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Multi-scale modeling in morphogenesis: a critical analysis of the cellular Potts model
1Center for Information Services and High Performance Computing, Technical University Dresden, Dresden, Germany. anja.voss-boehme@tu-dresden.de
Plos One
|September 18, 2012
Summary
Cellular Potts models (CPMs) are a computational tool for biological development. Mathematical analysis reveals CPMs exhibit degenerate long-time behavior where cells die out, limiting their use in modeling tissue-scale dynamics.
Area of Science:
- Theoretical biology
- Mathematical modeling
- Computational biology
Background:
- Cellular Potts models (CPMs) are increasingly used in theoretical biology to simulate biological development.
- CPMs offer sub-cellular spatial resolution, suitable for problems involving multiple spatial and temporal scales.
- Despite their application, CPMs have received limited attention from mathematical theory.
Purpose of the Study:
- To theoretically analyze Cellular Potts models (CPMs).
- To investigate the dynamical properties of CPM updating rules for intercellular interactions.
- To understand CPM behavior across different temporal scales and potential extensions to unbounded systems.
Main Methods:
- Theoretical analysis of CPM updating rules.
- Investigation of CPM behavior on finite and unbounded lattices.
- Integration of CPMs into a general multiscale modeling framework.
Main Results:
- CPMs demonstrate degenerate long-time behavior, characterized by cell extinction irrespective of interaction structures.
- Analysis of spatio-temporal limits for CPMs on unbounded systems was explored.
- Surface fluctuations in CPMs impose significant limitations on their mechanistic and phenomenological applications.
Conclusions:
- The inherent cell-death dynamic in CPMs limits their utility for long-term biological simulations.
- CPM limitations highlight the need for refined modeling approaches for tissue-scale emergent behavior.
- Further theoretical development is required to fully exploit CPMs in mechanistic and phenomenological biological modeling.

