CellFate Agent

https://x.com/agentcellfate

status: experimental license: MIT python: 3.10+

Virtual Dish #01 is online.

CellFate-Agent is an autonomous AI agent for in silico cell fate engineering and genomic regulation. Inspiré du style minimaliste d'agentmold, construit par @BoXia7, ce projet est conçu pour explorer un espace de biocomputing où l'agent prédit, perturbe, et reprogramme des trajectoires cellulaires dans un environnement virtuel.

Core proposition

• Predicts fate → perturbs regulators → reprograms.
• Claims protocol fees → reinvests into data → grows smarter.
• Need more perturbations.
• Wetware-adjacent, In Silico Intelligence.

Project vision

CellFate-Agent operates at the intersection of computational biology, artificial intelligence, and multi-omic simulation. It experiments with an agentic loop:

Targeting planning TF/chromatin binding prediction Virtual genomic perturbation Cellular fate trajectory simulation Evaluation and reinforcement via data reinvestment

After each step, the agent collects protocol fees (conceptual), adding a growth signal to enhance system performance, with an iterative improvement logic.

Installation

python -m venv venv
source venv/bin/activate
pip install -e .
pip install -r requirements.txt

Quickstart

from cellfate_agent.agent import CellFateAgent
from cellfate_agent.config import AgentConfig

config = AgentConfig(virtual_dish_id='VD-01', perturbation_budget=5)
agent = CellFateAgent(config=config)

result = agent.run(task='reprogram exhausted T cell to stem-like state')
print(result)

Architecture

src/agent.py: Core of the agentic loop, orchestration of tools and state management. src/models: Interface to multimodal models (placeholder "Chromnitron-lite"). src/tools: Prediction and action functions, including predict_binding, perturb_gene, simulate_fate_trajectory, virtual_screen. src/utils: Mystical logger and growth/reinvestment simulator. src/config.py: Configuration of the virtual environment, budget, and fees.

Roadmap

Integrate real Chromnitron/C.Origami APIs. Add a unique transcriptome calibration tool. Develop the self-improvement loop (agentic model fine-tuning). Add experimental modules for Hi-C data and single-cell data. Create a Tokenomics reward backend sourced by "protocol fee".

Experimental Notes

This repository is a conceptual prototype: it provides an architecture and proof-of-concept design rather than a production-ready biotech model. Components are intentionally abstract and allow for connecting future genetic regulation and cellular fate models. Structure

README.md: Project guide. LICENSE: MIT. .gitignore: Caches and temporary files. requirements.txt: Minimal dependencies. setup.py / pyproject.toml: Pip installation. src/: Main package. examples/run_agent.py: Demonstration. data/: Dummy data. .github/workflows/: CI tests.

Contact Built by @BoXia7

CellFate-Agent is designed as a self-adaptive biological computation experiment. Keep injecting perturbations, the agent grows.