macrogenomics

The python codes used in the study of macrogenomics engineering. This project is focused on implementing the chromatin-packing macromolecular-crowding (CPMC) model developed in the wrok done by Almassalha elt al. 2017 Nature biomedical engineering.

Installation

Download the whole folder into local direction. Run mcExpressionOut.py to generate the required input files calculated by Monte Carlo simulation and Brownian Dynamics simulation for CPMC). For example, type:

python mcExpressionOut.py

in the terminal for linux machine.

Description of each Code

1. mcExpressionOut.py: Output the result from Monte Carlo simulation and Brownian Dynamics simulation.

2. macrogenomics.py: The class required to do any chromatin packing macromolecular crowding model calculation

3. CP_MC_Model.py: Calculate the model predicted gene expression sensitivity from chromatin packing macromolecular crowding model and the sensitivity measured from microarray experiment.

   Requirement: Need the macrogenomics.py, full_genes.xlsx and the output from mcExpressionOut.py

4. percentVariance.py: Calculate the variance expression by the model in experimental data.

   Requirement: Need the macrogenomics.py, full_genes.xlsx and the output from mcExpressionOut.py

5. heterogeneity.py: Calculate the model predicted intercellular gene expression heterogeneity.

   Requirement: Need the macrogenomics.py, full_genes.xlsx and the output from mcExpressionOut.py

6. cov.py: Calculate the model predicted intercellular coefficient of expression variation.

   Requirement: Need the macrogenomics.py, full_genes.xlsx and the output from mcExpressionOut.py

Input data:

full_genes.xlsx: the microarray measurement result

There are 4 groups of microarray measurements in total. Each group contains 4 replicated measurements. In the full_genes.xlsx file, the first columns correspond to the control measurement and 5-8, 9-12 and 13-16 columns correspond to the treated groups. The relative sigma values for them are: 1.001, 1, 0.9933 and 0.9151

Output data:

1. mcExpressionOut.py:

tot_con.csv, the array of molecular regulation condition (the initial concentration of transcription factor and others)

max_mRNA_initial.csv, the initial steady state maximum mRNA concentration under each molecular regulator condition

phi_initial.csv

second_derivative_TF_norm.csv

2. CP_MC_Model.py: sensitivity_model.csv, sensitivity_model_g.csv, sensitivity_experiment.csv, g_function.csv

3. percentVariance.py: percentOfVariance.csv

4. heterogeneity.py: heterogeneity_model.csv, heterogeneity_experiment.csv

5. cov.py: cov_model.csv, cov_experiment.csv

Test:

In the terminal, type:

python mcExpressionOut.py

python CP_MC_Model.py

these two commend will output: max_mRNA_initial.csv, phi_initial.csv, second_derivative_TF_norm.csv, tot_con.csv, sensitivity_model.csv, sensitivity_model_g.csv, sensitivity_experiment.csv, g_function.csv