Sampling from the Solution Space and Metabolic Environments of Genome-Scale Metabolic Models

TL;DR

Flux sampling explores the full range of metabolic phenotypes in genome-scale models without requiring an objective function, revealing diverse solutions and responses to environmental changes.

Contribution

This paper reviews state-of-the-art flux sampling methods and demonstrates their application in analyzing metabolic models under various conditions.

Findings

Flux sampling uncovers phenotypes masked by traditional methods.

Targeted sampling focuses on phenotypes with near-optimal objective values.

Flux sampling effectively analyzes condition-dependent metabolic behaviors.

Abstract

Flux sampling is an analysis that, based on a distribution, picks randomly an efficient number of points from the solution space of a metabolic model. Unlike most constraint-based analyses, flux sampling does not require an objective function to optimize, allowing for the exploration of the whole spectrum of the phenotypes a species can exhibit. However, sampling can also be restricted to a subspace where a chosen objective reaches at least a specified fraction of its optimum. This targeted approach adds value when investigating phenotypes that are optimal for a specific function. Contrary to Flux Balance Analysis, which returns a single solution, sampling leverages statistical power to uncover phenotypes that otherwise would be masked. This can be especially useful when changing the conditions (medium) in which a species lives. Here, we highlight some state-of-the-art methods for applying flux sampling at Genome-Scale Metabolic Models in different scenarios, and we showcase flux sampling applications