Tag: MATLAB

CellX 2.12 – MATLAB Image Segmentation Package

CellX 2.12

:: DESCRIPTION

CellX is a tool for segmentation, fluorescence quantification, and tracking of cells on microscopy images.

::DEVELOPER

CSB – Computational Systems Biology Group

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Windows/Linux/MacOsX
  • MatLab

:: DOWNLOAD

 CellX

:: MORE INFORMATION

Citation

Using CellX to quantify intracellular events.
Mayer C, Dimopoulos S, Rudolf F, Stelling J.
Curr Protoc Mol Biol. 2013;Chapter 14:Unit 14.22.. doi: 10.1002/0471142727.mb1422s101.

Bioinformatics. 2014 Sep 15;30(18):2644-51. doi: 10.1093/bioinformatics/btu302. Epub 2014 May 21.
Accurate cell segmentation in microscopy images using membrane patterns.
Dimopoulos S, Mayer CE, Rudolf F, Stelling J.

KymoRod v0.12.0 – Matlab Graphical Interface for the Study of Hypocotyl Growth

KymoRod v0.12.0

:: DESCRIPTION

KymoRod is a method for automated kinematic analysis of rod-shaped plant organs.

::DEVELOPER

IJPB Modeling and digital imaging lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • MatLab

:: DOWNLOAD

 KymoRod

:: MORE INFORMATION

Citation

KymoRod: A method for automated kinematic analysis of rod-shaped plant organs.
Bastien R, Legland D, Martin M, Fregosi L, Peaucelle A, Douady S, Moulia B, Höfte H.
Plant J. 2016 Jun 29. doi: 10.1111/tpj.13255.

TreeVis – A MATLAB-based tool for Tree Visualization

TreeVis

:: DESCRIPTION

TreeVis is a new visualization algorithm to generate clear two-dimensional layouts of complex tree structures.

::DEVELOPER

Peng Qiu

:: SCREENSHOTS

TreeVis

:: REQUIREMENTS

  • Windows
  • Matlab / R package

:: DOWNLOAD

 TreeVis

:: MORE INFORMATION

Citation

Peng Qiu, and Sylvia K. Plevritis,
“TreeVis: A MATLAB-based Tool for Tree Visualization“,
Computer Methods and Programs in Biomedicine, 109(1):75-76, 2013.

DOTcvpSB 2010_E4 – Matlab Toolbox for Dynamic Optimization in Systems Biology

DOTcvpSB 2010_E4

:: DESCRIPTION

DOTcvpSB is a software toolbox which uses the CVP approach for handling continuous and mixed integer DO problems. DOTcvpSB has been successfully applied to several problems in systems biology and bioprocess engineering. The toolbox is written in MATLAB and provides an easy to use environment while maintaining a quite good performance. DOTcvpSB is designed for the Windows operating systems. The toolbox also contains a function for importing SBML models.

::DEVELOPER

(Bio)Process Engineering group, IIM-CSIC

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows
  • Matlab

:: DOWNLOAD

 DOTcvpSB

:: MORE INFORMATION

Citation:

T. Hirmajer, E. Balsa-Canto and J. R. Banga:
DOTcvpSB, a software toolbox for dynamic optimization in systems biology.
BMC Bioinformatics 2009, 10:199

PottersWheel 4.0 – MatLab Toolbox of Mathematical Modeling of Dynamical Systems

PottersWheel 4.0

:: DESCRIPTION

PottersWheel is a MATLAB toolbox for mechanistic mathematical modeling. It allows reaction network or ordinary differential equation (ODE) based modeling.

PottersWheel has been developed to provide an intuitive and yet powerful framework for data-based modeling of dynamical systems like biochemical reaction networks. Its key functionality is multi-experiment fitting, where several experimental data sets from different laboratory conditions are fitted simultaneously in order to improve the estimation of unknown model parameters, to check the validity of a given model, and to discriminate competing model hypotheses. New experiments can be designed interactively. Models are either created text based or using a visual model designer. Dynamically generated and compiled C files provide fast simulation and fitting procedures. Each function can either be accessed using a graphical user interface or via command line, allowing for batch processing within custom Matlab scripts.

::DEVELOPER

TIKANIS GmbH

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows/ Linux / Mac OsX
  • MATLAB

:: DOWNLOAD

PottersWheel

:: MORE INFORMATION

Citation

Mathematical modeling of biochemical systems with PottersWheel.
Maiwald T, Eberhardt O, Blumberg J.
Methods Mol Biol. 2012;880:119-38. doi: 10.1007/978-1-61779-833-7_8.

COBRA Toolbox 3.0.6 / COBRApy 0.17.1 – MATLAB Scripts for Constraint-based Modeling of Metabolic Networks

COBRA Toolbox 3.0.6 / COBRApy 0.17.1

:: DESCRIPTION

COBRA Toolbox (COnstraint-Based Reconstruction and Analysis Toolbox) is a software package running in the MATLAB environment which allows for quantitative prediction of cellular behavior using a constraint-based approach.

COBRApy is a package for constraints-based modeling of biological networks

::DEVELOPER

COBRA team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

COBRA Toolbox , COBRApy

:: MORE INFORMATION

Citation:

Becker, S. et al.,
“Quantitative prediction of cellular metabolism with constraint-based models: The COBRA Toolbox”,
Nat. Protoc 2, 727-738 (2007).

Gro2mat – A package to efficiently read Gromacs output in Matlab

Gro2mat

:: DESCRIPTION

gro2mat is a package that allows fast and easy access to Gromacs output files from Matlab.

::DEVELOPER

Oxford Protein Informatics Group (OPIG)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • MatLab

:: DOWNLOAD

 Gro2mat

:: MORE INFORMATION

Citation

J Comput Chem. 2014 Jul 30;35(20):1528-31. doi: 10.1002/jcc.23650. Epub 2014 Jun 12.
Gro2mat: a package to efficiently read gromacs output in MATLAB.
Dien H1, Deane CM, Knapp B.

DRAGON – Matlab package of DRAGON Clustering approach

DRAGON

:: DESCRIPTION

DRAGON (Divisive hierarchical maximum likelihood clustering) was verified on mutation and microarray data, and was gauged against standard clustering methods in the literature. Its validation included synthetic and significant biological data.

::DEVELOPER

Laboratory for Medical Science Mathematics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Matlab

:: DOWNLOAD

DRAGON

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2017 Dec 28;18(Suppl 16):546. doi: 10.1186/s12859-017-1965-5.
Divisive hierarchical maximum likelihood clustering.
Sharma A, López Y, Tsunoda T.

SIML – Matlab package of SIML Clustering approach

SIML

:: DESCRIPTION

The proposed SIML (Stepwise Iterative Maximum Likelihood) clustering algorithm has been tested on microarray datasets and SNP datasets. It has been compared with a number of clustering algorithms.

::DEVELOPER

Laboratory for Medical Science Mathematics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • Matlab

:: DOWNLOAD

SIML

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2016 Aug 24;17(1):319. doi: 10.1186/s12859-016-1184-5.
Stepwise iterative maximum likelihood clustering approach.
Sharma A, Shigemizu D, Boroevich KA, López Y, Kamatani Y, Kubo M, Tsunoda T