Potential unsatisfiability of cyclic constraints on stochastic biological networks biases selection toward hierarchical architectures

TL;DR

This paper investigates how cyclic constraints in biological networks can lead to unsatisfiability, potentially biasing evolution toward hierarchical architectures due to the likelihood of inconsistent constraints.

Contribution

It introduces a framework for analyzing network architecture based on constraint satisfaction and demonstrates that cyclic networks are more prone to unsatisfiability, influencing evolutionary bias.

Findings

Networks with more cycles are more likely to have unsatisfiable constraints.

Constraints can bias evolution toward hierarchical, modular architectures.

Context-dependent functionality of biological networks is highlighted.

Abstract

Constraints placed upon the phenotypes of organisms result from their interactions with the environment. Over evolutionary timescales, these constraints feed back onto smaller molecular subnetworks comprising the organism. The evolution of biological networks is studied by considering a network of a few nodes embedded in a larger context. Taking into account this fact that any network under study is actually embedded in a larger context, we define network architecture, not on the basis of physical interactions alone, but rather as a specification of the manner in which constraints are placed upon the states of its nodes. We show that such network architectures possessing cycles in their topology, in contrast to those that do not, may be subjected to unsatisfiable constraints. This may be a significant factor leading to selection biased against those network architectures where such inconsistent constraints are more likely to arise. We proceed to quantify the likelihood of inconsistency arising as a function of network architecture finding that, in the absence of sampling bias over the space of possible constraints and for a given network size, networks with a larger number of cycles are more likely to have unsatisfiable constraints placed upon them. Our results identify a constraint that, at least in isolation, would contribute to a bias in the evolutionary process toward more hierarchical-modular versus completely connected network architectures. Together, these results highlight the context-dependence of the functionality of biological networks.