Abstract
Mathematical interpretation and integration of experimental data for the goal of biological theory development has had little, if no, impact on previous progress in life sciences compared with the sophistication of experimental approaches themselves. The genesis of recent spectacular breakthroughs in molecular biology that led to the discovery of the enzymatic function of several nonmetabolic enzymes illustrates that this relationship is beginning to change.
The development of high-throughput technologies, for example of complete genome sequencing, leads to large amounts of quantified data on biological systems without direct link to biological function that require formalized and complex mathematical approaches for their interpretation. The research success in life sciences depends increasingly on the ability of researchers in experimental and theoretical biology to jointly focus on important questions. Currendy, theoretical methods have best chances to contribute to new biological insight independently of experiments in the area of genome text interpretation and especially for gene function prediction. Experimental studies can help progress in the development of theoretical methods by providing verified, sufficiently large and variable sequence datasets for the exploration of sequence-function relationships.
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Eisenhaber, F. (2006). Introduction. In: Discovering Biomolecular Mechanisms with Computational Biology. Molecular Biology Intelligence Unit. Springer, Boston, MA. https://doi.org/10.1007/0-387-36747-0_1
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DOI: https://doi.org/10.1007/0-387-36747-0_1
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