Ellis, L.B., Hershberger, C.D., and Wackett, L.P.. "The University of Minnesota Biocatalysis/Biodegradation Database: specialized metabolism for functional genomics." Nucleic Acids Res. 27
(1).
1999.
pp. 373-6.
[ .pdf ] [ PubMed ]
The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://www.labmed.umn.edu/umbbd/i nde x.html) first became available on the web in 1995 to provide information on microbial biocatalytic reactions of, and biodegradation pathways for, organic chemical compounds, especially those produced by man. Its goal is to become a representative database of biodegradation, spanning the diversity of known microbial metabolic routes, organic functional groups, and environmental conditions under which biodegradation occurs. The database can be used to enhance understanding of basic biochemistry, biocatalysis leading to speciality chemical manufacture, and biodegradation of environmental pollutants. It is also a resource for functional genomics, since it contains information on enzymes and genes involved in specialized metabolism not found in intermediary metabolism databases, and thus can assist in assigning functions to genes homologous to such less common genes. With information on >400 reactions and compounds, it is poised to become a resource for prediction of microbial biodegradation pathways for compounds it does not contain, a process complementary to predicting the functions of new classes of microbial genes.
Keywords: Bacteria_genetics ; Bacteria_*metabolism ; Bacterial Proteins_metabolism ; *Biodegradation ; Biotechnology ; *Catalysis ; *Databases Factual_trends ; Environmental Pollution ; Enzymes_chemistry ; Enzymes_genetics ; Enzymes_metabolism ; Genes ; Bacterial_genetics ; Genes ; Bacterial_physiology ; Human ; Information Storage and Retrieval ; Internet ; Minnesota ; Universities
Hofestadt, R. and Thelen, S.. "Quantitative modeling of biochemical networks." In Silico Biol. 1
(1).
1998.
pp. 39-53.
[ PubMed ] [ WebSite ]
Today different database systems for molecular structures (genes and proteins) and metabolic pathways are available. All these systems are characterized by the static data representation. For progress in biotechnology the dynamic representation of this data is important. The metabolism can be characterized as a complex biochemical network. Different models for the quantitative simulation of biochemical networks are discussed, but no useful formalization is available. This paper shows that the theory of Petrinets is useful for the quantitative modeling of biochemical networks.
Keywords: *Biochemistry ; Biotechnology ; Catalysis ; Computational Biology ; *Computer Simulation ; Databases Factual ; Glycolysis ; Models Biological ; Protein Engineering