Journal: BMC Bioinformatics

Article Title: Odefy -- From discrete to continuous models

doi: 10.1186/1471-2105-11-233

Figure Lengend Snippet: Odefy overview . Odefy generates models from sets of Boolean equations or Boolean hypergraphs created with yEd. Alternatively, Boolean models can be imported from the CellNetAnalyzer, GINsim or the PBN toolbox. Odefy contains a method for the automatic generation of multi-compartment models from a given single cell model. Boolean models can be exported to other discrete input formats (for the GNA and SQUAD toolboxes), used for Boolean simulations and analysis within Odefy, or they can be converted to systems of ordinary differential equation (ODE). These ODE systems can either be directly simulated and analyzed with Odefy or exported to well-established model formats, including MATLAB script files, SBML, SB Toolbox models and R script files.

Article Snippet: For the Odefy import process, we represent these operators by the MATLAB language-specific operators &&, || and ~, respectively.

Techniques:

Journal: BMC Bioinformatics

Article Title: Odefy -- From discrete to continuous models

doi: 10.1186/1471-2105-11-233

Figure Lengend Snippet: Boolean model definition . A The easiest way to define a Boolean model in Odefy is to specify a set of Boolean equations in a text file. This example represents an asymmetric version of the mutual inhibitory switch shown in the results section. Note the use of the MATLAB language-specific operators &&, || and ~. B Regulatory interaction graph created with the yEd graph editor. Regular arrows represent activatory influences whereas diamond-head arrows stand for inhibition. Note that we need to specify a generic logic to combine multiple regulatory inputs for node E . The Odefy default at least one activator and no inhibitors logic would result in E = ( A ∨ C ) ˄ ¬ ( B ∨ C ). C Alternative representation of the Boolean model as a hypergraph. Using a specialized node '&' we can precisely specify the Boolean logic for node E . All edges not incident to a '&' node are treated with an OR logic. The resulting Boolean update rule reads E = ( A ˄ ¬ B ) ∨ C ∨ ¬ D . ˄ = logical AND, ∨ = logical OR, ¬ = logical NOT.

Article Snippet: For the Odefy import process, we represent these operators by the MATLAB language-specific operators &&, || and ~, respectively.

Techniques: Inhibition

Journal: Scientific Reports

Article Title: Efficient microbial colony growth dynamics quantification with ColTapp, an automated image analysis application

doi: 10.1038/s41598-020-72979-4

Figure Lengend Snippet: ColTapp graphical user interface and analysis workflow. ( a ) For visualization, results are overlaid onto the images: typically, the circles representing detected colonies and the edges of the Voronoi tessellation shown here in blue on the image. The main analysis panel, on the left of the image, is separated in three tabs. Here the Detect tab, which allows the user to detect colonies on a plate, is visualized. The Main tab, which allows the user to analyze either time-lapse or endpoint images and the Visualize tab are displayed on the Supplementary Fig. . ( b ) Schematic of simple image acquisition setups including a camera holder. An Arduino board (blue icon) can be used to trigger the camera automatically for time-lapse imaging of a plate ( , Image acquisition, for implementation). ColTapp operates in two modes: either Time-lapse (TL) or Endpoint (EP) mode, depending on input data, illustrated by the two different folders (turquoise and yellow respectively). The turquoise highlighted functionalities are specific to the Time-lapse mode, while the yellow highlighted ones are specific to Endpoint mode. In the middle, the green highlighted functionalities are common to both modes. Note that each step of the workflow (apart from the two which are detailed in the following sections) has a corresponding section in the Supplementary text, which may serve as a guide to the user. For example, the “Analysis set-up” is described in the Supplementary text : a user may define the area of analysis on its images, as shown here in turquoise on the 3 example images. The implementation of the “colony detection” and “radius tracking over time” algorithms are described in the and illustrated here with small subsets of Figs. and respectively. The colony characteristics are illustrated here with a small subset of the Supplementary Fig. .

Article Snippet: We wrote the ColTapp application using the classical programming language MATLAB, and the code is designed as a modular shell that can host further image analysis methods that may meet specific needs, while benefiting from the easy to operate graphical interface.

Techniques: Imaging

Journal: Scientific Reports

Article Title: Efficient microbial colony growth dynamics quantification with ColTapp, an automated image analysis application

doi: 10.1038/s41598-020-72979-4

Figure Lengend Snippet: Comparison with other colony image analysis tools.

Article Snippet: We wrote the ColTapp application using the classical programming language MATLAB, and the code is designed as a modular shell that can host further image analysis methods that may meet specific needs, while benefiting from the easy to operate graphical interface.

Techniques: Comparison