AI & ML
System-Theoretic Analysis of Extended Cellular Potts Models for Tumor Invasion
arXiv cs.SY0 views
arXiv:2611.06690v1 Announce Type: new Abstract: The Cellular Potts Model has emerged as a standard framework for modeling multicellular systems and metastatic progression. Yet, computational extensions incorporating cell movement within spatial gradients of Extracellular Matrix (ECM), an important element of the tissue microenvironment, lack theoretical foundations. This work provides a system-theoretic formulation and analysis of Extended Cellular Potts Models (ECPMs) that explicitly couple tumor dynamics with ECM degradation. We reformulate the ECPM as an interconnected dynamical system: a stochastic process describing a network of locally interacting tumor cells coupled to a jump-linear system describing ECM evolution. By exploiting the sparse energy structure of the model, we characterize asymptotic ECM behavior, transition probabilities with uniform bounds, and global system convergence. In one-dimensional settings, we derive closed-form expressions for invasion time probabilities, revealing how mean invasion time depends on local ECM gradients, number of cells and their tendency to maintain a target volume. Numerical simulations validate theoretical predictions, establishing a principled foundation for analyzing haptotaxis-driven invasion in Potts-based models.
Source:https://arxiv.org/abs/2611.06690PreviousEnhanced Imitation Learning of Model Predictive Control through Importance WeightingNextExcitability of High-Frequency Positive Feedback
Related posts
Meta Quest Promo Codes and Coupons for November 2026
0Wired
BullMQ in NestJS: Retries, Idempotency and Dead-Letter Queues
0dev.to
80.6% at Chores, 12% at Real Work: AI's Benchmark Week
0dev.to
Efficient estimation of regression models for recurrent events in presence of terminal events
0arXiv stat.CO
Local Whittle estimation for distributional time series: A regularization framework in Bayes Hilbert space
0arXiv stat.CO
Inventory and Assortment Optimization under General Choice: A Unified Approximation Framework
0arXiv stat.CO