Abstract
A current challenge in data-driven mathematical modeling of cancer is identifying biologically-relevant parameters of mathematical models from sparse and often noisy experimental data of mixed types. We describe a cell cycle model and outline how to use the Optimization Toolbox in Matlab to estimate its timescale parameters, given flow cytometry and cell viability (synthetic) data, and illustrate the technique with simulated data. This technique can be similarly applied to a variety of cell cycle models, particularly as more laboratories begin to use high-content, quantitative cell screening and imaging platforms. An advanced version of this work (CellPD: cell line phenotype digitizer) will be released as open source in early 2016 at MultiCellDS.org.
Footnotes
Research supported by The Breast Cancer Research Foundation, USC James H. Zumberge Research & Innovation Fund, and USC Provost’s PhD Fellowship.
E. Juarez, A. Ghaffarizadeh, S. H. Friedman, and P. Macklin are with the Center for Applied Molecular Medicine, Department of Medicine, University of Southern California, 2250 Alcazar St., HSC-CSC 240, Los Angeles, Ca 90033–9075, USA. E-mail: [juarezro{at}usc.edu, aghaffar{at}usc.edu, samuelf{at}usc.edu, paul.macklin{at}usc.edu.
