BioNumbers is a comprehensive database of key quantitative properties in molecular and cell biology, designed to support computational, systems, and molecular cell biology research. The database covers a wide range of biological parameters, from cell sizes and metabolite concentrations to reaction rates and genome sizes. It aims to address the challenge of finding accurate and up-to-date numbers in the vast biological literature, which can be time-consuming and frustrating for researchers. BioNumbers is highly searchable and allows users to query by keywords or browse through menus. Registered users can contribute new entries and comments, which are curated to maintain high quality. The database currently contains over 4500 distinct properties from more than 200 organisms, with data extracted from over 1000 references. It is implemented as a Microsoft ASP.NET 2.0 web application with a Microsoft SQL Server 2005 database. The authors discuss the importance of numbers in transforming qualitative models into quantitative predictive models and highlight the database's role in facilitating research. Future plans include expanding the database's functionality, such as a 'BioNumber of the month' feature and a comparative table builder, and potentially creating a 'Journal of BioNumbers' to encourage the publication of important biological numbers.BioNumbers is a comprehensive database of key quantitative properties in molecular and cell biology, designed to support computational, systems, and molecular cell biology research. The database covers a wide range of biological parameters, from cell sizes and metabolite concentrations to reaction rates and genome sizes. It aims to address the challenge of finding accurate and up-to-date numbers in the vast biological literature, which can be time-consuming and frustrating for researchers. BioNumbers is highly searchable and allows users to query by keywords or browse through menus. Registered users can contribute new entries and comments, which are curated to maintain high quality. The database currently contains over 4500 distinct properties from more than 200 organisms, with data extracted from over 1000 references. It is implemented as a Microsoft ASP.NET 2.0 web application with a Microsoft SQL Server 2005 database. The authors discuss the importance of numbers in transforming qualitative models into quantitative predictive models and highlight the database's role in facilitating research. Future plans include expanding the database's functionality, such as a 'BioNumber of the month' feature and a comparative table builder, and potentially creating a 'Journal of BioNumbers' to encourage the publication of important biological numbers.