A Resource for Quantifying Biological Pattern Intricacies

This repository provides source code, data, and analyses supporting the paper "Quantifying the Intricacies of Biological Pattern Formation." The materials cover a comprehensive exploration of pattern complexity in various reaction-diffusion models, including the FitzHugh-Nagumo, Gray-Scott, and gene regulatory network (GRN) models. The files encompass simulations, complexity quantifications, and stability analyses, emphasizing the Diversity of Number of States (DNOS) and Diversity of Pattern Complexity (DPC) measures.

Supplementary Material Overview

This repository contains source code, data, and results supporting the analysis of biological pattern formation across various reaction-diffusion models. The focus is on quantifying complexity using the Diversity of Number of States (DNOS) and Diversity of Pattern Complexity (DPC) measures.

Reaction-Diffusion Simulations

Includes simulations for FitzHugh-Nagumo, Gray-Scott, and general Turing models, providing insights into spatiotemporal pattern formation and stability conditions.

Explores bistability, tristability, and the effects of 2D diffusion and self-interaction in toggle switch and gene regulatory network (GRN) models.

Complexity Analysis

Jupyter notebooks for classifying and quantifying complexity in generated patterns.

Evaluates DNOS and DPC metrics to analyze pattern diversity and formation dynamics.

Stability Analysis

Code for conducting linear stability analysis of Turing models, identifying conditions for pattern formation via eigenvalue analysis.

Notch-Mediated Lateral Inhibition

Simulations and analyses of Notch signaling pathways in Drosophila neurogenesis.

Combines data processing and visualization to explore complexity measures in gene regulatory network models.

Visualization Tools

Scripts and notebooks for visualizing the complete parameter spaces of GRNs, toggle switches, and reaction-diffusion systems.

Generates figures to illustrate complexity metrics and pattern formation dynamics.

Data Organization

Results, data, and code are categorized by specific models and analyses, facilitating reproducibility and exploration of the complexity space in biological systems.