Dynamic resource allocation in eukaryotic Resource Balance Analysis

TL;DR

This paper introduces a dynamic extension of Resource Balance Analysis for eukaryotic cells, modeling time-dependent resource allocation to optimize growth, and demonstrates how steady-state solutions are special cases of this dynamic framework.

Contribution

It develops a novel dynamic RBA model for eukaryotic cells using optimal control, capturing regulation and turnover not addressed by static models.

Findings

Optimal control characterizes resource allocation strategies.

Steady-state RBA solutions are limiting cases of the dynamic model.

The approach enhances understanding of eukaryotic cellular growth regulation.

Abstract

Resource Balance Analysis (RBA) is a framework for predicting steady-state cellular growth under resource constraints. However, classical RBA formulations are static and do not capture the dynamic regulation of biosynthetic resources or macromolecular turnover, which is particularly important in eukaryotic cells. In this work, we propose a dynamic extension of eukaryotic RBA based on an optimal control formulation. Cellular growth is modeled as the result of a time-dependent allocation of translational capacity between metabolic enzymes and macromolecular machinery, aimed at maximizing biomass accumulation over a finite time horizon. Using Pontryagin's Maximum Principle, we characterize optimal allocation strategies and show that steady-state RBA solutions arise as limiting regimes of the dynamic problem.