BGFit: management and automated fitting of biological growth curves
1 INESC-ID, R Alves Redol 9, 1000-029 Lisbon, Portugal
2 Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa (ITQB-UNL), Avenida da República, 2780-157 Oeiras, Portugal
3 Centre for Intelligent Systems, IDMEC-LAETA, IST-UL, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
4 Present address: CED-Discovery, Chr Hansen A/S, 10-12 Bøge Allé, DK2970, Hørsholm, Denmark
BMC Bioinformatics 2013, 14:283 doi:10.1186/1471-2105-14-283Published: 25 September 2013
Existing tools to model cell growth curves do not offer a flexible integrative approach to manage large datasets and automatically estimate parameters. Due to the increase of experimental time-series from microbiology and oncology, the need for a software that allows researchers to easily organize experimental data and simultaneously extract relevant parameters in an efficient way is crucial.
BGFit provides a web-based unified platform, where a rich set of dynamic models can be fitted to experimental time-series data, further allowing to efficiently manage the results in a structured and hierarchical way. The data managing system allows to organize projects, experiments and measurements data and also to define teams with different editing and viewing permission. Several dynamic and algebraic models are already implemented, such as polynomial regression, Gompertz, Baranyi, Logistic and Live Cell Fraction models and the user can add easily new models thus expanding current ones.
BGFit allows users to easily manage their data and models in an integrated way, even if they are not familiar with databases or existing computational tools for parameter estimation. BGFit is designed with a flexible architecture that focus on extensibility and leverages free software with existing tools and methods, allowing to compare and evaluate different data modeling techniques. The application is described in the context of bacterial and tumor cells growth data fitting, but it is also applicable to any type of two-dimensional data, e.g. physical chemistry and macroeconomic time series, being fully scalable to high number of projects, data and model complexity.