Weakly coupled map lattice models for multicellular patterning and collective normalization of abnormal single-cell states

TL;DR

This paper introduces a weakly coupled map lattice model using cellular automata to study how weakening local rules affects multicellular patterning and normalization of abnormal cells, with implications for tumor biology.

Contribution

It develops a novel model combining cellular automata and weak coupling to analyze patterning and normalization in biological networks.

Findings

Weak coupling influences pattern formation and cell state dynamics.

Model can simulate normalization of abnormal cells by normal neighbors.

Potential applications in understanding tumor cell behavior.

Abstract

We present a weakly coupled map lattice model for patterning that explores the effects exerted by weakening the local dynamic rules on model biological and artificial networks composed of two-state building blocks (cells). To this end, we use two cellular automata models based on: (i) a smooth majority rule (model I) and (ii) a set of rules similar to those of Conway's Game of Life (model II). The normal and abnormal cell states evolve according with local rules that are modulated by a parameter $\kappa$. This parameter quantifies the effective weakening of the prescribed rules due to the limited coupling of each cell to its neighborhood and can be experimentally controlled by appropriate external agents. The emergent spatio-temporal maps of single-cell states should be of significance for positional information processes as well as for intercellular communication in tumorigenesis where the collective normalization of abnormal single-cell states by a predominantly normal neighborhood may be crucial.