Optimal enzyme rhythms in cells

TL;DR

This paper develops a mathematical framework to determine optimal small-amplitude enzyme activity rhythms in cells, showing how these rhythms can enhance metabolic efficiency by exploiting network dynamics and external cues.

Contribution

It introduces a quadratic optimization approach to compute optimal enzyme phases and amplitudes, integrating network structure, kinetics, and external rhythms.

Findings

Optimal enzyme rhythms can be computed using quadratic optimization.

Enzymes with synergistic effects are coexpressed with specific phase shifts.

The theory predicts how cells can combine different regulation mechanisms for enzyme rhythms.

Abstract

Cells can use periodic enzyme activities to adapt to periodic environments or existing internal rhythms and to establish metabolic cycles that schedule biochemical processes in time. A periodically changing allocation of the protein budget between reactions or pathways may increase the overall metabolic efficiency. To study this hypothesis, I quantify the possible benefits of small-amplitude enzyme rhythms in kinetic models. Starting from an enzyme-optimised steady state, I score the effects of possible enzyme rhythms on a metabolic objective and optimise their amplitudes and phase shifts. Assuming small-amplitude rhythms around an optimal reference state, optimal phases and amplitudes can be computed by solving a quadratic optimality problem. In models without amplitude constraints, general periodic enzyme profiles can be obtained by Fourier synthesis. The theory of optimal enzyme rhythms combines the dynamics and economics of metabolic systems and explains how optimal small-amplitude enzyme profiles are shaped by network structure, kinetics, external rhythms, and the metabolic objective. The formulae show how orchestrated enzyme rhythms can exploit synergy effects to improve metabolic performance and that optimal enzyme profiles are not simply adapted to existing metabolic rhythms, but that they actively shape these rhythms to improve their own (and other enzymes') efficiency. The resulting optimal enzyme profiles "portray" the enzymes' dynamic effects in the network: for example, enzymes that act synergistically may be coexpressed, periodically and with some optimal phase shifts. The theory yields optimality conditions for enzyme rhythms in metabolic cycles, with static enzyme adaptation as a special case, and predicts how cells should combine transcriptional and posttranslational regulation to realise enzyme rhythms at different frequencies.