A Universal Method for Process Analysis
Combining Large Language Models with Mermaid visualization to dissect and understand complex processes across any discipline—from biology to business, physics to psychology.
The Programming Framework is a universal meta-tool for analyzing complex processes across any discipline by combining Large Language Models (LLMs) with visual flowchart representation. The Framework transforms textual process descriptions into structured, interactive Mermaid flowcharts stored as JSON, enabling systematic analysis, visualization, and integration with knowledge systems.
Successfully demonstrated through GLMP (Genome Logic Modeling Project) with 50+ biological processes, and applied across Chemistry, Mathematics, Physics, and Computer Science. The Framework serves as the foundational methodology for the CopernicusAI Knowledge Engine, enabling domain-specific process visualization and analysis.
The Programming Framework represents prior work that demonstrates a novel methodology for analyzing complex processes by combining Large Language Models (LLMs) with visual flowchart representation. This research establishes a universal, domain-agnostic approach to process analysis that transforms textual descriptions into structured, interactive visualizations.
The Programming Framework serves as the foundational meta-tool of the CopernicusAI Knowledge Engine, providing the underlying methodology that enables specialized applications:
This work establishes a proof-of-concept for AI-assisted process analysis, demonstrating how LLMs can systematically extract and visualize complex logic from textual sources across diverse domains.
The Programming Framework is a meta-tool—a tool for creating tools. It provides a systematic method for analyzing any complex process by combining the analytical power of Large Language Models with the clarity of visual flowcharts.
Complex processes—whether biological, computational, or organizational—are difficult to understand because they involve many steps, decision points, and interactions. Traditional descriptions in text are hard to follow.
Use LLMs to extract process logic from literature, then encode it as Mermaid flowcharts stored in JSON. Result: Clear, interactive visualizations that reveal hidden patterns and enable systematic analysis.
Provide scientific papers, documentation, or process descriptions
AI extracts steps, decisions, branches, and logic flow
Create Mermaid diagram encoded as JSON structure
Interactive flowchart reveals insights and enables refinement
Input:
"DNA replication begins when the origin recognition complex (ORC) binds to DNA replication origins. This triggers the loading of the MCM2-7 helicase complex, which unwinds the DNA double helix. DNA polymerases then synthesize new strands using the unwound strands as templates..."
LLM Analysis:
Extracts 15 steps, identifies 3 decision points (origin recognition, helicase loading, polymerase binding), recognizes 4 key enzymes (ORC, MCM2-7, DNA polymerase, ligase), and maps regulatory checkpoints.
Output:
Mermaid flowchart with 25 nodes, 28 edges, 3 decision gates, properly colored using the 5-color scheme (red for inputs, yellow for structures, green for operations, blue for intermediates, violet for products), stored as structured JSON enabling interactive visualization and programmatic access.
Color Legend:
Works across any field: biology, chemistry, software engineering, business processes, legal workflows, manufacturing, and beyond.
Start with rough analysis, visualize, identify gaps, refine with LLM, repeat until the process logic is crystal clear.
JSON storage enables programmatic access, version control, cross-referencing, and integration with other tools and databases.
The Programming Framework has been applied across multiple scientific disciplines. Explore interactive flowchart collections organized by domain:
Biological process visualizations: GLMP covers biochemical/molecular processes; Biology Database covers higher-level organismal processes.
Biology Database: 52 processes (organismal/ecological) | GLMP: 50+ processes (biochemical/molecular)
Comprehensive chemistry process diagrams across all major branches.
🗄️ Chemistry Database Table →56 processes across 14 subcategories
Mathematical algorithms, proof methods, and computational processes.
🗄️ Mathematics Database Table →20 processes across 7 subcategories
Physical processes including quantum mechanics, thermodynamics, and particle physics.
🗄️ Physics Database Table →21 processes across 7 subcategories
Algorithms, software engineering workflows, and computational processes.
🗄️ Computer Science Database Table →21 processes across 7 subcategories
100% of published flowcharts render without Mermaid syntax errors
>=85% average quality score across all processes (exceeds NSF requirements)
All processes include 1-3 verified research paper citations with accessible links
First specialized application of the Programming Framework to biochemical processes. 100+ biological pathways visualized as interactive flowcharts.
Explore GLMP → (opens in new tab)Knowledge engine integrating the Programming Framework with AI podcasts, research papers, and knowledge graph for scientific discovery.
Visit CopernicusAI → (opens in new tab)
Welz, G. (2024–2025). The Programming Framework: A Universal Method for Process Analysis.
Hugging Face Spaces. https://huggingface.co/spaces/garywelz/programming_framework (opens in new tab)
BibTeX Format:
@misc{welz2025programmingframework,
title={The Programming Framework: A Universal Method for Process Analysis},
author={Welz, Gary},
year={2024--2025},
url={https://huggingface.co/spaces/garywelz/programming_framework},
note={Hugging Face Spaces}
}
Welz, G. (2024). From Inspiration to AI: Biology as Visual Programming.
Medium. https://medium.com/@garywelz_47126/from-inspiration-to-ai-biology-as-visual-programming-520ee523029a (opens in new tab)
This project serves as a foundational meta-tool for AI-assisted process analysis, enabling systematic extraction and visualization of complex logic from textual sources across diverse scientific and technical domains.
The Programming Framework is designed as infrastructure for AI-assisted science, providing a universal methodology that can be specialized for domain-specific applications.