Analysis of the impact degree distribution in metabolic networks using branching process approximation

TL;DR

This paper introduces a branching process approximation method to predict the impact degree distribution in metabolic networks, aiding the assessment of their robustness against failures.

Contribution

It presents a novel theoretical approach to estimate impact degree distributions in metabolic networks using branching process theory, validated on real-world data.

Findings

The method effectively predicts impact degree distributions in metabolic networks.

It offers a new tool for evaluating metabolic robustness.

Limitations of the approximation are discussed.

Abstract

Theoretical frameworks to estimate the tolerance of metabolic networks to various failures are important to evaluate the robustness of biological complex systems in systems biology. In this paper, we focus on a measure for robustness in metabolic networks, namely, the impact degree, and propose an approximation method to predict the probability distribution of impact degrees from metabolic network structures using the theory of branching process. We demonstrate the relevance of this method by testing it on real-world metabolic networks. Although the approximation method possesses a few limitations, it may be a powerful tool for evaluating metabolic robustness.