dorsal/arxiv
View SchemaMathematical modeling of tumor therapy with oncolytic viruses: Effects of parametric heterogeneity on cell dynamics
| Authors | Georgy P. Karev, Artem S. Novozhilov, Eugene V. Koonin |
|---|---|
| Categories | |
| ArXiv ID | q-bio/0607014 |
| URL | https://arxiv.org/abs/q-bio/0607014 |
Abstract
One of the mechanisms that ensure cancer robustness is tumor heterogeneity, and its effects on tumor cells dynamics have to be taken into account when studying cancer progression. There is no unifying theoretical framework in mathematical modeling of carcinogenesis that would account for parametric heterogeneity. Here we formulate a modeling approach that naturally takes stock of inherent cancer cell heterogeneity and illustrate it with a model of interaction between a tumor and an oncolytic virus. We show that several phenomena that are absent in homogeneous models, such as cancer recurrence, tumor dormancy, an others, appear in heterogeneous setting. We also demonstrate that, within the applied modeling framework, to overcome the adverse effect of tumor cell heterogeneity on cancer progression, a heterogeneous population of an oncolytic virus must be used. Heterogeneity in parameters of the model, such as tumor cell susceptibility to virus infection and virus replication rate, can lead to complex, time-dependent behaviors of the tumor. Thus, irregular, quasi-chaotic behavior of the tumor-virus system can be caused not only by random perturbations but also by the heterogeneity of the tumor and the virus. The modeling approach described here reveals the importance of tumor cell and virus heterogeneity for the outcome of cancer therapy. It should be straightforward to apply these techniques to mathematical modeling of other types of anticancer therapy.
{
"annotation_id": "a7125b0c-9067-4c49-9848-559154354d7e",
"date_created": "2026-03-02T18:01:35.328000Z",
"date_modified": "2026-03-02T18:01:35.328000Z",
"file_hash": "a1d1ef2a59622f49f2f569c1e2958d567c8e61a9c998d746823f671eeeaff4a4",
"private": false,
"record": {
"abstract": "One of the mechanisms that ensure cancer robustness is tumor heterogeneity,\nand its effects on tumor cells dynamics have to be taken into account when\nstudying cancer progression. There is no unifying theoretical framework in\nmathematical modeling of carcinogenesis that would account for parametric\nheterogeneity. Here we formulate a modeling approach that naturally takes stock\nof inherent cancer cell heterogeneity and illustrate it with a model of\ninteraction between a tumor and an oncolytic virus. We show that several\nphenomena that are absent in homogeneous models, such as cancer recurrence,\ntumor dormancy, an others, appear in heterogeneous setting. We also demonstrate\nthat, within the applied modeling framework, to overcome the adverse effect of\ntumor cell heterogeneity on cancer progression, a heterogeneous population of\nan oncolytic virus must be used. Heterogeneity in parameters of the model, such\nas tumor cell susceptibility to virus infection and virus replication rate, can\nlead to complex, time-dependent behaviors of the tumor. Thus, irregular,\nquasi-chaotic behavior of the tumor-virus system can be caused not only by\nrandom perturbations but also by the heterogeneity of the tumor and the virus.\nThe modeling approach described here reveals the importance of tumor cell and\nvirus heterogeneity for the outcome of cancer therapy. It should be\nstraightforward to apply these techniques to mathematical modeling of other\ntypes of anticancer therapy.",
"arxiv_id": "q-bio/0607014",
"authors": [
"Georgy P. Karev",
"Artem S. Novozhilov",
"Eugene V. Koonin"
],
"categories": [
"q-bio.TO",
"q-bio.QM"
],
"title": "Mathematical modeling of tumor therapy with oncolytic viruses: Effects of parametric heterogeneity on cell dynamics",
"url": "https://arxiv.org/abs/q-bio/0607014"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "55d8f3dc-5504-4343-8bd2-f8c1d4b90586",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}