TT-CME

Tensor Train for the Chemical Master Equation. This repository implements the paper "Tensor-train approximation of the chemical master equation and its application for parameter inference" on top of the torchTT package ( upgraded version of this repository )

Installation

Requirements

• pytorch>=1.7
• numpy>=1.18
• scipy
• torchtt
• opt_einsum
• matplotlib
• numba

Using pip

pip install git+https://github.com/ion-g-ion/tt-cme

Packages

Sub-modules:

• TTCME.TimeIntegrator: Tensor train integrator for linear ODEs in the TT format (implements tAMEn)
• TTCME.basis: Implements the basic univariate bases.
• TTCME.pdf: This contains the basic probability density function pdfTT represented using tensor product basis and TT DoFs.
• TTCME.ttcme: This module implements the ChemicalReaction class as well as the ReactionSystem class.

The documentation can be found here and is generated using pdoc3 with:

pdoc3 --html tt_iga -o docs/ --config latex_math=True --force

Scripts and examples:

In this folder a couple of examples are presented:

• simple_gene.ipynb basic 2d simple gene expression model.
• bistable_toggle_model.ipynb bistable toggle switch model (bimodal solution).
• seir_model.ipynb solving the 4d SEIR model.
• simple_gene_convergence convergence study for the simple gene expression model with no parameter.
• seir_filtering.ipynb filtering and smoothing for the 4d SEIR model.
• simple_gene_param.ipynb parameter dependent simple gene expression model.
• simple_gene_param_inference.ipynb the parameter inference for the simple gene expression model.
• 3stage_param_inference.ipynb the parameter inference for the 3 stage gene expression model.
• SEIQR_param_inference.ipynb the parameter inference for the SEIQR model.

Author

Ion Gabriel Ion, ion.ion.gabriel@gmail.com