PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 11928498

The ERATO Systems Biology Workbench: enabling interaction and exchange between software tools for computational biology.

M Hucka·A Finney·H M Sauro·H Bolouri·J Doyle·H Kitano
View original source ↗Export metadata ↓

Abstract

Researchers in computational biology today make use of a large number of different software packages for modeling, analysis, and data manipulation and visualization. In this paper, we describe the ERATO Systems Biology Workbench (SBW), a software framework that allows these heterogeneous application components--written in diverse programming languages and running on different platforms--to communicate and use each others' data and algorithmic capabilities. Our goal is to create a simple, open-source software infrastructure which is effective, easy to implement and easy to understand. SBW uses a broker-based architecture and enables applications (potentially running on separate, distributed computers) to communicate via a simple network protocol. The interfaces to the system are encapsulated in client-side libraries that we provide for different programming languages. We describe the SBW architecture and the current set of modules, as well as alternative implementation technologies.

Explore related subjects

Computational BiologyComputer Communication NetworksComputer SimulationComputer SystemsProgramming LanguagesSoftwareStochastic ProcessesUser-Computer InterfaceJournal ArticleResearch Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M Hucka, A Finney, H M Sauro, H Bolouri, J Doyle, H Kitano. 2002. The ERATO Systems Biology Workbench: enabling interaction and exchange between software tools for computational biology.. https://doi.org/10.1142/9789812799623_0042

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Phage bioinformatics tools: a review of computational approaches for bacteriophage research.

Rising clinical interest in phage therapy and the exponential growth of metagenomic sequence catalogues have driven a rapid expansion of bacteriophage bioinformatics. More than 80 dedicated tools, mostly published since 2020, now span identification, assembly, annotation, taxonomy, lifestyle prediction, defence-system detection, and host prediction. Aimed at experienced practitioners and developers, this review synthesizes the field through the lens of three successive computational paradigms: sequence homology, bounded by database completeness; machine learning, constrained by labelled training data; and foundation models, which now achieve Matthews correlation coefficients above 0.95 in identification tasks and, through structure-informed prediction, raise functional annotation to over half of phage genes. Furthermore, we map the upstream components, namely, gene callers, homology engines, protein language models, and structural search tools, that underpin most downstream pipelines, exposing shared infrastructure and ecosystem-level fragility when dependencies change. To translate this into practice, we propose web-based and command-line reference workflows calibrated to user expertise and sample types. Finally, we set an agenda for the next wave of tool development. Roughly half of phage genes still resist functional annotation despite structural methods; no broadly generalizable strain-level host predictor exists for phage therapy; varying true-positive rates (0%-97%) underscore the absence of standardized community benchmarks analogous to Critical Assessment of Structure Prediction or Critical Assessment of Metagenome Interpretation. As generative genome models begin designing synthetic phages, progress will depend less on producing standalone tools than on rigorous evaluation, interoperable infrastructure, and clinically meaningful prediction targets.

Computational Biology↗
PubMed2026

Collective posterior inference from highly variable empirical replicates.

High-throughput experimental platforms now routinely generate data from dozens or hundreds of independent observations. Simulation-based inference (SBI) offers a powerful framework for estimating model parameters from such complex datasets, but standard methods struggle to scale to the noisy multiple-replicates regime without incurring prohibitive computational costs or careful hyperparameter tuning. Here, we introduce a new method for fast and robust collective posterior inference from multiple independent replicates using a robust product-of-experts aggregation scheme that automatically mitigates the influence of outliers. Evaluating it on synthetic and empirical evolutionary datasets, we find it achieves state-of-the-art estimation accuracy and computational efficiency, including inference from noisy observations. Our method is compatible with any SBI framework, providing a scalable, plug-and-play solution for inference from noisy multiple-replicate datasets.

Computational Biology↗
PubMed2026

ReGAIN: a bioinformatics platform for assessing probabilistic co-occurrence between resistance genes in bacterial pathogens.

MOTIVATION: Multidrug-resistant bacterial pathogens continue to rise globally, yet scalable methods are needed to infer how resistance determinants co-occur across pathogen populations and to quantify conditional dependencies underlying co-occurrence and shared genetic context. RESULTS: We present ReGAIN (Resistance Gene Association and Inference Network), an open-source platform that applies Bayesian network structure learning to infer probabilistic, conditional dependency relationships among antibiotic resistance, heavy metal tolerance, stress response, and virulence determinants in bacteria. In contrast to pairwise co-occurrence analyses, ReGAIN reports conditional probabilities, relative risks, and absolute risk differences with confidence intervals to prioritize candidate relationships for downstream prioritization. Applied across ESKAPEE pathogens, ReGAIN recapitulated established resistance gene relationships and identified additional candidate patterns consistent with co-selection and shared genetic context. Together, these results support scalable, reproducible population-wide analysis of resistance networks for surveillance, comparative genomics and epidemiology. AVAILABILITY: ReGAIN analyses are performed using Python v3.11.5 and R v4.4.1 and is available as open-source software through Bioconda at {https://anaconda.org/bioconda/regain-cli}. Source code and documentation can be found at {https://github.com/ERBringHorvath/regain_CLI}. All genomes used in this publication were downloaded from the National Center for Biotechnology Information database. Large supplementary tables and results data from the ESKAPEE pathogen example network analyses can be downloaded from https://figshare.com/articles/dataset/ReGAIN_command_line_software_and_supplemental_figures_/28959431.

Computational Biology↗