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T. Sakurada

Bio: T. Sakurada is an academic researcher from Keio University. The author has contributed to research in topics: BioPAX : Biological Pathways Exchange & Systems Biology Ontology. The author has an hindex of 2, co-authored 2 publications receiving 3054 citations.

Papers
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Journal ArticleDOI
TL;DR: This work summarizes the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks, a software-independent language for describing models common to research in many areas of computational biology.
Abstract: Motivation: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. Results: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. ∗ To whom correspondence should be addressed. Availability: The specification of SBML Level 1 is freely available from http://www.sbml.org/.

3,205 citations

Posted ContentDOI
27 Nov 2020-bioRxiv
TL;DR: This work developed a robotic AI system with a batch Bayesian optimization algorithm that autonomously induces the differentiation of induced pluripotent stem cell-derived retinal pigment epithelial (iPSC-RPE) cells, demonstrating immense use in medicine and research.
Abstract: Induced differentiation is one of the most experience- and skill-dependent processes in regenerative medicine, and establishing optimal conditions often takes years (an inordinate amount of time). Here, we developed a robotic-AI system that autonomously induces the differentiation of iPS cell-derived RPE (iPSC-RPE) cells. The system performed 216 forty-day cell culture experiments, with a total experimentation time of 8,640 days. The search for optimal differentiation conditions was accelerated using a novel batch Bayesian optimization technique with local penalization, compressing the search time to 185 days, with a cumulative robot operating time of 995 h. From 200 million possible parameter combinations, the system optimized the iPSC-RPE production conditions to yield improved pigmented scores that were up to 88% higher than the scores obtained with the pre-optimized conditions. Transferring tacit knowledge and skills often constitutes a serious obstacle when transposing basic cell experimental research from the laboratory to the medical forefront or for scaling to mass production. Our work demonstrates that autonomous robotic-AI systems can be effectively utilized for the systematic exploration of experimental conditions independently from the tacit knowledge of skilled professionals. This option guarantees immense use in future research.

15 citations


Cited by
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Journal ArticleDOI
TL;DR: An update on the online database resource Search Tool for the Retrieval of Interacting Genes (STRING), which provides uniquely comprehensive coverage and ease of access to both experimental as well as predicted interaction information.
Abstract: An essential prerequisite for any systems-level understanding of cellular functions is to correctly uncover and annotate all functional interactions among proteins in the cell. Toward this goal, remarkable progress has been made in recent years, both in terms of experimental measurements and computational prediction techniques. However, public efforts to collect and present protein interaction information have struggled to keep up with the pace of interaction discovery, partly because protein-protein interaction information can be error-prone and require considerable effort to annotate. Here, we present an update on the online database resource Search Tool for the Retrieval of Interacting Genes (STRING); it provides uniquely comprehensive coverage and ease of access to both experimental as well as predicted interaction information. Interactions in STRING are provided with a confidence score, and accessory information such as protein domains and 3D structures is made available, all within a stable and consistent identifier space. New features in STRING include an interactive network viewer that can cluster networks on demand, updated on-screen previews of structural information including homology models, extensive data updates and strongly improved connectivity and integration with third-party resources. Version 9.0 of STRING covers more than 1100 completely sequenced organisms; the resource can be reached at http://string-db.org.

3,239 citations

Journal ArticleDOI
TL;DR: This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations.
Abstract: Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network. This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations.

3,229 citations

Journal ArticleDOI
TL;DR: COPASI is presented, a platform-independent and user-friendly biochemical simulator that offers several unique features, and numerical issues with these features are discussed; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic-stochastic methods, and the importance of random number generator numerical resolution in Stochastic simulation.
Abstract: Motivation: Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods. Results: Here, we present COPASI, a platform-independent and user-friendly biochemical simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic--stochastic methods, and the importance of random number generator numerical resolution in stochastic simulation. Availability: The complete software is available in binary (executable) for MS Windows, OS X, Linux (Intel) and Sun Solaris (SPARC), as well as the full source code under an open source license from http://www.copasi.org. Contact: mendes@vbi.vt.edu

2,351 citations

Journal ArticleDOI
TL;DR: This protocol provides a detailed description of how to analyze genome-wide experimental data with the PANTHER classification system, and redesigned the website interface to improve both user experience and the system's analytical capability.
Abstract: The PANTHER (protein annotation through evolutionary relationship) classification system (http://wwwpantherdborg/) is a comprehensive system that combines gene function, ontology, pathways and statistical analysis tools that enable biologists to analyze large-scale, genome-wide data from sequencing, proteomics or gene expression experiments The system is built with 82 complete genomes organized into gene families and subfamilies, and their evolutionary relationships are captured in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models or HMMs) Genes are classified according to their function in several different ways: families and subfamilies are annotated with ontology terms (Gene Ontology (GO) and PANTHER protein class), and sequences are assigned to PANTHER pathways The PANTHER website includes a suite of tools that enable users to browse and query gene functions, and to analyze large-scale experimental data with a number of statistical tests It is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists In the 2013 release of PANTHER (v80), in addition to an update of the data content, we redesigned the website interface to improve both user experience and the system's analytical capability This protocol provides a detailed description of how to analyze genome-wide experimental data with the PANTHER classification system

2,221 citations

Journal ArticleDOI
TL;DR: The constraint-based reconstruction and analysis toolbox as discussed by the authors is a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraintbased approach and allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules.
Abstract: The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.

1,827 citations