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Agent-Based Learning Model for the Obesity Paradox in RCC

Belenchia M.
•
Rocchetti G.
•
Maestri S.
altro
Merelli E.
2021
  • journal article

Periodico
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
Abstract
A recent study on the immunotherapy treatment of renal cell carcinoma reveals better outcomes in obese patients compared to lean subjects. This enigmatic contradiction has been explained, in the context of the debated obesity paradox, as the effect produced by the cell-cell interaction network on the tumor microenvironment during the immune response. To better understand this hypothesis, we provide a computational framework for the in silico study of the tumor behavior. The starting model of the tumor, based on the cell-cell interaction network, has been described as a multiagent system, whose simulation generates the hypothesized effects on the tumor microenvironment. The medical needs in the immunotherapy design meet the capabilities of a multiagent simulator to reproduce the dynamics of the cell-cell interaction network, meaning a reaction to environmental changes introduced through the experimental data.
DOI
10.3389/fbioe.2021.642760
WOS
WOS:000649767000001
Archivio
https://hdl.handle.net/11390/1243027
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85105974504
https://ricerca.unityfvg.it/handle/11390/1243027
Diritti
metadata only access
Soggetti
  • bioagent

  • cell-cell interaction...

  • computational biology...

  • dynamical network

  • immunotherapy

  • interaction-as-percep...

  • multiagent system

  • renal cell carcinoma

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