Stem Cell Networks

TL;DR

This paper introduces a comprehensive computational theory of stem cell networks, classifying their architectures, dynamics, and phenotypes, and linking them to developmental processes and cancer, paving the way for new control methods.

Contribution

It provides a novel classification and mathematical framework for stem cell networks, connecting their structure to developmental outcomes and disease.

Findings

Stem cell networks have unique topologies and semantics.

Growth dynamics relate to Pascal's Triangle coefficients.

Implications for cancer stem cell control methods.

Abstract

We present a general computational theory of stem cell networks and their developmental dynamics. Stem cell networks are special cases of developmental control networks. Our theory generates a natural classification of all possible stem cell networks based on their network architecture. Each stem cell network has a unique topology and semantics and developmental dynamics that result in distinct phenotypes. We show that the ideal growth dynamics of multicellular systems generated by stem cell networks have mathematical properties related to the coefficients of Pascal's Triangle. The relationship to cancer stem cells and their control networks is indicated. The theory lays the foundation for a new research paradigm for understanding and investigating stem cells. The theory of stem cell networks implies that new methods for generating and controlling stem cells will become possible.