Hybrid computational models of multicellular tumour growth considering glucose metabolism

Gonçalves, Inês G. ; García-Aznar, José Manuel (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.1016/j.csbj.2023.01.044

Año: 2023

Publicado en: Computational and Structural Biotechnology Journal 21 (2023), 1262-1271
ISSN: 2001-0370

Factor impacto JCR: 4.4 (2023)

Categ. JCR: BIOCHEMISTRY & MOLECULAR BIOLOGY rank: 82 / 313 = 0.262 (2023) - Q2 - T1

Factor impacto CITESCORE: 9.3 - Computer Science Applications (Q1) - Genetics (Q1) - Structural Biology (Q1) - Biotechnology (Q1) - Biochemistry (Q1) - Biophysics (Q1)

Factor impacto SCIMAGO: 1.485 - Biochemistry (Q1) - Biophysics (Q1) - Biotechnology (Q1) - Computer Science Applications (Q1) - Genetics (Q1) - Structural Biology (Q2)

Financiación: info:eu-repo/grantAgreement/EC/H2020/101018587/EU/Individual and Collective Migration of the Immune Cellular System/ICoMICS
Financiación: info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE
Financiación: info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-122409OB-C21

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)