Inferring metabolic fluxes in nutrient-limited continuous cultures: A Maximum Entropy Approach with minimum information

TL;DR

This paper introduces a Maximum Entropy-based method to infer metabolic fluxes in nutrient-limited chemostats using minimal data, capturing both average fluxes and heterogeneity, and demonstrates its effectiveness on computational and experimental data.

Contribution

The paper presents a novel Maximum Entropy approach for inferring metabolic fluxes in chemostats with limited information, improving upon existing methods by capturing flux heterogeneity.

Findings

Accurately infers flux distributions with limited chemostat data.

Outperforms Flux Balance Analysis-based methods on E. coli data.

Provides insights into flux heterogeneity in cell cultures.

Abstract

We propose a new scheme to infer the metabolic fluxes of cell cultures in a chemostat. Our approach is based on the Maximum Entropy Principle and exploits the understanding of the chemostat dynamics and its connection with the actual metabolism of cells. We show that, in continuous cultures with limiting nutrients, the inference can be done with {\it limited information about the culture}: the dilution rate of the chemostat, the concentration in the feed media of the limiting nutrient and the cell concentration at steady state. Also, we remark that our technique provides information, not only about the mean values of the fluxes in the culture, but also its heterogeneity. We first present these results studying a computational model of a chemostat. Having control of this model we can test precisely the quality of the inference, and also unveil the mechanisms behind the success of our approach. Then, we apply our method to E. coli experimental data from the literature and show that it outperforms alternative formulations that rest on a Flux Balance Analysis framework.