Mathematical modeling of tumor-induced immunosuppression by myeloid-derived suppressor cells: Implications for therapeutic targeting strategies

Shariatpanahi, Seyed Peyman; Shariatpanahi, Seyed Pooya; Madjidzadeh, K.; Hassan, Manal M.; Abedi‐Valugerdi, Manuchehr

doi:10.1016/j.jtbi.2018.01.006

Cited by 35 publications

(78 citation statements)

References 46 publications

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“…The mathematical model of tumor-immune system interactions of this study was adapted from the model developed by shariatpanhi et al [40]. The structure of model is based on ordinary differential equation that with deterministic rates simulates the biological and biophysical/biochemical behaviors of TIS agents.…”

Section: Structure Of Ode Model Of Tismentioning

confidence: 99%

“…Mathematical modeling widely has been used to investigate the efficacy of different treatment strategies for various cancers. For instance, in a recent study the efficacy of L-arginine and 5-FU therapies for the treatment of lymphoma (El4-luc2 cell line), breast cancer (4T1 cell line) and lung carcinoma (3LL cell line) by a system of ODEs was evaluated [40]. For the same purpose, in another study, combination of radiotherapy and anti-PD-1 therapy by a discreet-time mathematical model was evaluated and temporal dynamics of TIS agents were captured [41].…”

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confidence: 99%

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In Silico Assessment of 5-FU Therapy via A Mathematical Model with Fuzzy Uncertain Parameters

Shafiekhani¹,

Jafari‬²,

Jafarzadeh³

et al. 2020

Preprint

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Abstract Background: Ordinary differential equation (ODE) models widely have been used in mathematical oncology to capture dynamics of tumor and immune cells and evaluate the efficacy of treatments. However, for dynamic models of tumor-immune system (TIS), some parameters are uncertain due to inaccurate, missing or incomplete data, which has hindered the application of ODEs that require accurate parameters. Methods: We extended an available ODE model of TIS interactions via fuzzy logic to illustrate the fuzzification procedure of an ODE model. Fuzzy ODE (FODE) models, in comparison with the stochastic differential equation (SDE) models, assigns a fuzzy number instead of a random number (from a specific probability density function) to the parameters, to capture parametric uncertainty. We used FODE model to predict tumor and immune cells dynamics and assess the efficacy of 5-FU. The present model is configurable for 5-FU chemotherapy injection timing and propose testable hypothesis in vitro/ in vivo experiments. Result: FODE model was used to explore the uncertainty of cells dynamics resulting from parametric uncertainty in presence and absence of 5-FU therapy. In silico experiments revealed that the frequent 5-FU injection created a beneficial tumor microenvironment that exerted detrimental effects on tumor cells by enhancing the infiltration of CD8+ T cells, and NK cells, and decreasing that of myeloid-derived suppressor (MDSC) cells. We investigate the effect of perturbation on model parameters on dynamics of cells through global sensitivity analysis (GSA) and compute correlation between model parameters and cell dynamics. Conclusion: ODE models with fuzzy uncertain kinetic parameters cope with insufficient experimental data in the field of mathematical oncology and can predict cells dynamics uncertainty band. In silico assessment of treatments considering parameter uncertainty and investigating the effect of the drugs on movement of cells dynamics uncertainty band may be more appropriate than in crisp setting.

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Section: Structure Of Ode Model Of Tismentioning

confidence: 99%

mentioning

confidence: 99%

In Silico Assessment of 5-FU Therapy via A Mathematical Model with Fuzzy Uncertain Parameters

Shafiekhani¹,

Jafari‬²,

Jafarzadeh³

et al. 2020

Preprint

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“…While mathematical models of tumor growth have become increasingly complex [27,28], simpler ordinary differential equation (ODE) models can still help provide insight into cancer dynamics. Such ODE models have been used to make predictions about the effectiveness of cancer treatments [29,30], including combination therapies [31,32] and help improve the way efficacy is measured [31,33].…”

Section: Introductionmentioning

confidence: 99%

Understanding the effect of measurement time on drug characterization

2020

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In order to determine correct dosage of chemotherapy drugs, the effect of the drug must be properly quantified. There are two important values that characterize the effect of the drug: ε max is the maximum possible effect of a drug, and IC 50 is the drug concentration where the effect diminishes by half. There is currently a problem with the way these values are measured because they are time-dependent measurements. We use mathematical models to determine how the ε max and IC 50 values depend on measurement time and model choice. Seven ordinary differential equation models (ODE) are used for the mathematical analysis; the exponential, Mendelsohn, logistic, linear, surface, Bertalanffy, and Gompertz models. We use the models to simulate tumor growth in the presence and absence of treatment with a known IC 50 and ε max. Using traditional methods, we then calculate the IC 50 and ε max values over fifty days to show the time-dependence of these values for all seven mathematical models. The general trend found is that the measured IC 50 value decreases and the measured ε max increases with increasing measurement day for most mathematical models. Unfortunately, the measured values of IC 50 and ε max rarely matched the values used to generate the data. Our results show that there is no optimal measurement time since models predict that IC 50 estimates become more accurate at later measurement times while ε max is more accurate at early measurement times.

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“…[ 1 7 ] In situations whereby the quantitative relationship is unknown, computational models are effective alternatives because they can qualitatively and without experimental data or with missing data describe the biological process and predict quantitative responses. [ 4 8 9 10 11 ] Moreover, since they can be nondeterministic or stochastic, they produce outputs with a range of uncertainties which are natural in biological processes. [ 12 13 ] The goal of computational modeling is to comprehend the general properties of complex networks by quantitative or qualitative terms in order to address the structure of cellular networks and modeling subtle dynamics from molecular levels that contribute to biological functions to intracellular levels that show the average behavior of biological systems.…”

Section: Introductionmentioning

confidence: 99%