  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.
