On the one hand, recent and novel technologies produce biological data sets of ever-increasing resolution that reveal not only genomic sequences but also RNA and protein abundances, their interactions with one another, their subcellular localization, and the identity and abundance of other biological molecules. This requires the development and application of sophisticated computational methods, encompassed by the field of bioinformatics. On the other hand, biomedical research has risen to the challenge of understanding the integrated functions of thousands of genes.
Physical and functional interaction networks chart connectivities, reveal functional modules, and provide clues on the functioning of specific genes. Using mathematical models of the stochastic and dynamical events of biology reveals fundamental design principles and allows for virtual experimentation. This is a focus of the field of systems biology. In addition, rapidly increasing capabilities of rapid molecular and genomic analyses in the clinic promise to transform medical practice in unprecedented ways. The ability to cross-query data and knowledge bases provides opportunities and challenges to computational sciences interfacing with medicine to produce support systems for data management, text and language processing, privacy, clinical decision support, and data mining for knowledge discovery. These define the goals of biomedical informatics.
An increasing number of young scientists have integrated the methods and approaches of the physical and life sciences in order to address biological questions in their laboratories. Their science is distinct from other genomic sciences, which typically involve applying quantitative techniques in one lab to the biological data generated in another. The power of math-based reasoning and theoretical physics is harnessed to discover fundamental aspects of biological systems. Iterations between theory and experiment, between mechanistic models and biological validation, are the key feature of Quantitative Biology.
University of California, San Diego, United States
On the one hand, recent and novel technologies produce biological data sets of ever-increasing resolution that reveal not only genomic sequences but also RNA and protein abundances, their interactions with one another, their subcellular localization, and the identity and abundance of other biological molecules. This requires the development and application of sophisticated computational methods, encompassed by the field of bioinformatics. On the other hand, biomedical research has risen to the challenge of understanding the integrated functions of thousands of genes.
Physical and functional interaction networks chart connectivities, reveal functional modules, and provide clues on the functioning of specific genes. Using mathematical models of the stochastic and dynamical events of biology reveals fundamental design principles and allows for virtual experimentation. This is a focus of the field of systems biology. In addition, rapidly increasing capabilities of rapid molecular and genomic analyses in the clinic promise to transform medical practice in unprecedented ways. The ability to cross-query data and knowledge bases provides opportunities and challenges to computational sciences interfacing with medicine to produce support systems for data management, text and language processing, privacy, clinical decision support, and data mining for knowledge discovery. These define the goals of biomedical informatics.
An increasing number of young scientists have integrated the methods and approaches of the physical and life sciences in order to address biological questions in their laboratories. Their science is distinct from other genomic sciences, which typically involve applying quantitative techniques in one lab to the biological data generated in another. The power of math-based reasoning and theoretical physics is harnessed to discover fundamental aspects of biological systems. Iterations between theory and experiment, between mechanistic models and biological validation, are the key feature of Quantitative Biology.
University of California, San Diego, United States
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