 | Knowledge Representation for Systems Biology
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| | | 1) Department of Computer Engineering & Computer Science University of Missouri-Columbia |
| | The system-level understanding of various biological behaviors and phenomena requires several components, such as: gene sequences, protein structures, gene functions and metabolic pathways. A challenging problem is representing, learning and reasoning about these biochemical reactions, gene and protein structures, relationships between genotypes and phenotypes, and their interplay. Building such knowledge bases often integrates various different kinds of knowledge into a single hierarchical framework. On one hand, the knowledge of metabolic pathways consists of kinetic computation, graphical representation, and database.On the other hand, the functionality of genomes includes QTL mappings and higher-level data mining. This paper describes a hierarchical model of cognitive maps for representing gene and metabolic knowledge as well as genotype to phenotype mappings. Cognitive maps are bi-directional graphs that can learn and reason quantitatively and qualitatively. An example for maize hybrids resistant to maize earworm in an agri-ecosystem of the biosphere illustrates a hierarchical cognitive map of biological mappings and biochemical reactions. |
| | Key Words: | Systematic Biology, Gene Function, Metabolic Pathway, Knowledge Representation, Cognitive Map, Hierarchical Cognitive Map |
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