Activity-independent nuclear position of cancer genes (green and purple) in cells of a model breast tumor. See footnote for details.
Sarah A Webb profiles Franziska Michor’s work in applying mathematical modelling to gain a better understanding of cancer.
Based at Memorial-Sloan Kettering Cancer Center in New York, Michor is using mathematics, computer science, biology, and medicine to investigate the origins of cancer, relationships among cancer types and the emergence of drug-resistant tumors.
“While her work has attracted the attention of collaborators and funders, publishing her research has proved less straightforward, she says. Even though computational work can be done relatively quickly, Michor’s work relies on experiments done by her collaborators to verify that models are biologically or medically relevant. Because of these cross-disciplinary methods, a paper can include sequencing data, gene expression data, and growth data alongside the mathematical models. As with some other interdisciplinary fields, it’s often difficult to find the right journal or reviewers who can evaluate the combination of mathematics and biology as a cohesive whole rather than as individual components.” [my emphasis]
Consider how such a research paper could be published in an open-access repository along with original datasets and mathematical models. Identifying appropriate data storage, metadata and effective cross-linkages would be an interesting challenge.
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Footnote: The image above shows activity-independent nuclear position of cancer genes (green and purple) in cells of a model breast tumor. (JCB 180(1) TOC1) This image is available to the public to copy, distribute, or display under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported licence. Reference: Meaburn and Misteli (2008) J. Cell Biol. 180:39-50. Published on: January 14, 2008. doi: 10.1083/jcb.200708204. Read the full article at: jcb.rupress.org/cgi/content/full/180/1/39 Image published on flickr.com by The JCB
