Mathematical Modeling of Oncolytic Virotherapy

Heidbuechel, Johannes P W; Abate-Daga, Daniel; Engeland, Christine E.; Enderling, Heiko

doi:10.1007/978-1-4939-9794-7_21

Cited by 9 publications

(4 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various mathematical approaches allow the analysis of biological systems. For example, to infer gene expression patterns together with the cell fate trajectory in cancer therapies, and with the objectives to dimensionally reduce the patterns' spaces, pseudotemporal ordering methods are useful [54][55][56][57]. In the dynamical systems point of view, there are some weakness to construct cancer networks, because of lack of enough time series measurements on some cell expressions such as protein oscillations, gene dynamics etc...…”

Section: Introductionmentioning

confidence: 99%

Bistability and chaotic behaviors in a 4D cancer oncolytic Virotherapy mathematical model: Pspice and FPGA implementations

Kabong Nono,

Megam Ngouonkadi

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

Oncolytic viruses (OVs) exploit characteristics of mass cells and tumor-related reaction of the body to the presence of antigen, to lyse malignant cells and modulate the tumor microenvironment. However, the effective clinical utilization of these powerful treatment modules necessitates their logical control, especially in order to prevent solid and metastatic outgrowths. Hence, it is imperative to develop methods to protect a virus from the annihilating surroundings from the bloodstream when traveling to tumor locations. Our article reports on bistability and chaotic behavior in a 4D cancer virotherapy model. We find that unstable, stable and chaotic behaviors can appear in the model when tuning some of its parameters. With the help of the chart of dynamic behaviors in parameter spaces, numerical investigations of the system's characteristics are analyzed followed by a discussion of the obtained results. It appears that the local transition change from an invariant one-torus (IT1) to its two-torus (IT2) counterpart can be found in the system and this undergoes a Neimark-Saker (NS) change of direction. Based on analytical analysis, it is foretold that the model exhibits striking behaviors. We also focus our study on the deign of ad-hoc electronic and FPGA implementations of the cancer virotherapy's model, to illustrate the obtained analytical results.

show abstract

Section: Introductionmentioning

confidence: 99%

Bistability and chaotic behaviors in a 4D cancer oncolytic Virotherapy mathematical model: Pspice and FPGA implementations

Kabong Nono,

Megam Ngouonkadi

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

show abstract

“…The construction of a mathematical model that describes a natural phenomenon is not an easy task, but scenarios that such a model can present are very important [17]. Usually, the closer we get to a better description of a real problem, the greater are the number of variables involved and the complexity of the equations [21].…”

Section: Introductionmentioning

confidence: 99%

Stability analysis of a fractional virotherapy model for cancer treatment

2022

View full text Add to dashboard Cite

show abstract

“…Due to the complexity of the tumour microenvironment, which makes it difficult to understand the interactions between the different components of this environment, mathematical models have been used over the last few decades to answer various questions about these interactions. The great majority of these models are single-scale models, which focus on spatial tumour invasion [5,16,12], on tumour oncolytic therapies [11,14,13,17,21,28,42], or both [6,24,27,38,43]. More recently, various multi-scale mathematical models have been derived to reproduce and investigate biological processes that take place at different spatial scales [1,2,3,4,30,31,39,36].…”

Section: Introductionmentioning

confidence: 99%

Nonlocal multiscale modelling of tumour-oncolytic viruses interactions within a heterogeneous fibrous/non-fibrous extracellular matrix

Alsisi¹,

Eftimie²,

Trucu³

2021

Preprint

View full text Add to dashboard Cite

In this study we investigate computationally tumour-oncolytic virus (OV) interactions that take place within a heterogeneous ExtraCellular Matrix (ECM). The ECM is viewed as a mixture of two constitutive phases, namely a fibre phase and a non-fibre phase. The multiscale mathematical model presented here focuses on the nonlocal cell-cell and cell-ECM interactions, and how these interactions might be impacted by the infection of cancer cells with the OV. At macroscale we track the kinetics of cancer cells, virus particles and the ECM. At microscale we track (i) the degradation of ECM by matrix degrading enzymes (MDEs) produced by cancer cells, which further influences the movement of tumour boundary; (ii) the re-arrangement of the microfibres that influences the re-arrangement of macrofibres (i.e., fibres at macroscale). With the help of this new multiscale model, we investigate two questions: (i) whether the infected cancer cell fluxes are the result of local or non-local advection in response to ECM density; and (ii) what is the effect of ECM fibres on the the spatial spread of oncolytic viruses and the outcome of oncolytic virotherapy.

show abstract

Mathematical Modeling of Oncolytic Virotherapy

Cited by 9 publications

References 71 publications

Bistability and chaotic behaviors in a 4D cancer oncolytic Virotherapy mathematical model: Pspice and FPGA implementations

Bistability and chaotic behaviors in a 4D cancer oncolytic Virotherapy mathematical model: Pspice and FPGA implementations

Stability analysis of a fractional virotherapy model for cancer treatment

Nonlocal multiscale modelling of tumour-oncolytic viruses interactions within a heterogeneous fibrous/non-fibrous extracellular matrix

Contact Info

Product

Resources

About