CAIN: Concordia Artificial Immune Network for B-cell Selection

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Disclosure

This repo is a work in progress and results from this algorithm were not validated on ground-truth data.

Purpose

CAIN simulates B-cell selection and an immune response following the introduction of a foreign pathogenic antigen. This algorithm is only applicable to antigens with linear epitope binding sites. B-cell selection occurs via amino acid substitution likelihoods according to the Point Accepted Mutation (PAM) matrix. The PAM1 and PAM250 matrices text file are provided. If you wish to use different versions, please place the files in the Resources directory and alter the code chunk in the Bcell_Selection.py file.

Following B-cell selection, an immune response is performed using game-theory concepts to run a 1x1 matrix game between each individual of the lymphocyte and antigen populations. The "loser" of each game loses an individual from their respective population and visa versa for the "winner".

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Dependencies

Please install these module versions prior to using CAIN.

• Python 3.0+
• Numpy 1.21.4
• Pandas 1.3.4
• tqdm 4.62.3

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Installation

To clone the repository, open the command terminal and set your working directory.

Next, run the following command:

$ git clone https://github.com/phicks22/CAIN

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Selection and Response

First: Set your root directory in the Arg_Parser.py file at the very top. Next, run the following command in the terminal, setting each argument per the desired parameters.

$ python Main.py -e <amino_acid_sequence> -n 100 -d 2 -p 4 -ex 1000 -r 10

Required arguments:

• -e: Antigen linear epitope. Example: ARIKDDCGHAI
• -n: Number of individuals in each B-cell population
• -d: Antigen division rate
• -p: Number of B-cell populations
• -ex: Number of exchange iterations that occur during B-cell selection
• -r: Number of immune response iterations

Running Main.py initiates the B-cell selection algorithm and immune response simultaneously.

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Unsupervised Parameter Clustering

1. To collect your data, uncomment the block section For Large Data Collection and set the disired ranges of your hyper-parameters. Run the model which will output an .npz file with kwds="data".
2. Load the data into Parameter_Clustering.py and choose the desired clustering method.