Category: System Biology & Modeling

RocketBugs / RocketBugsRender – Simulation of the Actin-based Motility of Listeria Monocytogenes

RocketBugs / RocketBugsRender

:: DESCRIPTION

RocketBugs is a simulation of the actin-based motility of Listeria monocytogenes.

RocketBugsRender is a special rendering and graphing program for the simulation output of “RocketBugs”, the actin-based Listeria motility simulations

::DEVELOPER

The Center for Cell Dynamics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java 

:: DOWNLOAD

RocketBugs  ,RocketBugsRender

:: MORE INFORMATION

Citation

Susanne M. Rafelski, Jonathan B. Alberts, Garrett M. Odell
An Experimental and Computational Study of the Effect of ActA Polarity on the Speed of Listeria monocytogenes Actin-based Motility
PLoS Comput Biol 5(7): e1000434. doi:10.1371/journal.pcbi.1000434

ParMSpindle 1.0 – Simulation of the ParM mediated Segregation of Bacterial Plasmids

ParMSpindle 1.0

:: DESCRIPTION

ParMSpindle is a 3D, force-based simulation of the ParM mediated segregation of bacterial plasmids, tracking the biochemical state of each monomer with experimentally determined filament dynamics

::DEVELOPER

The Center for Cell Dynamics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows / MacOsX
  • Java 

:: DOWNLOAD

   ParMSpindle

:: MORE INFORMATION

Odefy 1.20 – From Discrete to Continuous Models

Odefy 1.20

:: DESCRIPTION

Odefy is a MATLAB- and Octave-compatible toolbox for the automated transformation of Boolean models into systems of ordinary differential equations.

::DEVELOPER

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

:: SCREENSHOTS

Odefy

:: REQUIREMENTS

  • Windows / MacOsX/ Linux
  • Matlab / Octave

:: DOWNLOAD

 Odefy

:: MORE INFORMATION

Citation

Krumsiek J, Poelsterl S, Wittmann DM, Theis FJ.
Odefy – From discrete to continuous models.
BMC Bioinformatics. 2010, 11:233.

simbTUM 3.0a – Simulation of Stochastic processes and ODE models in Biology

simbTUM 3.0a

:: DESCRIPTION

simbTUM allows to define ODE-models or stochastic compartemental models of biological processes, to simulate, to fit data and to do some statistics (fitting and statistics for deterministic models only).

::DEVELOPER

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

:: SCREENSHOTS

simbTUM

:: REQUIREMENTS

  • Windows 

:: DOWNLOAD

 simbTUM

:: MORE INFORMATION

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.