Skip to main content

Gene Regulatory Interaction Network Simulator - GRiNS

Project description

Gene Regulatory Interaction Network Simulator (GRiNS)

A Python library for simulating gene regulatory networks (GRNs) using parameter-agnostic frameworks like RACIPE and Ising formalism, with GPU acceleration and efficient ODE solving.

Modeling gene regulatory networks (GRNs) is essential for understanding cellular processes, but parameterizing these networks becomes increasingly difficult as they scale. This Python library provides a simulation framework that unifies parameter-agnostic approaches, including RACIPE and Ising formalism, into a single, flexible tool.

Key Features

  • Simulation Frameworks: Supports both ODE-based (RACIPE) and coarse-grained (Ising formalism) methods for studying GRN dynamics.
  • Parameter-Agnostic Modeling: Translates network topology into mathematical models without requiring detailed parameter tuning.
  • Scalable Computation: Uses the Jax ecosystem for GPU acceleration and Diffrax for efficient ODE solving.
  • Data Processing Tools: Provides normalization and discretization functions to standardize simulation outputs for downstream analysis.

Overview of the simulation frameworks in GRiNS. GRiNS includes implementations of Random Circuit Perturbation (RACIPE) for continuous ODE-based modeling and Ising Boolean formalism for discrete-state simulations.

Documentation

You can access the full documentation, including installation instructions, usage examples, and detailed explanations of the simulation frameworks, at MoltenEcdysone09.github.io/GRiNS

Installation

GPU Version Installation (Recommended)

For optimal performance, it is recommended to install the GPU-accelerated version of the library. This version leverages CUDA for faster computations, making it well-suited for large-scale simulations. If you have a compatible NVIDIA GPU (refer to Jax Installation), install the library with:

pip install grins[cuda12]

CPU Version Installation

If you do not have a compatible GPU, you can install the CPU version instead:

pip install grins

Compared to the GPU version, the CPU version will be slower, especially for large simulations.

Citation

Please cite this package if you have used it.

@misc{harlapur2025grinspythonlibrarysimulating,
      title={GRiNS: A Python Library for Simulating Gene Regulatory Network Dynamics}, 
      author={Pradyumna Harlapur and Harshavardhan B V and Mohit Kumar Jolly},
      year={2025},
      eprint={2503.18356},
      archivePrefix={arXiv},
      primaryClass={q-bio.QM},
      url={https://arxiv.org/abs/2503.18356}, 
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

grins-0.1.2.tar.gz (45.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

grins-0.1.2-py3-none-any.whl (49.4 kB view details)

Uploaded Python 3

File details

Details for the file grins-0.1.2.tar.gz.

File metadata

  • Download URL: grins-0.1.2.tar.gz
  • Upload date:
  • Size: 45.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for grins-0.1.2.tar.gz
Algorithm Hash digest
SHA256 75ae7c1f92b587b3c66f19951379043f75c849a239d076ca2fb28991d86e8e3a
MD5 d31940ac9f67a50461deb1f350b05b22
BLAKE2b-256 889fb6bfbb05f9f3d4c0fa4d02a4be8f4611c6d9867f1bc99d5b668eff5e70af

See more details on using hashes here.

Provenance

The following attestation bundles were made for grins-0.1.2.tar.gz:

Publisher: python-publish.yml on MoltenEcdysone09/GRiNS

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file grins-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: grins-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 49.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for grins-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 eef71fd2f160381d35b5da515f6fdcd1998f72bcabcd1d70e6e498f8726f05cd
MD5 f297b43cc58c50379d0b370c823af17f
BLAKE2b-256 51b5f93ce1294723fd1c94e5f36c7da973448b5b31d34529bcd1a75fd713f69f

See more details on using hashes here.

Provenance

The following attestation bundles were made for grins-0.1.2-py3-none-any.whl:

Publisher: python-publish.yml on MoltenEcdysone09/GRiNS

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page