Towards the construction of a virtual yeast

Area of Science:

• Computational biology
• Synthetic biology
• Artificial intelligence in life sciences

Background:

• Simulating complex eukaryotic cellular behaviors computationally is a significant challenge.
• Saccharomyces cerevisiae offers a tractable and data-rich model for cellular studies.

Purpose of the Study:

• To create an AI-driven virtual yeast model for simulating eukaryotic cellular behaviors.
• To integrate multimodal biological data, mechanistic reasoning, and active experimentation for advanced cellular simulations.

Main Methods:

• Decomposition of cellular complexity into eight function-centered modules, each as a domain-specific AI tool.
• Orchestration of AI tools using a large language model (LLM).
• Integration of mechanistic knowledge, subcellular architecture, and dynamic states within a closed-loop learning pipeline using representation and generative learning.

Main Results:

• The virtual yeast autonomously designs and executes experiments.
• The platform optimizes biosynthetic pathways and generates/prioritizes hypotheses.
• Accelerated target discovery through AI-driven biological simulation.

Conclusions:

• The virtual yeast provides a generalizable blueprint for constructing virtual eukaryotic cells.
• This approach couples biological realism with AI reasoning to advance synthetic biology.
• The platform facilitates optimization, hypothesis generation, and target discovery in cellular research.