Goelzer, A. | bioRxiv Science

bioRxiv2022

RBAtools: a programming interface for Resource Balance Analysis modelling

MotivationEfficient resource allocation contributes to an organisms fitness and improves success in competition. The Resource Balance Analysis (RBA) computational framework enables the analysis of an organisms growth-optimal configurations in various environments, at genome-scale. The existing RBApy software enables the construction of RBA models on genome-scale and the calculation of medium-specific, growth-optimal cell states, including metabolic fluxes and the abundance of macromolecular machines. However, to address the needs of non-expert users, there is a need for a simple programming API, easy to use and interoperable with other software through convenient formats for models and data. ResultsThe RBAtools python package enables the convenient use of RBA models and addresses non-expert users. As a flexible programming interface, it enables the implementation of custom workflows and simplifies the modification of existing genome-scale RBA models and data export to various formats. The features comprise simulation, model fitting, parameter screens, sensitivity analysis, variability analysis, and the construction of Pareto fronts. Models and data are represented as structured tables, in HTML, and common formats for fluxomics and proteomics visualization. AvailabilityDetails about RBA can be found at rba.inrae.fr. RBAtools documentation, installation instructions, and tutorials are available at sysbioinra.github.io/rbatools. Contactwolfram.liebermeister@inrae.fr, anne.goelzer@inrae.fr

systems biology↗

bioRxiv2019

RBA for eukaryotic cells: foundations and theoretical developments

Resource allocation models were recently identified as new ways to investigate cell design principles. In particular, the Resource Balance Analysis (RBA) framework is the first constraint-based modelling method capable of accurate quantitative predictions of the genome-wide resource allocation. Initially developed and validated on bacteria, the objective of this paper is to provide the mathematical fundations of the extension of the RBA framework to eukaryotic cells. We especially investigate the way to handle the cellular compartments in order to formalize eventually the functioning of organelles. It turns out that the final RBA problem for eukaryotic cells is close to the one of prokaryotic cells from a theoretical point of view. The mathematical properties that were already identified on the prokaryotic RBA framework can be easily transposed to eukaryotic cells. In particular, the eukaryotic RBA problem can be solved easily at the cell scale by Linear Programming. This paves the way to future developments of RBA models for eukaryotic cells.

systems biology↗