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The near-complete sequencing of various organisms' genomes has led to the development of new experimental and instrumental techniques, enhancing our understanding of complex biological pathways and networks. Despite this progress, many metabolic pathways and enzymes remain unknown, necessitating the advancement of experimental and mathematical methods to reconstruct these networks, identify missing genes, and characterize known enzymes' kinetics. The post-genomic era emphasizes systems biology, integrating resources from diverse fields such as mathematics, computational biology, bioinformatics, functional genomics, and structural biology. The rapid growth in computational speed and data storage has created opportunities for accumulating extensive sequence, expression, and functional data, as well as for analyzing larger biological systems. However, the success of this discipline hinges on improving data quality, as systems-level investigations demand much higher standards than in the past. Developing truly integrated databases that handle heterogeneous data is crucial for retrieving gene properties, enzyme kinetics, and analyzing complex biological processes. This research perspective can lead to insights into cellular pathways related to disease biology and targeted molecular therapeutics, aiding early diagnosis and personalized therapy predictions. Nonetheless, current analyses are constrained by the lack of systematic colle
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Proceedings of the 3rd International Beilstein Workshop on Experimental Standard Conditions of Enzyme Characterizations, Martin G. Hicks
- Idioma
- Publicado en
- 2008
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