Control of genes by self-organizing multicellular interaction networks

TL;DR

This paper develops a new theoretical framework for understanding multicellular self-organization, using dynamic graphs to model cell interactions, with potential applications in controlling and engineering biological systems.

Contribution

It introduces a biologically-general theoretical approach based on dynamic graphs to explain multicellular self-organization in Escherichia coli.

Findings

Proposes a new graph-based theory for multicellular self-organization.

Develops ideas from first principles applicable to biological systems.

Aims to facilitate experimental and computational control of multicellular processes.

Abstract

Multicellular self-organization drives development in biological organisms, yet a comprehensive theory is lacking as basic properties of cells can complicate common approaches. Framing such properties by dynamic graphs led to new theoretical propositions for multicellular self-organization in Escherichia coli. Here, corresponding ideas are developed from biologically-general first principles. The resulting perspective could aid both experimental and computational approaches to multicellular biology as well as efforts to control and engineer it.