We can now generate synthetic gene expression data from simple example Boolean networks. We want to use these data to demonstrate how we will use a quasi-potential approach (or some other approach) to infer control kernel nodes.
A good place to start is with data created from the "Human Gonadal Sex Determination" network. Example data are currently saved in the file 250207_example_1_expression_time_t.csv, which originates from the BoolODE data in Bool ODE outputs/250124/output_1000cells_250124_gonadal/sampled_ExpData.csv (see https://github.com/Collective-Logic-Lab/Synthetic-RNA-maps/blob/main/250207_synthetic_data_for_peter.ipynb ). The data are taken from random initial conditions and random times.
See #4 for other possible Boolean network examples that might be useful (e.g. those with few attractors).
We can now generate synthetic gene expression data from simple example Boolean networks. We want to use these data to demonstrate how we will use a quasi-potential approach (or some other approach) to infer control kernel nodes.
A good place to start is with data created from the "Human Gonadal Sex Determination" network. Example data are currently saved in the file
250207_example_1_expression_time_t.csv, which originates from the BoolODE data inBool ODE outputs/250124/output_1000cells_250124_gonadal/sampled_ExpData.csv(see https://github.com/Collective-Logic-Lab/Synthetic-RNA-maps/blob/main/250207_synthetic_data_for_peter.ipynb ). The data are taken from random initial conditions and random times.See #4 for other possible Boolean network examples that might be useful (e.g. those with few attractors).