Background: Existing tools to model cell growth curves do not offer a flexible integrative approach to manage large\r\ndatasets and automatically estimate parameters. Due to the increase of experimental time-series from microbiology\r\nand oncology, the need for a software that allows researchers to easily organize experimental data and\r\nsimultaneously extract relevant parameters in an efficient way is crucial.\r\nResults: BGFit provides a web-based unified platform, where a rich set of dynamic models can be fitted to\r\nexperimental time-series data, further allowing to efficiently manage the results in a structured and hierarchical way.\r\nThe data managing system allows to organize projects, experiments and measurements data and also to define teams\r\nwith different editing and viewing permission. Several dynamic and algebraic models are already implemented, such\r\nas polynomial regression, Gompertz, Baranyi, Logistic and Live Cell Fraction models and the user can add easily new\r\nmodels thus expanding current ones.\r\nConclusions: BGFit allows users to easily manage their data and models in an integrated way, even if they are not\r\nfamiliar with databases or existing computational tools for parameter estimation. BGFit is designed with a flexible\r\narchitecture that focus on extensibility and leverages free software with existing tools and methods, allowing to\r\ncompare and evaluate different data modeling techniques. The application is described in the context of bacterial and\r\ntumor cells growth data fitting, but it is also applicable to any type of two-dimensional data, e.g. physical chemistry\r\nand macroeconomic time series, being fully scalable to high number of projects, data and model complexity.
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