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Related Experiment Video

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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Published on: May 9, 2017

Automated generation of bacterial resource allocation models.

Ana Bulović¹, Stephan Fischer², Marc Dinh²

¹Theoretische Biophysik, Humboldt-Universität zu Berlin, Berlin, Germany.

Metabolic Engineering

|June 13, 2019

Summary

Resource Balance Analysis (RBA) models predict cellular states using resource allocation. The new RBApy Python package simplifies whole-cell modeling for bacteria, making it accessible for research and strain engineering.

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Area of Science:

Computational biology
Systems biology
Metabolic modeling

Background:

Resource Balance Analysis (RBA) is a computational method for predicting whole-cell states, including growth rate and metabolic fluxes.
Accurate quantitative predictions are crucial for understanding cellular behavior across various conditions.

Purpose of the Study:

To present an integrated workflow of RBA with the Python package RBApy.
To make whole-cell modeling accessible for a wide range of bacterial strains.

Main Methods:

RBApy builds bacterial RBA models from genome-scale metabolic models, incorporating cellular growth and maintenance processes.
The package offers functions for model simulation, calibration, and visualization via Escher and Proteomaps.
The workflow was validated using Bacillus subtilis and applied to Escherichia coli.

Main Results:

RBApy successfully reproduced results from a validated RBA model for Bacillus subtilis.
A calibrated RBA model of Escherichia coli generated from scratch showed excellent agreement with measured flux values and enzyme abundances.
The RBApy package facilitates whole-cell modeling for diverse bacterial strains, including engineered ones like CO2-fixing E. coli.

Conclusions:

RBApy provides a user-friendly and efficient tool for whole-cell modeling in bacteria.
The package democratizes access to complex modeling techniques, supporting research in microbial physiology and synthetic biology.
RBApy enables accurate predictions of cellular states, aiding in the design and optimization of bacterial strains for various applications.