A Bayesian statistical analysis of stochastic phenotypic plasticity model of cancer cells

Zhou, Da; Mao, Shanjun; Cheng, Jing; Chen, Kaiyi; Cao, Xiaofang; Hu, Jie

doi:10.1016/j.jtbi.2018.05.031

Cited by 7 publications

(4 citation statements)

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“…More attention should be paid to stochastic model, which is not an alternative to deterministic model but a more complete description [33,34,35]. As a supplement to statistical average, the fluctuation around average has been proved to be very important information for model comparison and selection [26]. Besides, since variance is very sensitive to division pattern and mutation rate, it can also be used to develop efficient parameter estimation method combining the stochastic stem cell model and high-resolution experimental data, which would be of great value in future researches.…”

Section: Discussionmentioning

confidence: 99%

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Stochastic stem cell models with mutation: A comparison of asymmetric and symmetric divisions

Wang

et al. 2020

Preprint

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In order to fulfill cell proliferation and differentiation through cellular hierarchy, stem cells can undergo either asymmetric or symmetric divisions. Recent studies pay special attention to the effect of different modes of stem cell division on the lifetime risk of cancer, and report that symmetric division is more beneficial to delay the onset of cancer. The fate uncertainty of symmetric division is considered to be the reason for the cancer-delaying effect. In this paper we compare asymmetric and symmetric divisions of stem cells via studying stochastic stem cell models with mutations. Specially, by using rigorous mathematical analysis we find that both asymmetric and symmetric models show the same statistical average, but symmetric model shows higher fluctuation than asymmetric model. We further show that the difference between the two models would be more remarkable for lower mutation rates. Our work quantifies the uncertainty of cell division and highlights the significance of stochasticity for distinguishing between different modes of stem cell division.

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Section: Discussionmentioning

confidence: 99%

“…In order to model cellular hierarchies driven by different stem cell division patterns, we employ a compartment model framework compose of stem cell (type A) and non-stem cell (type B) [24,25,26]. Initially there are N wild-type stem cells in the population.…”

Section: Modelsmentioning

confidence: 99%

Stochastic stem cell models with mutation: A comparison of asymmetric and symmetric divisions

Wang

et al. 2020

Preprint

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“…This patient-specific method can be applied to many types of tumors, and provides an estimate of the CSC fraction to rationalize the optimal therapy in a clinical setting [68]. Zhou et al [69] applied a statistical approach to compute the transition rate between CSC and differentiated cells in colon cancer cells and showed phenotypic plasticity with back and forth transitions [69]. Furthermore, Yu et al [70] gathered the differential response of CSCs and differentiated cells to radiotherapy for different tumor types including glioblastoma, lung, prostate, and breast cancer, and fitted this tumor-specific information with a stochastic mathematical model to explain the different inter-tumor responses to radiation therapy [70].…”

Section: Csc Tumor Progression and Therapy: From Modeling To The Clinicmentioning

confidence: 99%

Deciphering the Dynamics of Epithelial-Mesenchymal Transition and Cancer Stem Cells in Tumor Progression

et al. 2019

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Purpose of review:The Epithelial-Mesenchymal Transition (EMT) and the generation of Cancer Stem Cells (CSCs) are two fundamental aspects contributing to tumor growth, acquisition of resistance to therapy, formation of metastases, and tumor relapse. Recent experimental data identifying the circuits regulating EMT and CSCs has driven the development of computational models capturing the dynamics of these circuits, and consequently various aspects of tumor progression. Recent findings:We review the contribution made by these models in a) recapitulating experimentally observed behavior, b) making experimentally testable predictions, and c) driving emerging notions in the field, including the emphasis on the aggressive potential of hybrid epithelial/mesenchymal (E/M) phenotype(s). We discuss dynamical and statistical models at intracellular and population level relating to dynamics of EMT and CSCs, and those focusing on interconnections between these two processes. Summary:These models highlight the insights gained via mathematical modeling approaches, and emphasizes that the connections between hybrid E/M phenotype(s) and stemness can be explained by analyzing underlying regulatory circuits. Such experimentally curated models have the potential of serving as platforms for better therapeutic design strategies.Recent studies have made significant progress in identifying the molecular networks regulating EMT, CSCs, and their interconnections [24]. These networks are formidably complex, and capable to give rise to emergent non-linear behavior. Identification of these networks has driven a surge in deciphering their underlying principles from a dynamical systems perspective. This approach has Reconstructing EMT plasticity from experiments: data-driven approaches to EMTRecent experimental techniques are capable of generating large and high-throughout ('omics' level) data. This deluge has driven a class of data-driven, or 'top-down', models, which employ a variety of statistical tools to reconstruct correlations among genes and develop expression signatures of different EMT phenotypes.

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“…Currently, in various application domains there is a need to solve practical problems associated with modeling and assessing the state of objects, characterized by the following features: -the presence of a large number of inputs, outputs and states; -different types of parameters; -uncertainty, fuzziness and incompleteness of data at the input of the object; -fuzzy severity of the structure. Traditional approaches based on mathematical statistics [1,2] or simulation modeling [3,4] do not allow building adequate models of objects in the indicated conditions. Therefore, at present, artificial intelligence methods are used when solving many problems related to the assessment of the state of objects [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Neural Network Model for Rules Generating of the Objects State Determining in Uncertainty

Ismagilov¹

2018

helix

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This article solves the problem of modelling a fuzzy neural network to generate fuzzy rules and to use them for determining the state of objects in conditions of uncertainty. Traditional methods of mathematical statistics do not allow constructing adequate models of objects under the specified conditions. Therefore, at present, the solution of many problems is based on the use of data mining technologies using the methods of fuzzy logic. The traditional approach to the construction of fuzzy systems is associated with the need to involve an expert to formulate fuzzy rules. To eliminate this drawback, the automation of generating fuzzy rules based on machine learning methods is relevant. One of the approaches to solving this problem is to build a fuzzy neural network and instruction in it based on the data characterizing the object under study. The implementation of this approach required the choice of the type of fuzzy rules, taking into account the peculiarities of the processed data. In addition, the development of an algorithm of logic rule-based inference was required. The stages of the algorithm determine the number of layers in a fuzzy neural network and their functionality. An algorithm for learning a fuzzy neural network has been developed, as a result of which a system of fuzzy-production rules is built. In order to assess the effectiveness of the developed model of a fuzzy neural network, a software package has been implemented. On its basis, the studies on assessment of the classifying ability of the generated rules using the example of data analysis from the UCI Machine Learning Repository have been conducted. The research results show that the classifying ability of fuzzy rules is not inferior to other classification methods in accuracy. In addition, the inferencing algorithm based on the rules makes it possible to successfully perform the classification in the absence of some of the source data. This proves the significance of the obtained results and the effectiveness of the proposed approach for solving the problem of instance state estimate in conditions of fuzziness, heterogeneity and the lack of a part of the analyzed data.

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A Bayesian statistical analysis of stochastic phenotypic plasticity model of cancer cells

Cited by 7 publications

References 45 publications

Stochastic stem cell models with mutation: A comparison of asymmetric and symmetric divisions

Stochastic stem cell models with mutation: A comparison of asymmetric and symmetric divisions

Deciphering the Dynamics of Epithelial-Mesenchymal Transition and Cancer Stem Cells in Tumor Progression

Fuzzy Neural Network Model for Rules Generating of the Objects State Determining in Uncertainty

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