Flux-based classification of reactions reveals a functional bow-tie organization of complex metabolic networks

TL;DR

This paper uses flux balance analysis to classify reactions in metabolic networks, revealing a functional bow-tie organization that distinguishes input, output, and intermediate components, advancing understanding of network structure and function.

Contribution

It introduces a novel reaction classification algorithm that uncovers a functional bow-tie structure in metabolic networks, differing from purely graph-theoretic approaches.

Findings

Reactions show bimodal activity distribution across environments

The network decomposes into input, output, and intermediate sub-networks

Reveals a functional bow-tie organization distinct from structural bow-tie models

Abstract

Unraveling the structure of complex biological networks and relating it to their functional role is an important task in systems biology. Here we attempt to characterize the functional organization of the large-scale metabolic networks of three microorganisms. We apply flux balance analysis to study the optimal growth states of these organisms in different environments. By investigating the differential usage of reactions across flux patterns for different environments, we observe a striking bimodal distribution in the activity of reactions. Motivated by this, we propose a simple algorithm to decompose the metabolic network into three sub-networks. It turns out that our reaction classifier which is blind to the biochemical role of pathways leads to three functionally relevant sub-networks that correspond to input, output and intermediate parts of the metabolic network with distinct structural characteristics. Our decomposition method unveils a functional bow-tie organization of metabolic networks that is different from the bow-tie structure determined by graph-theoretic methods that do not incorporate functionality.