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
During animal development, cells undergo dynamic changes in position and gene expression. A collection of quantitative information about morphological dynamics under a wide variety of gene perturbations would provide a rich resource for understanding the molecular mechanisms of development. Here, we created a database, the Worm Developmental Dynamics Database (http://so.qbic.riken.jp/wddd/), which stores a collection of quantitative information about cell division dynamics in early Caenorhabditis elegans embryos with single genes silenced by RNA-mediated interference. The information contains the three-dimensional coordinate values of the outlines of nuclear regions and the dynamics of the outlines over time. The database provides free access to 50 sets of quantitative data for wild-type embryos and 136 sets of quantitative data for RNA-mediated interference embryos corresponding to 72 of the 97 essential embryonic genes on chromosome III. The database also provides sets of four-dimensional differential interference contrast microscopy images on which the quantitative data were based. The database will provide a novel opportunity for the development of computational methods to obtain fresh insights into the mechanisms of development. The quantitative information and microscopy images can be synchronously viewed through a web browser, which is designed for easy access by experimental biologists.
Motivation: Large-scale gene expression profiles measured in gene deletion mutants are invaluable sources for identifying gene regulatory networks. Signed directed graph (SDG) is the most common representation of gene networks in genetics and cell biology. However, no practical procedure that deduces SDGs consistent with such profiles has been developed. Results: We developed the DBRF–MEGN (difference-based regulation finding–minimum equivalent gene network) method in which an algorithm deduces the most parsimonious SDGs consistent with expression profiles of gene deletion mutants. Positive (or negative) directed edges representing positive (or negative) gene regulations are deduced by comparing the gene expression level between the wild-type and mutant. The most parsimonious SDGs are deduced using graph theoretical procedures. Compensation for excess removal of edges by restoring a minimum number of edges makes the method applicable to cyclic gene networks. Use of independent groups of edges greatly reduces the computational cost, thus making the method applicable to large-scale expression profiles. We confirmed the applicability of our method by applying it to the gene expression profiles of 265 Saccharomyces cerevisiae deletion mutants, and we confirmed our method's validity by comparing the pheromone response pathway, general amino acid control system, and copper and iron homeostasis system deduced by our method with those reported in the literature. Interpretation of the gene network deduced from the S. cerevisiae expression profiles by using our method led to the prediction of 132 transcriptional targets and modulators of transcriptional activity of 18 transcriptional regulators. Availability: The software is available on request. Supplementary information: http://www.so.bio.keio.ac.jp/dbrf-megn/
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