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Self-organization in brain tumors: How cell morphology and cell density influence glioma pattern formation.
Jamous, Sara; Comba, Andrea; Lowenstein, Pedro R; Motsch, Sebastien.
Afiliação
  • Jamous S; Arizona State University, School of Mathematical & Statistical Sciences, Tempe, Arizona, United States of America.
  • Comba A; University of Michigan, School of Medicine, Department of Neurosurgery and Rogel Cancer Center, Ann Arbor, Michigan, United States of America.
  • Lowenstein PR; University of Michigan, School of Medicine, Department of Neurosurgery and Rogel Cancer Center, Ann Arbor, Michigan, United States of America.
  • Motsch S; Arizona State University, School of Mathematical & Statistical Sciences, Tempe, Arizona, United States of America.
PLoS Comput Biol ; 16(5): e1007611, 2020 05.
Article em En | MEDLINE | ID: mdl-32379821
ABSTRACT
Modeling cancer cells is essential to better understand the dynamic nature of brain tumors and glioma cells, including their invasion of normal brain. Our goal is to study how the morphology of the glioma cell influences the formation of patterns of collective behavior such as flocks (cells moving in the same direction) or streams (cells moving in opposite direction) referred to as oncostream. We have observed experimentally that the presence of oncostreams correlates with tumor progression. We propose an original agent-based model that considers each cell as an ellipsoid. We show that stretching cells from round to ellipsoid increases stream formation. A systematic numerical investigation of the model was implemented in [Formula see text]. We deduce a phase diagram identifying key regimes for the dynamics (e.g. formation of flocks, streams, scattering). Moreover, we study the effect of cellular density and show that, in contrast to classical models of flocking, increasing cellular density reduces the formation of flocks. We observe similar patterns in [Formula see text] with the noticeable difference that stream formation is more ubiquitous compared to flock formation.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Biologia Computacional / Glioma Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Biologia Computacional / Glioma Idioma: En Ano de publicação: 2020 Tipo de documento: Article