Metabolite patterns reveal regulatory responses to genetic perturbations

TL;DR

This paper introduces REMEP, a metabolite-centric computational method that improves the prediction of cellular responses to genetic perturbations by capturing metabolite patterns, outperforming existing flux-based approaches.

Contribution

The paper presents REMEP, a novel metabolite-focused method that enhances accuracy in predicting cellular responses to gene knockouts compared to traditional flux-based models.

Findings

REMEP outperforms existing methods in predicting knockout effects in E. coli and S. cerevisiae.

REMEP captures cellular regulatory signatures through metabolite patterns.

The approach offers new insights into cellular regulation and control mechanisms.

Abstract

Genetic and environmental perturbation experiments have been used to study microbes in a bid to gain insight into transcriptional regulation, adaptive evolution, and other cellular dynamics. These studies have potential in enabling rational strain design. Unfortunately, experimentally determined intracellular flux distribution are often inconsistent or incomparable due to different experimental conditions and methodologies. Computational strain design relies on constraint-based reconstruction and analysis (COBRA) techniques to predict the effect of gene knockouts such as flux balance analysis (FBA), regulatory on/off minimization(ROOM), minimization of metabolic adjustment (MOMA), relative optimality in metabolic networks (RELATCH). Most of these knock-out prediction methods are based on conserving inherent flux patterns (between wild type and mutant) that are thought to be representative of the cellular regulatory structure. However, it has been recently demonstrated that these methods show poor agreement with experimental data. To improve the fidelity of knockout predictions and subsequent computational strain design, we developed REMEP, a metabolite-centric method. We demonstrate the improved performance of REMEP by comparing the different methods on experimental knockout data of E. coli, and S. cerevisiae grown in batch cultures. REMEP retains most of the features of earlier algorithms but is much more accurate in capturing cellular responses to genetic perturbations. A primary reason for this is that REMEP relies on the assumption that cellular regulatory structure leaves a signature on metabolite patterns and not just flux patterns. REMEP will also prove useful in uncovering novel insights into cellular regulation and control.