Initial commit: GLMP project structure with paper draft and AI agents

.gitignore

@@ -0,0 +1,83 @@
+# Python virtual environment
+venv/
+env/
+ENV/
+.env
+
+# Python bytecode
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+
+# Distribution / packaging
+dist/
+build/
+*.egg-info/
+
+# Data files
+*.fasta
+*.fastq
+*.bam
+*.sam
+*.vcf
+*.bed
+*.gtf
+*.gff
+*.csv
+*.xlsx
+*.xls
+*.db
+*.sqlite
+
+# API credentials and secrets
+google-credentials.json
+*credentials*.json
+*.pem
+*.key
+
+# Logs
+logs/
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+lerna-debug.log*
+.pnpm-debug.log*
+
+# Diagnostic reports
+report.[0-9]*.[0-9]*.[0-9]*.[0-9]*.json
+
+# Runtime data
+pids
+*.pid
+*.seed
+*.pid.lock
+
+# Coverage directory
+coverage
+*.lcov
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+*~
+
+# OS specific files
+.DS_Store
+Thumbs.db
+.directory
+desktop.ini
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# Cache directories
+.cache/
+__pycache__/
+.pytest_cache/
+.coverage
+htmlcov/ 

README.md

@@ -0,0 +1,44 @@
+# Genome Logic Modeling Project (GLMP)
+
+> A systems biology initiative to model genomes as executable programs using AI.
+
+## 🧬 Project Overview
+The **Genome Logic Modeling Project (GLMP)** aims to represent biological processes as computational logic—modeling genes, operons, regulatory circuits, and organism life cycles as flowcharts, logic gates, and modular programs. Our tools include human insight, large language models (LLMs), and AI-assisted diagram synthesis.
+
+## 🎯 Goals
+- Build structured diagrams for thousands of genetic circuits and full organism programs.
+- Extract and generalize logic patterns from existing biological research.
+- Propose and test new models of how genomes function as dynamic, adaptive systems.
+- Build a reproducible platform where AI agents generate, refine, and review logic diagrams.
+
+## 🧠 What We're Doing Now
+- Modeling viruses as compact genetic programs.
+- Creating AI agents for literature analysis, diagram synthesis, and meta-modeling.
+- Tracing the visual evolution of genetic diagrams from Mendel to modern AI systems biology.
+
+## 📁 Project Structure
+- `paper/` — Markdown drafts, academic diagrams, figure captions.
+- `diagrams/` — Biological flowcharts (historic, 1995, and 2025+).
+- `agents/` — Modular LLM agent scripts for extraction, synthesis, analysis.
+- `datasets/` — Curated references and papers (by organism class).
+- `figures/` — Timeline visuals of genome logic representations.
+- `roadmaps/` — Strategy diagrams and tiered analysis plans.
+
+## 🤝 Authors & Contributors
+- **Gary Welz** — Originator, principal investigator  
+  Retired professor, CUNY (Mathematics & Computer Science)  
+  [gwelz@jjay.cuny.edu](mailto:gwelz@jjay.cuny.edu)
+
+- **ChatGPT-4o** — Co-author and diagram modeling assistant  
+- **Claude Sonnet 3.5** — Contributed flowcharting and conceptual notes  
+- Future reviewers and AI models will be acknowledged
+
+## 📬 Contact & Contribution
+We welcome feedback and collaboration from researchers, developers, and AI enthusiasts.
+
+📫 [gwelz@jjay.cuny.edu](mailto:gwelz@jjay.cuny.edu)  
+🔗 GitHub: [github.com/garywelz](https://github.com/garywelz)  
+🔗 Hugging Face: [huggingface.co/garywelz](https://huggingface.co/garywelz)
+
+## 📖 License
+To be determined — likely MIT (for code) and CC BY (for diagrams and text). 

docs/paper/figures/b-galchart2.gif:Zone.Identifier

@@ -0,0 +1,4 @@
+[ZoneTransfer]
+ZoneId=3
+ReferrerUrl=https://web.archive.org/web/19970310064130/http://landru.unx.com/DD/advisor/docs/jul95/welz.genome0.shtml
+HostUrl=https://web.archive.org/web/19970310064130im_/http://landru.unx.com/DD/advisor/docs/jul95/images/b-galchart2.gif

docs/paper/genome-logic-modeling.md

@@ -0,0 +1,96 @@
+# Is the Genome Like a Computer Program?  
+**Author:** Gary Welz  
+**Date:** April 12, 2025  
+
+---
+
+## Abstract
+This article revisits the metaphor of the genome as a computer program, a concept first proposed publicly by the author in 1995. Drawing on historical discussions in computational biology, including previously unpublished exchanges from the bionet.genome.chromosome newsgroup, we explore how the genome functions not merely as a passive database of genes but as an active, logic‑driven computational system. The genome executes massively parallel processes—driven by environmental inputs, chemical conditions, and internal state—using a computational architecture fundamentally different from conventional computing. From early visual metaphors in Mendelian genetics to contemporary logic circuits in synthetic biology, this paper traces the historical development of computational models that express genomic logic, while critically examining both the utility and limitations of the program metaphor. We conclude that the genome represents a unique computational paradigm that could inform the development of novel computing architectures and artificial intelligence systems.
+
+---
+
+## 1. Introduction
+Biological processes have often been described through metaphor: the cell as a factory, DNA as a blueprint, and most provocatively—the genome as a computer program. Unlike static descriptions, this metaphor opens the door to seeing life itself as computation: a dynamic process with inputs, logic conditions, iterative loops, subroutines, and termination conditions.
+
+In 1995, the author explored this idea in an essay published in *The X Advisor*, proposing that gene regulation could be modeled as a logic program. That same year, in discussions on the bionet.genome.chromosome newsgroup, computational biologists including Robert Robbins of Johns Hopkins University developed this metaphor further, exploring profound differences between genomic and conventional computation. This article revisits and expands that vision through both historical analysis and modern advances in biology and AI.
+
+---
+
+## 2. Historical Context
+
+### 2.1 Early Visualizations of Biological Logic  
+The visualization of biological logic began with Gregor Mendel in the 19th century. Though his work predates formal computational thinking, Mendel's charts—showing ratios of inherited traits—used symbolic logic to track biological outcomes. Later, chromosome theory and operon models introduced control diagrams that represented genetic regulatory mechanisms.
+
+### 2.2 The Development of Computational Metaphors  
+In the 1960s, François Jacob and Jacques Monod's lac operon model introduced a logic gate–like system for regulating gene expression, paving the way for computational thinking in molecular biology. This early model showed how gene expression could be controlled through what resembled conditional logic.
+
+### 2.3 The 1995 Bionet.Genome.Chromosome Discussions  
+In April 1995, a significant exchange on the bionet.genome.chromosome newsgroup explored the genome‑as‑program metaphor in depth. The author initiated this discussion by asking whether "an organism's genome can be regarded as a computer program" and whether its structure could be represented as "a flowchart with genes as objects connected by logical terms."
+
+Robert Robbins of Johns Hopkins University responded with a comprehensive analysis that both supported and complicated the metaphor. While acknowledging the digital nature of the genetic code, Robbins highlighted that the genome functions more like "a mass storage device" with properties not shared by electronic counterparts, and that genomic programs operate with unprecedented levels of parallelism—"in excess of 10¹⁸ parallel processes" in the human body.
+
+---
+
+### 2.4 The Author's 1995 Essay and Flowchart Model
+
+<div align="center">
+  ![Figure 1: β-Galactosidase Regulation Flowchart (1995)](/mnt/data/b-galchart2.gif)
+  
+  **Figure 1:** Original 1995 flowchart modeling the lac operon's β‑galactosidase regulation as a decision‑tree program. Decision diamonds are conditional checks (lactose, glucose), rectangles are processes, and dashed lines are feedback loops.
+</div>
+
+This original flowchart depicted the lac operon as a decision tree with conditional branches, feedback loops, and termination conditions—showing how the presence or absence of lactose and glucose created logical pathways leading to different outcomes for β-galactosidase production.
+
+---
+
+### 2.5 Mendel's Punnett Squares
+
+<div align="center">
+  ![Figure 2: Gregor Mendel's Punnett Square for Monohybrid Cross](cf4c2c90-6ece-4d03-a98c-8dfaecfd4000.png)  
+  **Figure 2:** Annotated Punnett square illustrating Mendel's 3:1 phenotypic ratio in monohybrid crosses. (Source: Wikipedia)
+</div>
+
+Mendel's simple grid laid the groundwork for visualizing inheritance as predictable combinations—an early form of "genetic logic."
+
+---
+
+## 3. Modern and Intermediate Visualizations
+
+### 3.1 Color‑Vision Genetics (Elmer et al., 2021)
+
+<div align="center">
+  ![Figure 3: Color‑Vision Genotypes (Elmer 2021)](a1f6505c-2c6a-4598-b5f7-0506f9a41390.png)  
+  **Figure 3:** Six‑grid representation of inheritance patterns underlying color‑vision deficiencies. (Adapted from Jacobs & Elmer 2021)
+</div>
+
+This contemporary example shows how Punnett‑style grids can be extended to complex traits like X‑linked color blindness.
+
+### 3.2 Jacobs & Elmer (2021) Findings
+
+<div align="center">
+  ![Figure 4: Major Findings of Jacobs & Elmer (2021)](590b8e73-dd47-4125-aa28-d5f0e54d9e83.png)  
+  **Figure 4:** Schematic of differentially expressed/spliced genes and PCA plots illustrating genotype clusters, from Jacobs & Elmer, *Front. Genet.* 2021.
+</div>
+
+This figure integrates gene‐ontology categories, simple flowcharts for genotype cases, and PCA‐based clustering—showing how modern genomics combines logic diagrams with high‑dimensional data visualizations.
+
+---
+
+## 4. The Genome as a Mass Storage Device
+*(…sections 4–12 unchanged; see full draft above…)*
+
+---
+
+## 5. AI‑Driven Roadmap (Claude 3.5)
+
+<div align="center">
+  ![Figure 5: AI‑Generated Genome Logic Modeling Roadmap](d9a87136-e997-4f65-9f37-7bd3872f6898.png)  
+  **Figure 5:** A five‑phase research roadmap generated by Claude 3.5, showing Literature Review, Flowchart Creation, Pattern Analysis, AI Hypothesis Testing, and Experimental Testing loops.
+</div>
+
+---
+
+## References
+*(…as listed in the full draft…)*
+
+--- 

docs/paper/glmp_draft.md

@@ -0,0 +1,4 @@
+# Is the Genome Like a Computer Program?
+*(Full Markdown draft for GLMP article, including figures and captions)*
+
+... [Full content as in the latest draft] ...

docs/paper/preview.html

@@ -0,0 +1 @@
+<!DOCTYPE html><html><head><title>GLMP Paper</title><style>body{font-family:Arial,sans-serif;max-width:800px;margin:0 auto;padding:20px;line-height:1.6;}h1{color:#2c3e50;}h2{color:#34495e;}h3{color:#7f8c8d;}code{background:#f8f9fa;padding:2px 4px;border-radius:3px;}</style></head><body>

requirements.txt

@@ -0,0 +1,26 @@
+# Core data science packages
+numpy>=1.24.0
+pandas>=2.0.0
+scipy>=1.10.0
+
+# Machine learning
+scikit-learn>=1.2.0
+tensorflow>=2.12.0
+torch>=2.0.0
+
+# Data visualization
+matplotlib>=3.7.0
+seaborn>=0.12.0
+plotly>=5.13.0
+
+# Bioinformatics
+biopython>=1.81
+pysam>=0.19.0
+bedtools>=0.1.0
+
+# Utilities
+tqdm>=4.65.0
+python-dotenv>=1.0.0
+pytest>=7.3.1
+black>=23.3.0
+flake8>=6.0.0 

src/models/agents/__init__.py

@@ -0,0 +1,15 @@
+"""
+AI Agents for Genome Logic Modeling Project
+"""
+
+from .extractor_ai import ExtractorAI
+from .diagram_synthesizer_ai import DiagramSynthesizerAI
+from .pattern_recognizer_ai import PatternRecognizerAI
+from .paper_writer_ai import PaperWriterAI
+
+__all__ = [
+    'ExtractorAI',
+    'DiagramSynthesizerAI',
+    'PatternRecognizerAI',
+    'PaperWriterAI'
+] 

src/models/agents/agent_templates.md

@@ -0,0 +1,19 @@
+# AI Agent Templates for GLMP
+
+## 1. Extractor AI
+- Task: Read papers, extract logic from text.
+
+## 2. Diagram Synthesizer AI
+- Task: Convert logic into standardized flowcharts.
+
+## 3. Pattern Recognizer AI
+- Task: Identify recurring logic motifs.
+
+## 4. Meta-Modeler AI
+- Task: Generalize patterns into system-wide theories.
+
+## 5. Critic AI
+- Task: Evaluate and suggest improvements to models.
+
+## 6. Experiment Prescriber AI
+- Task: Propose testable experiments for logic models. 

src/models/agents/diagram_synthesizer_ai.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+"""
+Diagram Synthesizer AI Agent for Genome Logic Modeling Project
+Responsible for generating and synthesizing diagrams from genomic logic patterns
+"""
+
+import logging
+from typing import Dict, List, Optional
+import networkx as nx
+import matplotlib.pyplot as plt
+import svgwrite
+
+class DiagramSynthesizerAI:
+    def __init__(self, config: Optional[Dict] = None):
+        """Initialize the Diagram Synthesizer AI agent."""
+        self.config = config or {}
+        self.logger = logging.getLogger(__name__)
+        
+    def create_logic_diagram(self, data: Dict) -> nx.DiGraph:
+        """Create a directed graph representing genomic logic."""
+        self.logger.info("Creating logic diagram")
+        # Implementation here
+        pass
+        
+    def generate_svg(self, graph: nx.DiGraph, output_path: str):
+        """Generate SVG diagram from the logic graph."""
+        self.logger.info(f"Generating SVG diagram at {output_path}")
+        # Implementation here
+        pass
+        
+    def optimize_layout(self, graph: nx.DiGraph) -> Dict:
+        """Optimize the layout of the diagram."""
+        self.logger.info("Optimizing diagram layout")
+        # Implementation here
+        pass
+
+if __name__ == "__main__":
+    # Set up logging
+    logging.basicConfig(level=logging.INFO)
+    
+    # Initialize and test the agent
+    synthesizer = DiagramSynthesizerAI()
+    # Add test code here 

src/models/agents/extractor_ai.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+"""
+Extractor AI Agent for Genome Logic Modeling Project
+Responsible for extracting and preprocessing genomic data and logic patterns
+"""
+
+import logging
+from typing import Dict, List, Optional
+import pandas as pd
+import numpy as np
+
+class ExtractorAI:
+    def __init__(self, config: Optional[Dict] = None):
+        """Initialize the Extractor AI agent."""
+        self.config = config or {}
+        self.logger = logging.getLogger(__name__)
+        
+    def extract_genomic_data(self, source: str) -> pd.DataFrame:
+        """Extract genomic data from various sources."""
+        self.logger.info(f"Extracting genomic data from {source}")
+        # Implementation here
+        pass
+        
+    def preprocess_data(self, data: pd.DataFrame) -> pd.DataFrame:
+        """Preprocess the extracted data."""
+        self.logger.info("Preprocessing genomic data")
+        # Implementation here
+        pass
+        
+    def validate_data(self, data: pd.DataFrame) -> bool:
+        """Validate the extracted and preprocessed data."""
+        self.logger.info("Validating genomic data")
+        # Implementation here
+        pass
+
+if __name__ == "__main__":
+    # Set up logging
+    logging.basicConfig(level=logging.INFO)
+    
+    # Initialize and test the agent
+    extractor = ExtractorAI()
+    # Add test code here 

src/models/agents/paper_writer_ai.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+"""
+Paper Writer AI Agent for Genome Logic Modeling Project
+Responsible for generating and editing academic paper content
+"""
+
+import logging
+from typing import Dict, List, Optional
+import json
+from pathlib import Path
+
+class PaperWriterAI:
+    def __init__(self, config: Optional[Dict] = None):
+        """Initialize the Paper Writer AI agent."""
+        self.config = config or {}
+        self.logger = logging.getLogger(__name__)
+        
+    def generate_section(self, section_type: str, data: Dict) -> str:
+        """Generate content for a specific paper section."""
+        self.logger.info(f"Generating {section_type} section")
+        # Implementation here
+        pass
+        
+    def edit_section(self, section: str, edits: Dict) -> str:
+        """Edit existing paper section based on feedback."""
+        self.logger.info("Editing paper section")
+        # Implementation here
+        pass
+        
+    def format_references(self, references: List[Dict]) -> str:
+        """Format references in the required citation style."""
+        self.logger.info("Formatting references")
+        # Implementation here
+        pass
+
+if __name__ == "__main__":
+    # Set up logging
+    logging.basicConfig(level=logging.INFO)
+    
+    # Initialize and test the agent
+    writer = PaperWriterAI()
+    # Add test code here 

src/models/agents/pattern_recognizer_ai.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+"""
+Pattern Recognizer AI Agent for Genome Logic Modeling Project
+Responsible for identifying and analyzing patterns in genomic logic
+"""
+
+import logging
+from typing import Dict, List, Optional
+import numpy as np
+from sklearn.cluster import DBSCAN
+from sklearn.preprocessing import StandardScaler
+
+class PatternRecognizerAI:
+    def __init__(self, config: Optional[Dict] = None):
+        """Initialize the Pattern Recognizer AI agent."""
+        self.config = config or {}
+        self.logger = logging.getLogger(__name__)
+        
+    def identify_patterns(self, data: np.ndarray) -> List[Dict]:
+        """Identify patterns in genomic data."""
+        self.logger.info("Identifying patterns in genomic data")
+        # Implementation here
+        pass
+        
+    def analyze_pattern_significance(self, patterns: List[Dict]) -> List[Dict]:
+        """Analyze the statistical significance of identified patterns."""
+        self.logger.info("Analyzing pattern significance")
+        # Implementation here
+        pass
+        
+    def cluster_patterns(self, patterns: List[Dict]) -> Dict:
+        """Cluster similar patterns together."""
+        self.logger.info("Clustering patterns")
+        # Implementation here
+        pass
+
+if __name__ == "__main__":
+    # Set up logging
+    logging.basicConfig(level=logging.INFO)
+    
+    # Initialize and test the agent
+    recognizer = PatternRecognizerAI()
+    # Add test code here