PT  - JOURNAL ARTICLE
AU  - Edwin Juarez
AU  - Ahmadreza Ghaffarizadeh
AU  - Samuel H. Friedman
AU  - Edmond Jonckheere
AU  - Paul Macklin
TI  - Estimating cell cycle model parameters using systems identification
AID  - 10.1101/035766
DP  - 2015 Jan 01
TA  - bioRxiv
PG  - 035766
4099  - http://biorxiv.org/content/early/2015/12/31/035766.short
4100  - http://biorxiv.org/content/early/2015/12/31/035766.full
AB  - 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.