title: Automated inference procedure for the determination of cell growth parameters.
abstract: The growth rate and carrying capacity of a cell population are key to the characterization of the population's viability and to the quantification of its responses to perturbations such as drug treatments. Accurate estimation of these parameters necessitates careful analysis. Here, we present a rigorous mathematical approach for the robust analysis of cell count data, in which all the experimental stages of the cell counting process are investigated in detail with the machinery of Bayesian probability theory. We advance a flexible theoretical framework that permits accurate estimates of the growth parameters of cell populations and of the logical correlations between them. Moreover, our approach naturally produces an objective metric of avoidable experimental error, which may be tracked over time in a laboratory to detect instrumentation failures or lapses in protocol. We apply our method to the analysis of cell count data in the context of a logistic growth model by means of a user-friendly computer program that automates this analysis, and present some samples of its output. Finally, we note that a traditional least squares fit can provide misleading estimates of parameter values, because it ignores available information with regard to the way in which the data have actually been collected.
authors: Department of Physics, University of Toronto, 60 St. George Street, Toronto, Ontario, M5S 1A7, Canada.
authors: Edouard A
authors: EA
authors: Harris
authors: Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario, M5S 3E1, Canada.
authors: Eun Jee
authors: EJ
authors: Koh
authors: Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, Ontario, M5S 3E1, Canada.
authors: Jason
authors: J
authors: Moffat
authors: Department of Chemical and Physical Sciences, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, Ontario, L5L 1C6, Canada.|Impact Centre, University of Toronto, 112 College Street, Toronto, Ontario, M5G 1A7, Canada.
authors: David R
authors: DR
authors: McMillen
mesh_terms: D022126:Access to Information; D000465:Algorithms; D001499:Bayes Theorem; D002452:Cell Count / Q000379:methods*; D045744:Cell Line, Tumor; D049109:Cell Proliferation; D005260:Female; D006801:Humans; D020407:Internet; D016018:Least-Squares Analysis; D016015:Logistic Models; D008954:Models, Biological; D010051:Ovarian Neoplasms / Q000503:physiopathology; D011336:Probability; D012984:Software; D013995:Time