Signatures of arithmetic simplicity in metabolic network architecture

TL;DR

This study explores the fundamental principles underlying metabolic network architecture by analyzing artificial chemical universes, revealing properties like autocatalytic cycles and hierarchical modules that may also apply to real biological systems.

Contribution

The paper introduces a novel application of flux constraint-based models to artificial chemistries, uncovering universal properties and principles that could explain metabolic complexity.

Findings

Presence of autocatalytic cycles in artificial pathways

Hierarchical modular organization observed

Logarithmic relationship between pathway length and molecule size

Abstract

Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that several of the properties predicted by the artificial chemistry model hold for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity.