Abstract
Motivation: Rapid advances in live-cell imaging analysis and mathematical modeling have produced a large amount of quantitative data on spatiotemporal dynamics of biological objects ranging from molecules to organisms. There is now a crucial need to bring these large amounts of quantitative biological dynamics data together centrally in a coherent and systematic manner. This will facilitate the reuse of this data for further analysis.Results: We have developed the Systems Science of Biological Dynamics database (SSBD) to store and share quantitative biological dynamics data. SSBD currently provides 311 sets of quantitative data for single molecules, nuclei and whole organisms in a wide variety of model organisms from Escherichia coli to Mus musculus. The data are provided in Biological Dynamics Markup Language format and also through a REST API. In addition, SSBD provides 188 sets of time-lapse microscopy images from which the quantitative data were obtained and software tools for data visualization and analysis.Availability and Implementation: SSBD is accessible at http://ssbd.qbic.riken.jp.Contact: sonami@riken.jp
Highlights
One of the leading challenges of systems biology is to understand the nature of the dynamical behaviors of biological phenomena
We present the Systems Science of Biological Dynamics database (SSBD; http://ssbd.qbic.riken.jp) for storing and sharing quantitative biological dynamics data
The first way is to download a dataset with the use of a unified format for representing quantitative biological dynamics data called Biological Dynamics Markup Language (BDML; Kyoda et al, 2015)
Summary
One of the leading challenges of systems biology is to understand the nature of the dynamical behaviors of biological phenomena. Computational image analysis techniques can quantitatively extract numerical data from these microscopy images (Peng, 2008; Sommer and Gerlich, 2013) These quantitative biological dynamics data can be analyzed further to provide crucial insight into the nature of dynamical behaviors of biological phenomena. Data of microtubule-dependent pronuclear migration in early C.elegans embryos were generated to reveal the mechanism of the nuclear centering process (Kimura and Onami, 2005) In such simulation studies, data from computer simulations are often compared with in vivo dynamic patterns from biological experiments to evaluate the model validity. The deviation in predictions can be reused leading to further experiments and refinement of the models (Mogilner et al, 2006) Such kinds of quantitative data from computer simulation are neither stored nor shared. It provides additional software tools for data visualization and analysis
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