From 679f07645f8e6d6fc81451464eda06a44dc1dabf Mon Sep 17 00:00:00 2001 From: teddygroves Date: Fri, 16 Aug 2024 16:21:52 +0200 Subject: [PATCH] Add readme --- README.md | 47 ++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 46 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index e8d3a62..e39c980 100644 --- a/README.md +++ b/README.md @@ -1 +1,46 @@ -# enzax +# Enzax + +[![Tests](https://github.com/dtu-qmcm/enzax/actions/workflows/run_tests.yml/badge.svg)](https://github.com/dtu-qmcm/actions/workflows/run_tests.yml) +[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip) +[![Supported Python versions: 3.12 and newer](https://img.shields.io/badge/python->=3.12-blue.svg)](https://www.python.org/) + +Enzax is a library of automatically differentiable equations and solvers for modelling networks of enzyme-catalysed reactions, written in [JAX](https://jax.readthedocs.io/en/latest/). + +Enzax provides straightforward, fast and interoperable access to the gradients of realistic metabolic network models, allowing you to incorporate these models in your MCMC and machine learning algorithms when you want to, for example, predict the effect of down-regulating an enzyme on the yield of a fermentation experiment. + +## Installation + +```sh +pip install enzax +``` + +## Usage + +### Find a kinetic model's steady state + +```python +from enzax.examples import methionine +from enzax.steady_state import solve_steady_state +from jax import numpy as jnp + +guess = jnp.full((5,) 0.01) + +steady_state = solve_steady_state( + methionine.parameters, methionine.unparameterised_model, guess +) +``` + +### Find a steady state's Jacobian with respect to all parameters + +```python +import jax +from enzax.examples import methionine +from enzax.steady_state import solve_steady_state +from jax import numpy as jnp + +guess = jnp.full((5,) 0.01) + +jacobian = jax.jacrev(solve_steady_state)( + methionine.parameters, methionine.unparameterised_model, guess +) +```