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: [email protected].

MOTIVATION: Recent progress in live-cell imaging and modeling techniques has resulted in generation of a large amount of quantitative data (from experimental measurements and computer simulations) on spatiotemporal dynamics of biological objects such as molecules, cells and organisms. Although many research groups have independently dedicated their efforts to developing software tools for visualizing and analyzing these data, these tools are often not compatible with each other because of different data formats. RESULTS: We developed an open unified format, Biological Dynamics Markup Language (BDML; current version: 0.2), which provides a basic framework for representing quantitative biological dynamics data for objects ranging from molecules to cells to organisms. BDML is based on Extensible Markup Language (XML). Its advantages are machine and human readability and extensibility. BDML will improve the efficiency of development and evaluation of software tools for data visualization and analysis. AVAILABILITY AND IMPLEMENTATION: A specification and a schema file for BDML are freely available online at http://ssbd.qbic.riken.jp/bdml/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The enzyme nomenclature system, commonly known as the enzyme commission (EC) number, plays a key role in classifying and predicting enzymatic reactions. However, numerous reactions have been described in various pathways that do not have an official EC number, and the reactions are not expected to have an EC number assigned because of a lack of articles published on enzyme assays. To predict the EC number of a non-classified enzymatic reaction, we focus on the structural similarity of its substrate and product to the substrate and product of reactions that have been classified. RESULTS: We propose a new method to assign EC numbers using a maximum common substructure algorithm, mutual information and a support vector machine, termed the Enzyme COmmission numbers Handler (ECOH). A jack-knife test shows that the sensitivity, precision and accuracy of the method in predicting the first three digits of the official EC number (i.e. the EC sub-subclass) are 86.1%, 87.4% and 99.8%, respectively. We furthermore demonstrate that, by examining the ranking in the candidate lists of EC sub-subclasses generated by the algorithm, the method can successfully predict the classification of 85 enzymatic reactions that fall into multiple EC sub-subclasses. The better performance of the ECOH as compared with existing methods and its flexibility in predicting EC numbers make it useful for predicting enzyme function. AVAILABILITY: ECOH is freely available via the Internet at http://www.bioinfo.sk.ritsumei.ac.jp/apps/ecoh/. This program only works on 32-bit Windows.

This paper presents a clustering method for the comparative analysis of genomes and metabolic pathways. Our method consists of several steps: (1) extract linear sequences of metabolic reactions (non-branching pathways) from a network of metabolic pathways, (2) perform pairwise alignments of every pair of non-branching pathways, and (3) explore a set of genes for each aligned non-branching pathways such that those genes encode enzymes of the pathway and the genes are closely located on a genome (such as operons or gene clusters). To measure the similarity between pathways, we formalized a scoring system by using the functional hierarchy of the EC numbers of enzymes. By applying our method to the metabolic pathways in Escherichia coli, we have obtained several pairs of pathways with similar reactions having enzymes encoded by neighbor genes on the E. coli genome. The resulting pathway pairs were mainly found in metabolisms between similar amino acids (tryptophan and histidine) and between similar sugars (fucose and rhamnose).