README.md

iGEM_ParisBettencourt21

The official repository of iGEM Paris Bettencourt team's software tools.

Cell counting

There are two programs dedicated to the cell counting from the GFP images obtained from the experiments.

The first python program(gfpminicell_count.py) involves the following image processing techniques

• cleaning by border stripping
• gray-scaling
• gaussian blurring
• binary thresholding for preparing the processed image

The second program(cell_count.py) involves additional sharpening and thresholding techniques before gaussian blurring in order to capture the full contours of different sized images. So in sequence, the image goes through cleaning -> gray-scaling -> filter-convolved sharpening -> binary thresholding -> gaussian blurring -> final binary thresholding.

For the final counting, a procedure called contour mapping from OpenCV is used and the corresponding contours are counted as the number of cells seen on the processed images.

Example Images

There are two folders dedicated to store images for each of the two programs described above:

• Cells folder consists of sample images containing both mother and minicells and
• Minicells folder has Green Fluorescent Protein (GFP) images of only minicells

Running the program for cell counting

We need a basic python environment and preferably Miniconda or Anaconda as they help keep all the packages modular

• In order to install the required packages for this program, run the following command that uses python3-pip: pip3 install -r requirements.txt

• For minicell counting (from images that has only minicells filtered), put them in the Minicells folder and run the following command python gfpminicell_count.py -i GFPMinicells/3.png

• For cell counting from images that includes both mother and minicells, put them in the Cells folder and run the following command in the main folder containing the .py file python cell_count.py -i Cells/mgfp01.JPG

  Note: Cells/mgfp01.JPG and Minicells/1.png are just sample images from the example folders, they could be replaced by GFP microscopic images with the corresponding relative path to the files

Interpreting results

The program outputs:

• Original border-cropped image: "Cleaned-original image"

• Final processed image on which the contours are mapped and counted: "Contour-ready image"

• The number of cells counted in the GFP image: "cell-count" (Also in the window-caption of the processed image) Example above: cell-count is 18

Another (Mini)cells count example

Image uploader

The image_uploading_bot.py script is dedicated to automated web navigation. It uses the selenium python extensions to

• Upload a set of images from a local folder to the igem servers
• Store the data of the uploaded files for accessible wiki editing

Minicell producing culture models

There are two version of simulation for culture of minicell producing strains:

• Version 1 is an algorithmic approach for low number of cells
• Version 2 is another approach using approximation of partial differential equations

Version 1

On the first version - minicell_bioproduction_model_v1.py, different simulations were implementated according to different assumptions (constant/exponential) of growth of the cell and production rates of mini-cells. The ouputs are graphs of cell-growth and minicell/mothercell counts

Version 2

On the second version - minicell_bioproduction_model_v2.py, a set of differential equations have been used based on growth-fragmentation problem. The ouputs are graphs of cell-growth and minicell/mothercell counting

Simulated example graphs are below: