Quantifying differences in cell line population dynamics using CellPD

Abstract

BackgroundThe increased availability of high-throughput datasets has revealed a need for reproducible and accessible analyses which can quantitatively relate molecular changes to phenotypic behavior. Existing tools for quantitative analysis generally require expert knowledge.ResultsCellPD (cell phenotype digitizer) facilitates quantitative phenotype analysis, allowing users to fit mathematical models of cell population dynamics without specialized training. CellPD requires one input (a spreadsheet) and generates multiple outputs including parameter estimation reports, high-quality plots, and minable XML files. We validated CellPD’s estimates by comparing it with a previously published tool (cellGrowth) and with Microsoft Excel’s built-in functions. CellPD correctly estimates the net growth rate of cell cultures and is more robust to data sparsity than cellGrowth. When we tested CellPD’s usability, biologists (without training in computational modeling) ran CellPD correctly on sample data within 30 min. To demonstrate CellPD’s ability to aid in the analysis of high throughput data, we created a synthetic high content screening (HCS) data set, where a simulated cell line is exposed to two hypothetical drug compounds at several doses. CellPD correctly estimates the drug-dependent birth, death, and net growth rates. Furthermore, CellPD’s estimates quantify and distinguish between the cytostatic and cytotoxic effects of both drugs—analyses that cannot readily be performed with spreadsheet software such as Microsoft Excel or without specialized computational expertise and programming environments.ConclusionsCellPD is an open source tool that can be used by scientists (with or without a background in computational or mathematical modeling) to quantify key aspects of cell phenotypes (such as cell cycle and death parameters). Early applications of CellPD may include drug effect quantification, functional analysis of gene knockout experiments, data quality control, minable big data generation, and integration of biological data with computational models.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0337-5) contains supplementary material, which is available to authorized users.

Extinction rates in tumor public goods games

“…However, in most tumors the net growth rate was more moderate, and the actual cellular birth and death rates were at least of similar order in magnitude (α/µ ≈ 1). This stands in contrast to the birth-death rate ratios observed in cell cultures, where birth rates often exceed death rates by an order of magnitude (α/µ ≈ 10) [42,43,41,44].…”

“…Since for both r and χ, several independent measurements were performed, we calculated distributions of α and µ for the three cell lines described above. We contrasted these distributions to in vitro distributions of cellular birth and death rates, adapted from [41] (Fig. 3 therein), which are, notably, very similar to other in vitro-values, e.g.…”

“…Although net tumor growth was high, death and birth rates were similar in all clones considered. In comparison, we also show in vitro cell line rates, estimated by Juarez et al [41]. We further used the fact that the IL11 cells are growth factor producers.…”

“…For generation of the in vitro distributions we used normally distributed rates (truncated by 0), with a mean death rate of 0.12/day (SD 0.0672) and a mean birth rate of 1.32/day (SD 0.048), adapted from Juarez et al [41].…”

Open source tools and standardized data in cancer systems biology

“…Virtu-alCell [17] can directly read microscopy data for its cell geometry. Software such as CellPD has been written to support standardized proliferation and death rate analysis of high-content microscopy data using the MultiCellDS data model [32].…”