Python
GRNLab User Guide
A data generator for gene regulatory networks with Hill-function kinetics. Every dataset comes with its ground-truth signed adjacency and the full kinetic parameter set. GRNLab generates data; it does not infer networks. Install with pip install grnlab; import as import grnlab as grn.
Chapters
01Getting startedInstall, generate a first dataset, and learn the three objects you will handle.02Concepts and conventionsThe equations exactly as coded, the orientation of S, seeds, and every field.03Networks: motifs and random topologiesMotifs with presets, four random topologies, and how to check a network.04Building modelsHow parameters are sampled, how to override them, two-stage kinetics, hand edits.05Simulating expression dataTime grids, solvers, cells, three process-noise models, observation noise.06Perturbations, steady states, and knockout screensKnockouts and overexpression, steady states, DREAM-style screens.07Time-varying stimuliStep, pulse, ramp, sine and custom inputs; exact integration across edges.08FiguresNetwork drawings in pathway notation, expression plots, publication export.09Exporting data and reproducing itCSV and binary layouts, ground-truth companions, exact reproduction.10RecipesSeven complete workflows from benchmark datasets to bifurcation sweeps.11Accuracy and validationUnit tests, the closed-form audit, numerical methods and their limits.12Function referenceEvery function, option, default and error identifier on one page.13Frequently asked questionsShort answers to the questions that come up first.
Where to go first
New users: Getting started, then Concepts and conventions. Looking for one option: the Function reference. Wondering whether the numbers are right: Accuracy and validation. The MATLAB guide has the same chapters.