GPRuler – Metabolic Gene-Protein-Reaction Rules Automatic Reconstruction

GPRuler

:: DESCRIPTION

GPRuler is an open-source tool to automate the reconstruction process of gene-protein-reaction (GPR) rules for any living organism, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction.

::DEVELOPER

GPRuler team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

GPRuler

:: MORE INFORMATION

Citation:

Di Filippo M, Damiani C, Pescini D.
GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction.
PLoS Comput Biol. 2021 Nov 8;17(11):e1009550. doi: 10.1371/journal.pcbi.1009550. Epub ahead of print. PMID: 34748537.

Vesimulus 2.1 – Molecular Stochastic Simulations of Metabolic or Signaling Systems

Vesimulus 2.1

:: DESCRIPTION

Vesimulus is a simulation package for molecular stochastic simulations of metabolic or regulatory biological systems.

::DEVELOPER

Chair of Computational Biology at the Saarland University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX

:: DOWNLOAD

Vesimulus

:: MORE INFORMATION

Citation

PLoS One. 2010 Nov 22;5(11):e14070. doi: 10.1371/journal.pone.0014070.
Bridging the gap: linking molecular simulations and systemic descriptions of cellular compartments.
Geyer T, Mol X, Blass S, Helms V.

Metingear 1.1.6 – The Metabolic Development Environment

Metingear 1.1.6

:: DESCRIPTION

Metingear is an open source desktop application for creating and curating genome scale metabolic networks with chemical structure.

::DEVELOPER

John May

:: SCREENSHOTS

:: REQUIREMENTS

Windows/ Linux/ MacOsX
Java

:: DOWNLOAD

Metingear

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Sep 1;29(17):2213-5. doi: 10.1093/bioinformatics/btt342. Epub 2013 Jun 13.
Metingear: a development environment for annotating genome-scale metabolic models.
May JW1, James AG, Steinbeck C.

idFBA – Dynamic analysis of integrated Signaling, Metabolic, and Regulatory Networks

idFBA

:: DESCRIPTION

idFBA (integrated dynamic FBA) is a flux balance analysis (FBA)-based strategy that dynamically simulates cellular phenotypes arising from integrated networks.

::DEVELOPER

the Computational Systems Biology Laboratory, Department of Biomedical Engineering, University of Virginia.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows/ MacOsX
MatLab

:: DOWNLOAD

idFBA

:: MORE INFORMATION

Citation:

Lee, J.M., E.P. Gianchandani, J.A. Eddy, and J.A. Papin. 2008.
Dynamic analysis of integrated signaling, metabolic, and regulatory networks.
PLoS Computational Biology, 4(5): e1000086