Automated generation of bacterial resource allocation models

TL;DR

This paper introduces RBApy, a Python package that automates the creation and simulation of bacterial resource allocation models, enabling accurate predictions of cell states across conditions.

Contribution

It presents an integrated workflow and software tool for building and calibrating bacterial RBA models from genome data, improving accessibility and reproducibility.

Findings

RBApy accurately reproduces Bacillus subtilis RBA results.

Calibrated E. coli RBA model fits experimental data well.

Demonstrates modeling of engineered CO2-fixing E. coli.

Abstract

Resource Balance Analysis (RBA) is a computational method based on resource allocation, which performs accurate quantitative predictions of whole-cell states (i.e. growth rate, meta-bolic fluxes, abundances of molecular machines including enzymes) across growth conditions. We present an integrated workflow of RBA together with the Python package RBApy. RBApy builds bacterial RBA models from annotated genome-scale metabolic models by add-ing descriptions of cellular processes relevant for growth and maintenance. The package in-cludes functions for model simulation and calibration and for interfacing to Escher maps and Proteomaps for visualization. We demonstrate that RBApy faithfully reproduces results ob-tained by a hand-curated and experimentally validated RBA model for Bacillus subtilis. We also present a calibrated RBA model of Escherichia coli generated from scratch, which ob-tained excellent fits to measured flux values and enzyme abundances. RBApy makes whole-cell modeling accessible for a wide range of bacterial wild-type and engineered strains, as il-lustrated with a CO2-fixing Escherichia coli strain.