BackgroundPlate readers can measure the growth curves of many microbial strains in a high-throughput fashion. The hundreds of absorbance readings collected simultaneously for hundreds of samples create technical hurdles for data analysis.ResultsGrowthcurver summarizes the growth characteristics of microbial growth curve experiments conducted in a plate reader. The data are fitted to a standard form of the logistic equation, and the parameters have clear interpretations on population-level characteristics, like doubling time, carrying capacity, and growth rate.ConclusionsGrowthcurver is an easy-to-use R package available for installation from the Comprehensive R Archive Network (CRAN). The source code is available under the GNU General Public License and can be obtained from Github (Sprouffske K, Growthcurver sourcecode, 2016).

The canonical route from normal tissue to cancer occurs through sequential acquisition of somatic mutations. Many studies have constructed a linear genetic model for tumorigenesis using the genetic alterations associated with samples at different stages of neoplastic progression from cross-sectional data. The common interpretation of these models is that they reflect the temporal order within any given tumor. Linear genetic methods implicitly neglect genetic heterogeneity within a neoplasm; each neoplasm is assumed to consist of one dominant clone. We modeled neoplastic progression of colorectal cancer using an agent-based model of a colon crypt and found clonal heterogeneity within our simulated neoplasms, as observed in vivo. Just 7.3% of cells within neoplasms acquired mutations in the same order as the linear model. In 41% of the simulated neoplasms, no cells acquired mutations in the same order as the linear model. We obtained similarly poor results when comparing the temporal order to oncogenetic tree models inferred from cross-sectional data. However, when we reconstructed the cell lineage of mutations within a neoplasm using several biopsies, we found 99.7% cells within neoplasms acquired their mutations in an order consistent with the cell lineage mutational order. Thus, we find that using cross-sectional data to infer mutational order is misleading, while phylogenetic methods based on sampling intra-tumor heterogeneity accurately reconstructs the evolutionary history of tumors. Additionally, we find evidence that disruption of differentiation is likely the first lesion in progression for most cancers, and should be one of the few regularities of neoplastic progression across cancers.