About GLMP — Genome Logic Modeling Project

Project Overview

The Genome Logic Modeling Project (GLMP) represents a systematic approach to understanding biological processes as computational programs. By applying programming framework methodologies to biological systems, we reveal the underlying logical architecture that governs cellular decision-making.

Our research has identified a conserved computational pattern across 108 biological processes: the 100:11:6:2 architecture, representing the ratio of sequential conditionals to OR gates to AND gates to NOT gates. This discovery suggests that evolution has optimized biological systems for efficiency, robustness, integration, and control.

108

Biological Processes

7,152

Total Nodes

100:11:6:2

Computational Architecture

Model Organisms

Key Publications

"The 100:11:6:2 Pattern: Evidence for a Conserved Computational Architecture in Prokaryotic and Eukaryotic Core Processes"

Welz, G. (2025) — Comprehensive analysis of ~7,000 computational nodes across 108 biological processes reveals evolution's optimization for efficiency, robustness, integration, and control. This work demonstrates that biological regulatory processes implement computable Boolean logic expressible as formal mathematical formulas.

"The Programming Framework: A Universal Method for Process Analysis Using LLMs and Visual Flowcharts"

Welz, G. (2025) — Introduces the meta-tool underlying GLMP, demonstrating how large language models can systematically convert natural language descriptions of biological processes into structured computational visualizations.

"From Inspiration to AI: Biology as Visual Programming"

Welz, G. (2025) — Explores the conceptual foundation for viewing genomes as computational programs, building upon early insights from 1995 with modern visualization and analysis tools.

Methodology

GLMP employs a standardized eight-category color-coding system to represent biological processes as computational programs:

Green: Environmental Triggers & Input Signals
Amber/Gold: Enzymes & Catalytic Proteins
Dark Sky Blue: Chemical Processing & Reactions
Light Cyan: Intermediate States & Molecules
Yellow 🟡: OR Logic Gates (Decision Points)
Purple 🟣: AND Logic Gates (Integration)
Red 🔴: NOT Logic Gates (Repression/Inhibition)
Black: Final Products & Outputs

Research Impact

Our findings suggest that non-coding DNA may encode logical formulas through the spatial arrangement of regulatory elements, implementing Boolean circuits in genomic sequence. This represents a testable hypothesis that invites experimental validation by the research community.

The complete dataset of 108 processes with comprehensive citations is publicly available through our interactive database, enabling researchers to validate findings, explore computational patterns, and test proposed hypotheses through experimental approaches.

Contact Information

Gary Welz
Principal Investigator, Genome Logic Modeling Project
Department of Mathematics and Computer Science
John Jay College of Criminal Justice, City University of New York (CUNY)
Email: gwelz@jjay.cuny.edu
GitHub: github.com/garywelz
Hugging Face: huggingface.co/garywelz