Abstract
Systems biology combines computational modeling with quantitative experimental measurements to study complex biological processes. Here, we outline an approach for parameterizing and validating a systems biology model to yield predictive tool that can generate testable hypotheses and expand biological understanding.
Footnotes
Research supported by the National Cancer Institute (F31CA200242 to J.A.R.) and National Science Foundation (1552065 to S.D.F.).
J. A. Rohrs, S. Z. Makaryan, and S. D. Finley are with the University of Southern California, Los Angeles, CA 90089 USA (J.A.R. email: jrohrs{at}usc.edu; S.Z.M. email: makaryan{at}usc.edu; S.D.F., corresponding author, phone: 213-740-8788; fax: 213-821-3897; e-mail: sfinley{at}usc.edu).
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.