Web-based Applications for Building, Managing and Analysing Kinetic Models of Biological Systems

Overview

Journal Brief Bioinform

Publisher Oxford University Press

Specialty Biology

Date 2008 Sep 23

PMID 18805901

Citations 6

Authors

Dong-Yup Lee

Rajib Saha

Faraaz Noor Khan Yusufi

Wonjun Park

Iftekhar A Karimi

Affiliations

Soon will be listed here.

Abstract

Mathematical modelling and computational analysis play an essential role in improving our capability to elucidate the functions and characteristics of complex biological systems such as metabolic, regulatory and cell signalling pathways. The modelling and concomitant simulation render it possible to predict the cellular behaviour of systems under various genetically and/or environmentally perturbed conditions. This motivates systems biologists/bioengineers/bioinformaticians to develop new tools and applications, allowing non-experts to easily conduct such modelling and analysis. However, among a multitude of systems biology tools developed to date, only a handful of projects have adopted a web-based approach to kinetic modelling. In this report, we evaluate the capabilities and characteristics of current web-based tools in systems biology and identify desirable features, limitations and bottlenecks for further improvements in terms of usability and functionality. A short discussion on software architecture issues involved in web-based applications and the approaches taken by existing tools is included for those interested in developing their own simulation applications.

Citing Articles

CerealESTDb: A Comprehensive Resource for Abiotic Stress-Responsive Annotated ESTs With Predicted Genes, Gene Ontology, and Metabolic Pathways in Major Cereal Crops.

Kumar S, Bhati J, Saha A, Lal S, Pandey P, Mishra D Front Genet. 2022; 13:842868.

PMID: 35281847 PMC: 8907976. DOI: 10.3389/fgene.2022.842868.

SBML Level 3: an extensible format for the exchange and reuse of biological models.

Keating S, Waltemath D, Konig M, Zhang F, Drager A, Chaouiya C Mol Syst Biol. 2020; 16(8):e9110.

PMID: 32845085 PMC: 8411907. DOI: 10.15252/msb.20199110.

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.

Tang J, Fu J, Wang Y, Li B, Li Y, Yang Q Brief Bioinform. 2019; 21(2):621-636.

PMID: 30649171 PMC: 7299298. DOI: 10.1093/bib/bby127.

NOREVA: normalization and evaluation of MS-based metabolomics data.

Li B, Tang J, Yang Q, Li S, Cui X, Li Y Nucleic Acids Res. 2017; 45(W1):W162-W170.

PMID: 28525573 PMC: 5570188. DOI: 10.1093/nar/gkx449.

EasyLCMS: an asynchronous web application for the automated quantification of LC-MS data.

Fructuoso S, Sevilla A, Bernal C, Lozano A, Iborra J, Canovas M BMC Res Notes. 2012; 5:428.

PMID: 22884039 PMC: 3494514. DOI: 10.1186/1756-0500-5-428.

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