# Protocol for analyzing emergence dynamics of diabetes with obesity using numerical continuation and bifurcation analysis

## TL;DR

This paper provides a protocol to study how diabetes with obesity develops slowly over time using mathematical modeling and software tools.

## Contribution

A new protocol is introduced for analyzing diabetes progression using numerical continuation and bifurcation analysis.

## Key findings

- The protocol uses software to analyze differences in metabolic variable timescales.
- It identifies thresholds separating healthy and diabetic states through bi-stable dynamics.

## Abstract

Type 2 diabetes (T2D) is a multifactorial disease that slowly and inconspicuously progresses over years. Here, we present a protocol for analyzing slow progression dynamics of T2D with obesity. We describe steps for using software to exploit the differences between the timescales of the metabolic variables and using numerical continuation and bifurcation analysis. We detail procedures to analyze bi-stable system dynamics and identify the thresholds that separate healthy and diabetic states.

For complete details on the use and execution of this protocol, please refer to Yildirim et al. (2023).1

•Protocol analyzes emergence dynamics of diabetes with obesity•Steps described to perform numerical continuation and bifurcation analysis•Guidance on using software packages to translate XPPAUT models into Python

Protocol analyzes emergence dynamics of diabetes with obesity

Steps described to perform numerical continuation and bifurcation analysis

Guidance on using software packages to translate XPPAUT models into Python

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Type 2 diabetes (T2D) is a multifactorial disease that slowly and inconspicuously progresses over years. Here, we present a protocol for analyzing slow progression dynamics of T2D with obesity. We describe steps for using software to exploit the differences between the timescales of the metabolic variables and using numerical continuation and bifurcation analysis. We detail procedures to analyze bi-stable system dynamics and identify the thresholds that separate healthy and diabetic states.