Explorability and the origin of Network Sparsity in Living Systems

TL;DR

This paper introduces the concept of explorability to explain why living system networks are sparse, showing that sparsity emerges from optimizing both explorability and robustness, and relates to ecosystem stability.

Contribution

It proposes a new variational principle linking network sparsity with explorability and robustness, providing a theoretical explanation for observed patterns in biological networks.

Findings

Sparsity scales inversely with system size in biological networks.

Higher connectivity complicates optimizing explorability and robustness.

The approach offers insights into the stability of ecosystems and May's paradox.

Abstract

The increasing volume of ecologically and biologically relevant data has revealed a wide collection of emergent patterns in living systems. Analyzing different datasets, ranging from metabolic gene-regulatory to species interaction networks, we find that these networks are sparse, i.e. the percentage of the active interactions scales inversely proportional to the system size. This puzzling characteristic has been neither yet considered nor explained. Herein, we introduce the new concept of explorability, a measure of the ability of the system to adapt to newly intervening changes. We show that sparsity is an emergent property resulting from a variational principle aiming at the optimization of both explorability and dynamical robustness, the capacity of the system to remain stable after perturbations of the underlying dynamics. Networks with higher connectivities lead to an incremental difficulty to find better values for both the explorability and dynamical robustness, associated with the fine-tuning of the newly added interactions. A relevant characteristic of our solution is its scale invariance, that is, it remains optimal when several communities are assembled togheter. Connectivity is also a key ingredient determining ecosystem stability and our proposed solution contributes to solving May's celebrated complexity-stability paradox.