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Related Experiment Video

Updated: Jul 27, 2025

Understanding Cerebellar Pattern Formation

Understanding Cerebellar Pattern Formation

Published on: November 1, 2007

Data driven modeling of pseudopalisade pattern formation.

Sandesh Athni Hiremath¹, Christina Surulescu²

¹Mechanical and Process Engineering, TU Kaiserslautern, Gottlieb-Daimler-Straße 42, 67663, Kaiserslautern, Rhineland-Palatinate, Germany. sandesh.hiremath@mv.uni-kl.de.

Journal of Mathematical Biology

|June 10, 2023

Summary

This study uses a data-driven approach to understand pseudopalisade formation in glioblastoma (GBM). Researchers identified bio-mechanisms and proposed strategies to disrupt these aggressive tumor patterns.

Keywords:

Data driven modeling Optimal control Pattern formation Pseudopalisades

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Last Updated: Jul 27, 2025

Understanding Cerebellar Pattern Formation

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Published on: November 1, 2007

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

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Published on: February 4, 2013

Area of Science:

Computational biology
Mathematical oncology
Tumor microenvironment dynamics

Background:

Pseudopalisades are characteristic patterns in glioblastoma (GBM), a grade IV brain tumor, indicating aggressiveness.
The exact bio-mechanisms driving pseudopalisade formation are complex and not fully understood.
Current understanding relies on macroscopic models of GBM dynamics coupled with extracellular pH.

Purpose of the Study:

To develop a data-driven methodology to investigate pseudopalisade formation in GBM.
To identify key bio-mechanisms responsible for generating observed pseudopalisade patterns.
To propose strategies for disrupting pseudopalisade formation and potentially controlling GBM growth.

Main Methods:

Utilized a macroscopic GBM dynamics model coupled with extracellular pH dynamics.
Formulated a terminal value optimal control problem to identify bio-mechanism parameters.
Employed histological images of pseudopalisade-like structures as target patterns for model fitting.
Developed pattern-counteracting strategies and explored parameter synthesis for pattern disruption.

Main Results:

Successfully identified optimal model parameters that generate specific pseudopalisade patterns from histological data.
Proposed two distinct strategies to counteract and potentially inhibit pseudopalisade formation.
Demonstrated a method for synthesizing new pseudopalisade patterns through linear combination of identified parameters.
Investigated the potential for complex therapeutic approaches to reverse simple pseudopalisade patterns via numerical simulations.

Conclusions:

The data-driven methodology provides insights into the bio-mechanisms underlying pseudopalisade formation in GBM.
Identified strategies offer potential avenues for developing novel therapeutic interventions against GBM.
The findings suggest that complex pseudopalisade patterns may arise from simpler underlying mechanisms, offering new therapeutic design possibilities.