Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451324 | PMC |
http://dx.doi.org/10.2390/biecoll-jib-2015-266 | DOI Listing |
Am J Clin Pathol
January 2025
Department of Pathology, Saint Louis University, St Louis, MO, US.
Objectives: The College of American Pathologists (CAP) Cancer Protocols are developed to facilitate cancer synoptic reporting. CAP offers these Cancer Protocols in both free printable and commercially licensed electronic formats. Several academic institutions have also implemented these Cancer Protocols as web-based services.
View Article and Find Full Text PDFPrev Vet Med
December 2024
Department of Genetics, Animal Breeding and Ethology, Faculty of Animal Science, University of Agriculture in Krakow, al. Mickiewicza 24/28, Krakow 30-059, Poland. Electronic address:
The purpose of the paper was to apply an Artificial Neural Networks with Radial Basis Function to develop an application model for diagnosing a subclinical ketosis type I and II in dairy cattle. While building the neural network model, applied methodology was compatible to the procedures used in Data Mining processes. The data set was created based on the composition of milk samples of 1520 Polish Holstein-Friesian cows.
View Article and Find Full Text PDFPlant Phenomics
December 2024
Department of General and Organic Viticulture, Hochschule Geisenheim University, Geisenheim, Germany.
Understanding root system architecture (RSA) is essential for improving crop resilience to climate change, yet assessing root systems of woody perennials under field conditions remains a challenge. This study introduces a pipeline that combines field excavation, in situ 3-dimensional digitization, and transformation of RSA data into an interoperable format to analyze and model the growth and water uptake of grapevine rootstock genotypes. Eight root systems of each of 3 grapevine rootstock genotypes ("101-14", "SO4", and "Richter 110") were excavated and digitized 3 and 6 months after planting.
View Article and Find Full Text PDFPLoS One
December 2024
Department of Bioengineering, University of Washington, Seattle, WA, United States of America.
The reproducibility of computational biology models can be greatly facilitated by widely adopted standards and public repositories. We examined 50 models from the BioModels Database and attempted to validate the original curation and correct some of them if necessary. For each model, we reproduced these published results using Tellurium.
View Article and Find Full Text PDFBioinform Adv
November 2024
Department of Biotechnology, Delft University of Technology, Delft 2629 HZ, The Netherlands.
Summary: We present py_cFBA, a Python-based toolbox for conditional flux balance analysis (cFBA). Our toolbox allows for an easy implementation of cFBA models using a well-documented and modular approach and supports the generation of Systems Biology Markup Language models. The toolbox is designed to be user-friendly, versatile, and freely available to non-commercial users, serving as a valuable resource for researchers predicting metabolic behaviour with resource allocation in dynamic-cyclic environments.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!