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Welcome to Driven.

Driven is a python package providing methods to analyze omics data using genome-scale models.


Driven provides state-of-the-art methods to analyze omics using constraint-based modeling. New methods will be implemented and all of them are accessible via a friendly API. The experimental data can be easily imported (see Easy data access)

Driven is integrated with several libraries: cobrapy for easy access to Constraint-based Models, ESCHER it allows easy visualization of the results. All of this can be used within jupyter notebooks.

State-of-the-art Methods





Easy data access

Importing data into driven is easy. Driven reads tabular files directly:

Expression Profiles

Expression data can be easily imported from comma separated values (CSV) files:
from driven.datasets.expression_profile import ExpressionProfile
data = ExpressionProfile.from_csv("your_data.csv")
If the data in the file needs to be filtered or processed, driven can import data from pandas DataFrame.
data = ExpressionProfile.from_data_frame(dataframe)

Flux data

It is also very simple to compute a flux distribution using flux measurements, e.g. 13C fluxes. The flux constraints can be imported in two formats: measurements +- error, or upper and lower limits.
from driven.datasets.fluxes import FluxConstraints
# measurements
flux_constraints = FluxConstraints.from_csv("measurments.csv", type="measurements")
# constraints
flux_constraints = FluxConstraints.from_csv("constraints.csv", type="constraints")
More details about pre-processing data sets can be found in Workflows