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Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
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GRiNS: a python library for simulating gene regulatory network dynamics.

Pradyumna Harlapur1, Harshavardhan Bv2, Mohit Kumar Jolly3

  • 1Department of Bioengineering, Indian Institute of Science, Bengaluru, Karnataka, 560012, India.

BMC Bioinformatics
|October 17, 2025
PubMed
Summary
This summary is machine-generated.

Gene Regulatory Interaction Network Simulator (GRiNS) is a new Python library for efficient, scalable gene regulatory network modeling. It simplifies complex simulations, enabling better understanding of cellular processes.

Keywords:
Boolean Ising formalismGene regulatory networksNetwork dynamicsParameter-agnostic simulationRandom circuit perturbation (RACIPE)Systems biology

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Gene regulatory networks (GRNs) control cellular processes, but their complexity challenges computational modeling.
  • Parameterizing large-scale GRN models is computationally intensive and difficult.

Purpose of the Study:

  • To introduce Gene Regulatory Interaction Network Simulator (GRiNS), a novel Python library for parameter-agnostic GRN modeling.
  • To provide a scalable and efficient tool for simulating GRN dynamics.

Main Methods:

  • GRiNS integrates RACIPE (ordinary differential equations) and Boolean Ising formalism.
  • The library leverages GPU acceleration for enhanced simulation performance.
  • GRiNS offers modular design for customizable parameter selection, initial conditions, and time-series outputs.

Main Results:

  • GRiNS enables parameter-agnostic simulations of GRNs, accommodating varying network sizes.
  • It provides a scalable alternative for large networks using Boolean Ising formalism, reducing computational cost.
  • The library enhances accuracy and customizability for ODE-based simulations via RACIPE.

Conclusions:

  • GRiNS facilitates the study of dynamic and steady-state behaviors in GRNs efficiently and scalably.
  • The tool supports parameter-agnostic modeling approaches for complex biological systems.
  • Accessible documentation and installation guides are available online.