Cancer cells metabolize glucose through metabolic pathways that differ from those used by healthy and differentiated cells. In particular, tumours have been shown to consume more glucose than their healthy counterparts and to use anaerobic metabolic pathways, even under aerobic conditions. Nevertheless, scientists have still not been able to explain why cancer cells evolved to present an altered metabolism and what evolutionary advantage this might provide them. Experimental and computational models have been increasingly used in recent years to understand some of these biological questions. Multicellular tumour spheroids are effective experimental models as they replicate the initial stages of avascular solid tumour growth. Furthermore, these experiments generate data which can be used to calibrate and validate computational studies that aim to simulate tumour growth. Hybrid models are of particular relevance in this field of research because they model cells as individual agents while also incorporating continuum representations of the substances present in the surrounding microenvironment that may participate in intracellular metabolic networks as concentration or density distributions. Henceforth, in this review, we explore the potential of computational modelling to reveal the role of metabolic reprogramming in tumour growth.
Keywords: ABM, agent-based model; ATP, adenosine triphosphate; CA, cellular automata; CPM, cellular Potts model; ECM, extracellular matrix; FBA, Flux Balance Analysis; FDG-PET, [18F]-fluorodeoxyglucose-positron emission tomography; MCTS, multicellular tumour spheroids; ODEs, ordinary differential equations; PDEs, partial differential equations; SBML, Systems Biology Markup Language; Warburg effect; agent-based models; glucose metabolism; hybrid modelling; multicellular simulations.
© 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.