Reportlinker.com announces that a new market research report is available in its catalogue.
Reportlinker Adds Bioinformatics and Computational Biology: Bottlenecks and Options
The interdisciplinary fields of Bioinformatics and Computational Biology are locked in a high stakes race with analytical instrument developers and innovators. The pace and scope of change in many fields of biomedical research rivals what we once associated only with semiconductor devices. This report explores the interlocking challenges facing instrumentation advances, computational demands and our evolving systems biology knowledge. Key challenges presented in this report include:
- Instrumentation capable of generating terabytes of raw data daily
- Storage requirements for human gene sequences
- Need for cross platform data analysis standards
- Appropriateness of analysis & modeling applications
- Database data quality and annotation protocols
Bioinformatics and Computational Biology: Bottlenecks and Options reviews the state of the art and aims to determine the significant technological and market trends in the application of informatics and computation techniques to biological research and drug discovery. The progress of molecular biology has given us a profound understanding of human physiology and pathology at a molecular level. However, we understand that a functioning organism is more than simply a sum of chemical reactions. In recent years a concerted effort has been directed at moving from a reductionist approach to understanding physiology in an integrative systems framework complete with the associated mathematical-based models.
The growth of systems biology has been aided by the availability of constantly evolving computational capacity of cheap hardware as well as advances in analytical research instruments capable in some applications of generating terabytes of data each day. Such instruments are being used to make time series measurements of multiple-analyte fluxes during the perturbation of a physiological system. The robustness of such data are the building blocks for computational biology.