Build a Multi-Agent AI Workflow for Biological Network Modeling, Protein Interactions, Metabolism, and Cell Signaling Simulation

The provided code snippet outlines a comprehensive computational systems biology pipeline that integrates multiple AI agents to analyze different aspects of biological systems. The goal is to generate insights into gene regulation, protein interactions, metabolic pathways optimization, and cell signaling dynamics. Here's an overview of the key components:

1. Gene Regulatory Network Analysis

• Agent: GeneRegulatoryNetworkAgent
• Functionality:
  • Generates a synthetic gene regulatory network with specified parameters.
  • Simulates gene expression over time based on this network.
  • Analyzes the resulting data to identify key features such as peak activities, interaction strengths, and overall network topology.

2. Protein Interaction Prediction

• Agent: ProteinInteractionPredictionAgent
• Functionality:
  • Generates synthetic protein-protein interaction (PPI) datasets.
  • Trains a logistic regression model to predict PPI probabilities based on feature engineering of the proteins' properties and interactions.
  • Evaluates model performance using ROC AUC and average precision scores.

3. Metabolic Pathway Optimization

• Agent: MetabolicOptimizationAgent
• Functionality:
  • Optimizes metabolic fluxes under constraints such

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