GeneWeaver is an open-source, research-stage computational framework designed to explore, prototype, and simulate concepts in synthetic genomics, population genetics, and molecular computation. The project focuses on building in-silico models that bridge theoretical genome design with evolutionary and environmental constraints, allowing experimentation before any wet-lab validation.
Rather than presenting a finished production system, GeneWeaver serves as a conceptual and experimental platform for investigating how engineered genetic constructs might behave under mutation, selection, environmental stress, and artificial design constraints such as non-canonical base systems.
The framework is written in Python (3.9+), prioritizing clarity, modularity, and extensibility for future research expansion.
GeneWeaver is built around the following research goals:
- Explore synthetic and non-canonical genome representations (e.g., XNA-like abstractions)
- Model stochastic population dynamics under evolutionary pressures
- Investigate molecular-scale logic concepts inspired by DNA strand displacement
- Provide a sandbox for computational genomics experimentation
- Encourage interdisciplinary thinking across biology, computation, and systems design
This project is intended for learning, research exploration, and conceptual prototyping rather than immediate real-world deployment.
GeneWeaver introduces experimental genome representations that extend beyond canonical DNA bases (A, T, C, G). These abstractions are used to explore:
- Information encoding beyond natural biological constraints
- Mutation resistance and error-checking concepts
- Controlled genome transformations in simulations
⚠️ These models are computational abstractions, not claims of real biochemical synthesis.
The framework explores bio-inspired logic systems inspired by DNA strand displacement and molecular reaction cascades. These simulations investigate:
- Logic-gate-like behavior using sequence interactions
- Competition-based decision models (winner-take-all analogs)
- Signal propagation in constrained molecular environments
This work is theoretical and exploratory, aimed at understanding how computation might emerge at the molecular level.
GeneWeaver includes early models for simulating population-level genetic behavior, incorporating:
- Genetic drift
- Mutation
- Selection pressure
- Population bottlenecks and founder effects
These simulations help explore how engineered or abstract genomes evolve over many generations under varying conditions.
Current State: Research & Prototyping
- Python-based simulation scaffolding
- Early population dynamics models
- Experimental genome/XNA abstractions
- Modular code layout for future expansion
- Molecular logic simulations
- Extended genome integrity checks
- Visualization of evolutionary trends
- Expanded validation benchmarks
- Performance optimization
- Improved visualization dashboards
- Formal documentation of mathematical models
GeneWeaver is actively evolving, and many components are intentionally exploratory.
GeneWeaver may be useful for:
- Students exploring computational biology & bioinformatics
- Researchers prototyping genomic simulation ideas
- Learning projects combining biology, math, and programming
- Conceptual groundwork for future synthetic biology research
It is not intended for clinical, industrial, or biosecurity deployment.
The framework draws inspiration from established principles such as:
- Stochastic processes (e.g., Wright–Fisher models)
- Hamming distance and error metrics
- Thermodynamic intuition for molecular interactions
- Monte Carlo simulations
- Basic kinetic modeling concepts
These models are simplified and adapted for computational experimentation rather than biological precision.
📍 See ROADMAP.md for planned research directions and development phases.
git clone https://github.com/cmessin02-cmyk/GeneWeaver.git
cd GeneWeaver
pip install -r requirements.txt
python main.py
📄 License
MIT License
© 2025 — Sounak Chatterjee