RBApy: extending resource allocation modeling to eukaryotes in complex environments.

Abstract

Motivation: Resource allocation modeling-as the Resource Balance Analysis (RBA) framework-provides a way to understand and predict how limited cellular resources (e.g. energy, proteins, etc.) in cells are distributed among competing cell processes within a limited cellular space. Currently, resource allocation modeling for eukaryotes remains limited due to the lack of software capable of generating calibrated RBA models for these types of cells, unlike prokaryotes, which benefit from the software tools such as RBApy, RBAtools, and the RBAml format for model encoding.

Results: Here, we extended the RBA toolkit (RBApy, RBAtools, and RBAml) to account for specific aspects of eukaryotic cells growing in complex environments such as varying temperature, light, or nutritional conditions. We used them to generate and simulate RBA models of both prokaryotic (Escherichia coli) and eukaryotic (Arabidopsis thaliana) cells for varying temperatures. The resulting models show excellent prediction capabilities when benchmarked against published experimental datasets. The upgraded RBA toolkit will pave the way to creating, calibrating, and running resource allocation models for crops, livestock, or humans for a wide range of medical, biotechnological, or agricultural applications in the future.

Availability and implementation: RBApy and RBAtools are available via PyPI and at https://github.com/RBAgroup.