Renormalization Group Transformation for Hamiltonian Dynamical Systems in Biological Networks

TL;DR

This paper introduces a novel application of renormalization group theory to biological network dynamics, aiming to infer properties of complex biological systems from simple motifs.

Contribution

It develops an original framework applying renormalization group transformations to biological networks, bridging simple motifs and complex systems.

Findings

First step towards a renormalization-based analysis of biological networks

Framework shows promise for understanding complex biological systems

Establishes foundation for future empirical validation

Abstract

We apply the renormalization group theory to the dynamical systems with the simplest example of basic biological motifs. This includes the interpretation of complex networks as the perturbation to simple network. This is the first step to build our original framework to infer the properties of biological networks, and the basis work to see its effectiveness to actual complex systems.