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An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images

Published on: August 31, 2012

MorphoGrad: A MATLAB toolbox for simulating steady-state morphogen gradients under cell-to-cell variability.

Jan A Adelmann¹, Roman Vetter¹, Dagmar Iber¹

¹Department of Biosystems Science and Engineering, ETH Zürich, Schanzenstrasse 44, 4056 Basel, Switzerland; Swiss Institute of Bioinformatics, Schanzenstrasse 44, 4056 Basel, Switzerland.

|June 4, 2026

Summary

This summary is machine-generated.

This study introduces a MATLAB protocol for simulating morphogen gradients in tissues, incorporating cell-to-cell variability. The method enhances in silico modeling of biological patterning precision.

Keywords:

Bioinformatics Biophysics Developmental biology Physics Systems biology

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Area of Science:

Developmental biology
Computational biology
Systems biology

Background:

Accurately modeling morphogen gradients in silico is crucial for understanding tissue patterning.
Biological variability at the cellular level presents a significant challenge for computational models.

Purpose of the Study:

To present a MATLAB-based protocol for simulating steady-state morphogen gradients in 1D and 2D tissues.
To incorporate and analyze cell-to-cell variability in model parameters within these simulations.

Main Methods:

Development of a MATLAB protocol detailing steps for simulation setup.
Configuration of cell-based geometries and reaction-diffusion equations.
Implementation of stochastic parameter variability and analysis of resulting gradients.

Main Results:

Successful simulation of steady-state morphogen gradients in silico.
Quantification of patterning precision under conditions of stochastic parameter variability.
Demonstration of the protocol's applicability to both one- and two-dimensional tissue models.

Conclusions:

The developed protocol provides a robust framework for in silico study of morphogen gradient dynamics.
Accounting for cell-to-cell variability is essential for realistic simulations of tissue patterning.
This tool aids in dissecting the contribution of parameter stochasticity to developmental robustness.