Computational biology is a very broad discipline, in that it seeks to build models for diverse types of experimental data e. Perhaps the most important task that computational biologists carry out and that training in computational biology should equip prospective computational biologists to do is to frame biomedical problems as computational problems.
This often means looking at a biological system in a new way, challenging current assumptions or theories about the relationships between parts of the system, or integrating different sources of information to make a more comprehensive model than had been attempted before. In this context, it is worth noting that the primary goal need not be to increase human understanding of the system; even small biological systems can be sufficiently complex that scientists cannot fully comprehend or predict their properties.
Thus the goal can be the creation of the model itself; the model should account for as much currently available experimental data as possible.
Note that this does not mean that the model has been proven , even if the model makes one or more correct predictions about new experiments. N Engl J Med. Article PubMed Google Scholar. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. A survey of best practices for RNA-seq data analysis. Genome Biol. Download references. You can also search for this author in PubMed Google Scholar. Correspondence to Itai Yanai. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Reprints and Permissions. Yanai, I. Computational biologists: moving to the driver's seat. Genome Biol 18, Download citation. Published : 23 November Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative.
Skip to main content. Search all BMC articles Search. Download PDF. Abstract The recent shift of computational biologists from bioinformatics service providers to leaders of cutting-edge programs highlights the accompanying cultural and conceptual changes that should be implemented by funding bodies and academic institutions. Introduction Computational approaches began to revolutionize the life sciences a generation ago, when DNA sequences became more widely available [ 1 ].
Table 1 Shifting roles of computational biologists Full size table. Role in research Computational biology originated as a tool rather than as its own discipline as it did not embody a set of core questions. A feeling for the biology PhD training programs now enable the development of leading scientists who are not only interested in algorithm creation and data analysis but who are also keenly aware of the most pressing questions in biomedical research.
Environment By virtue of the classical university departmental structure, when computational researchers began launching independent labs they would often be surrounded by traditional life science researchers who could not speak the language of quantitative biology.
Data generation In the past, by virtue of their focus on computational approaches and lack of expertise at the bench, computational biologists would focus on data sets that were available in the public domain or from collaborators.
Data exploration The ability to generate and test a hypothesis has always been the cornerstone of science. Concluding remarks In spite of the transformation that we describe here in how computational biologists now contribute to the scientific enterprise, one aspect that has not changed is the need for computational biologists to collaborate closely with experimentalists and clinicians across the biomedical spectrum.
References 1. Google Scholar 2. Article PubMed Google Scholar 5. View author publications. Ethics declarations Competing Interests The authors declare that they have no competing interests. About this article. Cite this article Yanai, I.
Students participating in this degree program will be better positioned to develop productive careers in the burgeoning fields of computational biology and bioinformatics. Employment opportunities for students with these skills can be found throughout industry, including the areas of healthcare, forensics and the pharmaceutical companies.
As one might expect in an emerging field, even more research opportunities exist. Here are a few:.
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