iVUN 1.2 – interactive Visualization of Uncertain Biochemical Reaction Networks

iVUN 1.2

:: DESCRIPTION

iVUN is a visualization toolbox which supports uncertainty-aware analysis of static and dynamic attributes of biochemical reaction networks

::DEVELOPER

iVUN Team

:: SCREENSHOTS

iVUN

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • Java

:: DOWNLOAD

 iVUN

:: MORE INFORMATION

Citation

C. Vehlow, J. Hasenauer, A. Kramer, J. Heinrich, N. Radde, F. Allgoewer, and D. Weiskopf.
Uncertainty-aware visual analysis of biochemical reaction networks.
In Proceedings of IEEE Symposium on Biological Data Visualization(Biovis), pages 91–98, 2012.

TSNI / TSNI-integral – Time Series Network Identification

TSNI / TSNI-integral

:: DESCRIPTION

TSNI assumes that the gene network can be modeled by the following system of ordinary differential equation to represent the rate of synthesis of a transcript as a function of the concentrations of every other transcript in a cell and the external perturbation.

TSNI-integral

::DEVELOPER

di Bernardo Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • MatLab

:: DOWNLOAD

  TSNI / TSNI-integral 

:: MORE INFORMATION

Citation

IET Syst Biol. 2007 Sep;1(5):306-12.
Inference of gene networks from temporal gene expression profiles.
Bansal M, di Bernardo D.

MNI – Identify the Gene Targets of a Drug Treatment based on Gene-expression data

MNI

:: DESCRIPTION

The MNI (Mode-of-action by Network Inference ) is an algorithm to identify the gene targets of a drug treatment based on gene-expression data. In a typical use of the algorithm, a single expression profile, say obtained as a result of a treatment under study, is used as the test profile while a set of hundreds of expression profiles is used as the training set. The MNI algorithm uses the large training data set of expression profiles to construct a statistical model of gene-regulatory networks in a cell or tissue. The model describes combinatorial influences of genes on one another.

::DEVELOPER

di Bernardo Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • MatLab

:: DOWNLOAD

 MNI

:: MORE INFORMATION

Citation

Nat Protoc. 2006;1(6):2551-4.
The mode-of-action by network identification (MNI) algorithm: a network biology approach for molecular target identification.
Xing H, Gardner TS.

NIR – Network Inference by Reverse-engineering

NIR

:: DESCRIPTION

In order to estimate the coefficient of the gene interactions NIR solves a linear regression problem for each gene considering a fixed number of k regressors. The regressor set is chosen according the residual sum of square error (RSS) minimization criterion. In this version, NIR exhaustively searches the best regressors in the space of all the possible k-tuples of genes.

::DEVELOPER

di Bernardo Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • MatLab / OCTAVE

:: DOWNLOAD

 NIR

:: MORE INFORMATION

Citation

Pac Symp Biocomput. 2004:486-97.
Robust identification of large genetic networks.
Di Bernardo D, Gardner TS, Collins JJ.

bioSDP 0.3 – Analysis of uncertain Biochemical Networks via Semidefinite Programming

bioSDP 0.3

:: DESCRIPTION

bioSDP is a Matlab Toolbox for the analysis of uncertain biochemical networks via semidefinite programming.

::DEVELOPER

Institute of Computational Biology, German Research Center for Environmental Health (GmbH)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • MatLab

:: DOWNLOAD

 bioSDP

:: MORE INFORMATION

Citation

  • Hasenauer, J.; Waldherr, S.; Wagner, K. & Allgöwer, F.: Parameter Identification, Experimental Design and Model Falsification for Biological Network Models Using Semidefinite Programming. IET Systems Biology 4:119-130, 2010.

D2D – Quantitative Dynamic Modeling of Biochemical processes

D2D

:: DESCRIPTION

D2D (Data 2 Dynamics) is a collection of numerical methods for quantitative dynamic modeling of biochemical processes, which provides reliable and efficient model calibration methods

::DEVELOPER

Institute of Computational Biology, German Research Center for Environmental Health (GmbH)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • MatLab

:: DOWNLOAD

 D2D

:: MORE INFORMATION

Citation

Raue A., et al.
Lessons Learned from Quantitative Dynamical Modeling in Systems Biology.
PLOS ONE, 8(9), e74335, 2013.

jagn 1.02 – Java-Based Model for Artificial Gene Networks Generation

jagn 1.02

:: DESCRIPTION

jagn is a software for an Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data, which can be used by computational methods to recover the network topology, and then, analyse the results based on complex networks measurements/topology.

::DEVELOPER

Fabrício Martins Lopes

:: SCREENSHOTS

jagn

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java

:: DOWNLOAD

  jagn

:: MORE INFORMATION

Citation:

Lopes, Fabrício M.; Cesar-Jr, Roberto M.; Costa, Luciano da F.
Gene expression complex networks: synthesis, identification and analysis.
Journal of Computational Biology, v. 18, p. 1353-1367, 2011.

SLML Tools v1.5.2 – Implement Generative Models of Subcellular Location

SLML Tools v1.5.2

:: DESCRIPTION

SLML tools implements the generative models of subcellular location

::DEVELOPER

Murphy Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 SLML Tools

:: MORE INFORMATION

Citation

T. Zhao and R.F. Murphy. (2007)
Automated learning of generative models for subcellular location: Building blocks for systems biology.
Cytometry 71A:978-990.

MyProteinNet 2 – Build Up-to-date PIN for Organisms, Tissues, Cells Subsets and user-defined contexts

MyProteinNet 2

:: DESCRIPTION

The MyProteinNet web server allows users to eas- ily create such context-sensitive protein interaction networks. Users can automatically gather and con- solidate data from up to 11 different databases to create a generic protein interaction network (inter- actome).

::DEVELOPER

Yeger-Lotem Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Basha O, Flom D, Barshir R, Smoly I, Tirman S, Yeger-Lotem E.
MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts.
Nucleic Acids Res. 2015 Jul 1;43(W1):W258-63. doi: 10.1093/nar/gkv515. Epub 2015 May 18. PMID: 25990735; PMCID: PMC4489290.

MotifNet – Web-server for Network Motif analysis

MotifNet

:: DESCRIPTION

MotifNet allows researchers to analyze integrated networks, where nodes and edges may be labeled, and to search for motifs of up to eight nodes.

::DEVELOPER

Yeger-Lotem Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Smoly IY, Lerman E, Ziv-Ukelson M, Yeger-Lotem E.
MotifNet: a web-server for network motif analysis.
Bioinformatics. 2017 Jun 15;33(12):1907-1909. doi: 10.1093/bioinformatics/btx056. PMID: 28165111.