and serine/threonine kinase are specific types of the more general class of protein kinase enzymes. Another function of the class structure is to serve as a network through which inheritance of attributes may occur. For example, the attribute 'subunits' is inherited by all subclasses of the general class multisubunit protein. Information may be accessed through links established in the class structure and through links relating one object as part of another. Relationships form the basis of separate modules within SENEX. This paper describes the types of relationships currently used and planned in the representation of age-related changes in cellular signal transduction processes of mammalian central nervous systems. We also describe tools for specific retrieval of relationships and for tracing links in complex reaction cascades. Application of these tools to identifying possible signal transduction pathways to guide further exploration through experimentation is discussed. |
| Databáze: | OpenAIRE |
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