This repository provides resources and model files for the genome-scale resource balance analysis (RBA) model scRBA that accompanies the following manuscript:
- Hoang V. Dinh and Costas D. Maranas. "Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis." Metab. Eng., 2023, 77:242-255. https://doi.org/10.1016/j.ymben.2023.04.009
which have been updated to follow the conventions in the following manuscript:
- Eric J. Mooney, Patrick F. Suthers, Wheaton L. Schroeder, Hoang V. Dinh, Xi Li, Yihui Shen, Tianxia Xiao, Catherine M. Call, Heide Baron, Arjuna M. Subramanian, Daniel R. Weilandt, Felix C. Keber, Martin Wühr, Joshua D. Rabinowitz, Costas D. Maranas. "Metabolic flux and resource balance in the oleaginous yeast Rhodotorula toruloides." Metab. Eng. https://doi.org/10.1016/j.ymben.2025.11.012.
Please see the manuscripts above for details on the model, and the repository https://github.com/maranasgroup/rtRBA.
For the 2023 version, formulation and software usage guide are available at suppMat/scRBA_suppText1_2023-04-10.docx and suppMat/scRBA_suppText2_2023-04-10.docx Program and packages requirements for the provided software implementation: Python 3 (plus packages: cobra, pandas, numpy, matplotlib, jupyter, scikit-learn, cplex (compiled from installed CPLEX linear programming solver), and all automatically-installed associated dependencies to those already listed) and GAMS with ("built-in") Soplex solver. (Tested on Python 3.6 and GAMS 39.1.0. Python package installation could be done through "pip".)
This work was funded by the DOE Center for Advanced Bioenergy and Bioproducts Innovation (U.S. Department of Energy, Office of Science, Biological and Environmental Research Program under Award Number DE-SC0018420). Any opinions, findings, and conclusions or recommendations expressed herein are those of the author(s) and do not necessarily reflect the views of the U.S. Department of Energy. Computations for this research were performed on the Pennsylvania State University’s Roar Collab supercomputer. The authors of this work recognize the Penn State Institute for Computational and Data Sciences (RRID:SCR_025154) for providing access to computational research infrastructure within the Roar Core Facility (RRID: SCR_026424).