Literature DB >> 32987579

Basic stochastic model for tumor virotherapy.

Tuan Anh Phan1, Jianjun Paul Tian1.   

Abstract

The complexity of oncolytic virotherapy arises from many factors. In this study, we incorporate environmental noise and stochastic effects to our basic deterministic model and propose a stochastic model for viral therapy in terms of Ito stochastic differential equations. We conduct a detailed analysis of the model using boundary methods. We find two combined parameters, one describes possibilities of eradicating tumors and one is an increasing function of the viral burst size, which serve as thresholds to classify asymptotical dynamics of the model solution paths. We show there are three ergodic invariant probability measures which correspond to equilibrium states of the deterministic model, and extra possibility to eradicate tumor due to strong variance of tumor growth rate and medium viral burst size. Numerical analysis demonstrates several typical solution paths with biological explanations. In addition, we provide some medical interpretations and implications.

Entities:  

Keywords:  Ito stochastic differential equation ; ergodic invariant probability measure ; viral burst size ; virotherapy

Mesh:

Year:  2020        PMID: 32987579      PMCID: PMC8881055          DOI: 10.3934/mbe.2020236

Source DB:  PubMed          Journal:  Math Biosci Eng        ISSN: 1547-1063            Impact factor:   2.194


  16 in total

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Review 9.  Gene therapy progress and prospects cancer: oncolytic viruses.

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Authors:  Zeljko Bajzer; Thomas Carr; Kresimir Josić; Stephen J Russell; David Dingli
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  1 in total

1.  Deterministic and stochastic modeling for PDGF-driven gliomas reveals a classification of gliomas.

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