Distributed delays in a hybrid model of tumor-immune system interplay

Giulio Caravagna, Alex Graudenzi, Alberto d'Onofrio

Research output: Contribution to journalArticlepeer-review

Abstract

A tumor is kinetically characterized by the presence of multiple spatio-temporal scales in which its cells interplay with, for instance, endothelial cells or Immune system effectors, exchanging various chemical signals. By its nature, tumor growth is an ideal object of hybrid modeling where discrete stochastic processes model low-numbers entities, and mean-field equations model abundant chemical signals. Thus, we follow this approach to model tumor cells, effector cells and Interleukin-2, in order to capture the Immune surveillance effect. We here present a hybrid model with a generic delay kernel accounting that, due to many complex phenomena such as chemical transportation and cellular differentiation, the tumor-induced recruitment of effectors exhibits a lag period. This model is a Stochastic Hybrid Automata and its semantics is a Piecewise Deterministic Markov process where a two-dimensional stochastic process is interlinked to a multi-dimensional mean-field system. We instantiate the model with two well-known weak and strong delay kernels and perform simulations by using an algorithm to generate trajectories of this process. Via simulations and parametric sensitivity analysis techniques we (i) relate tumor mass growth with the two kernels, we (ii) measure the strength of the Immune surveillance in terms of probability distribution of the eradication times, and (iii) we prove, in the oscillatory regime, the existence of a stochastic bifurcation resulting in delay-induced tumor eradication.

Original languageEnglish
Pages (from-to)37-57
Number of pages21
JournalMathematical Biosciences and Engineering
Volume10
Issue number1
DOIs
Publication statusPublished - Feb 2013

Keywords

  • Algorithms
  • Animals
  • Computer Simulation
  • Humans
  • Immune System
  • Immunologic Surveillance
  • Kinetics
  • Markov Chains
  • Medical Oncology
  • Models, Biological
  • Models, Statistical
  • Neoplasms
  • Oscillometry
  • Sensitivity and Specificity
  • Stochastic Processes

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