Stochasticity and Drug Effects in Dynamical Model for Cancer Stem Cells
The Cancer Stem Model allows for a dynamical description of cancer colonies which accounts for the existence of different families of cells, namely stem cells, highly proliferating and quasi-immortal, and differentiated cells, both undergoing cellular processes under numerous activated pathways. In...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/15/3/677 |
| _version_ | 1797625017071042560 |
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| author | Ludovico Mori Martine Ben Amar |
| author_facet | Ludovico Mori Martine Ben Amar |
| author_sort | Ludovico Mori |
| collection | DOAJ |
| description | The Cancer Stem Model allows for a dynamical description of cancer colonies which accounts for the existence of different families of cells, namely stem cells, highly proliferating and quasi-immortal, and differentiated cells, both undergoing cellular processes under numerous activated pathways. In the present work, we investigate a dynamical model numerically, as a system of coupled differential equations, and include a plasticity mechanism, of differentiated cells turning into a stem state if the stem concentration drops low. We are particularly interested in the stability of the model once we introduce stochastically evolving parameters, associated with environmental and cellular intrinsic variabilities, as well as the response of the model after introducing a drug therapy. As long as we stay within the characteristic time scale of the system, defined on the base of the needed time for the trajectories to converge on stable states, we observe that the system remains stable for the main parameters evolving stochastically according to white noise. As for the drug treatments, we discuss a model both for the kinetics and the dynamics of the substance in the organism, and then consider the impact of different types of therapies in a few particular examples, outlining some interesting mechanisms, such as the tumor growth paradox, that possibly impact the outcome of therapy significantly. |
| first_indexed | 2024-03-11T09:50:52Z |
| format | Article |
| id | doaj.art-3da414f301284e73b5f1eb295b5d63f6 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-11T09:50:52Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-3da414f301284e73b5f1eb295b5d63f62023-11-16T16:15:37ZengMDPI AGCancers2072-66942023-01-0115367710.3390/cancers15030677Stochasticity and Drug Effects in Dynamical Model for Cancer Stem CellsLudovico Mori0Martine Ben Amar1Laboratoire de Physique de l’Ecole Normale Supérieure, Ecole Normale Supérieure, Université PSL, CNRS, 75005 Paris, FranceLaboratoire de Physique de l’Ecole Normale Supérieure, Ecole Normale Supérieure, Université PSL, CNRS, 75005 Paris, FranceThe Cancer Stem Model allows for a dynamical description of cancer colonies which accounts for the existence of different families of cells, namely stem cells, highly proliferating and quasi-immortal, and differentiated cells, both undergoing cellular processes under numerous activated pathways. In the present work, we investigate a dynamical model numerically, as a system of coupled differential equations, and include a plasticity mechanism, of differentiated cells turning into a stem state if the stem concentration drops low. We are particularly interested in the stability of the model once we introduce stochastically evolving parameters, associated with environmental and cellular intrinsic variabilities, as well as the response of the model after introducing a drug therapy. As long as we stay within the characteristic time scale of the system, defined on the base of the needed time for the trajectories to converge on stable states, we observe that the system remains stable for the main parameters evolving stochastically according to white noise. As for the drug treatments, we discuss a model both for the kinetics and the dynamics of the substance in the organism, and then consider the impact of different types of therapies in a few particular examples, outlining some interesting mechanisms, such as the tumor growth paradox, that possibly impact the outcome of therapy significantly.https://www.mdpi.com/2072-6694/15/3/677cancer stem cellstumor heterogeneitystochasticityplasticityactivatorinhibitor |
| spellingShingle | Ludovico Mori Martine Ben Amar Stochasticity and Drug Effects in Dynamical Model for Cancer Stem Cells Cancers cancer stem cells tumor heterogeneity stochasticity plasticity activator inhibitor |
| title | Stochasticity and Drug Effects in Dynamical Model for Cancer Stem Cells |
| title_full | Stochasticity and Drug Effects in Dynamical Model for Cancer Stem Cells |
| title_fullStr | Stochasticity and Drug Effects in Dynamical Model for Cancer Stem Cells |
| title_full_unstemmed | Stochasticity and Drug Effects in Dynamical Model for Cancer Stem Cells |
| title_short | Stochasticity and Drug Effects in Dynamical Model for Cancer Stem Cells |
| title_sort | stochasticity and drug effects in dynamical model for cancer stem cells |
| topic | cancer stem cells tumor heterogeneity stochasticity plasticity activator inhibitor |
| url | https://www.mdpi.com/2072-6694/15/3/677 |
| work_keys_str_mv | AT ludovicomori stochasticityanddrugeffectsindynamicalmodelforcancerstemcells AT martinebenamar stochasticityanddrugeffectsindynamicalmodelforcancerstemcells |