Prediction of source nutrients for microorganisms using metabolic networks

TL;DR

This paper reviews how metabolic network models can predict essential nutrients for microorganisms, aiding cultivation efforts, and presents a protocol for applying these models to genome-scale networks for experimental validation.

Contribution

It introduces a protocol for using metabolic network models to predict source nutrients for microbes, facilitating cultivation of uncultivated species.

Findings

Metabolic models can predict nutrient requirements for microbes.

The protocol applies to genome-scale metabolic networks.

Method supports experimental validation of microbial growth conditions.

Abstract

Metagenomics has lowered the barrier to microbial discovery--enabling the identification of novel microbes without isolation--but cultures remain imperative for the deep study of microbes. Cultivation and isolation of non-model microbes remains a major challenge, despite advances in high-throughput culturomic methods. The quantity of simultaneous experimental variables is constrained by time and resources, but the list can be reduced using computational biology. Given an annotated genome, metabolic modelling can be used to predict source nutrients required for the growth of a microbe, which acts as an initial screen to inform culture and isolation experiments. This chapter provides an overview of metabolic networks and modelling and how they can be used to predict the nutrient requirements of a microorganism, followed by a sample protocol using a toy metabolic network, which is then expanded to a genome-scale metabolic network application. These methods can be applied to any metabolic network of interest--which in turn can be created from any genome of interest--and are a starting point for experimental validation of source nutrients required for microorganisms that remain uncultivated to date.