Balancing the cellular budget: lessons in metabolism from microbes to cancer

TL;DR

This review explores the metabolic preferences of cancer cells, especially the Warburg effect, by comparing them with microbial and normal physiology to understand the underlying optimization strategies and resource allocation.

Contribution

It introduces the resource allocation framework and dynamical systems modeling as new approaches to understand cellular metabolic choices in cancer and microbes.

Findings

Metabolic shifts favoring glycolysis can be driven by growth needs but are context-dependent.

Optimization goals in cell metabolism are multifaceted, balancing energy costs and benefits.

Resource allocation models offer a promising way to analyze cellular metabolic strategies.

Abstract

Cancer cells are often seen to prefer glycolytic metabolism over oxidative phosphorylation even in the presence of oxygen-a phenomenon termed the Warburg effect. Despite significant strides in the decades since its discovery, a clear basis is yet to be established for the Warburg effect and why cancer cells show such a preference for aerobic glycolysis. In this review, we draw on what is known about similar metabolic shifts both in normal mammalian physiology and overflow metabolism in microbes to shed new light on whether aerobic glycolysis in cancer represents some form of optimisation of cellular metabolism. From microbes to cancer, we find that metabolic shifts favouring glycolysis are sometimes driven by the need for faster growth, but the growth rate is by no means a universal goal of optimal metabolism. Instead, optimisation goals at the cellular level are often multi-faceted and any given metabolic state must be considered in the context of both its energetic costs and benefits over a range of environmental contexts. For this purpose, we identify the conceptual framework of resource allocation as a potential testbed for the investigation of the cost-benefit balance of cellular metabolic strategies. Such a framework is also readily integrated with dynamical systems modelling, making it a promising avenue for new answers to the age-old question of why cells, from cancers to microbes, choose the metabolic strategies they do.