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    Combined State and Parameter Identifiability for a Model of Drug-Resistant Cancer Dynamics

    Source: Journal of Dynamic Systems, Measurement, and Control:;2021:;volume( 143 ):;issue: 011::page 0111005-1
    Author:
    Doosthosseini, Mahsa
    ,
    Fathy, Hosam
    DOI: 10.1115/1.4051646
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This article analyzes the combined parameter and state identifiability for a model of a cancerous tumor's growth dynamics. The model describes the impact of drug administration on the growth of two populations of cancer cells: a drug-sensitive population and a drug-resistant population. The model's dynamic behavior depends on the underlying values of its state variables and parameters, including the initial sizes and growth rates of the drug-sensitive and drug-resistant populations, respectively. The article's primary goal is to use Fisher identifiability analysis to derive and analyze the Cramér–Rao theoretical bounds on the best-achievable accuracy with which this estimation can be performed locally. This extends previous work by the authors, which focused solely on state estimation accuracy. This analysis highlights two key scenarios where estimation accuracy is particularly poor. First, a critical drug administration rate exists where the model's state observability is lost, thereby making the independent estimation of the drug-sensitive and drug-resistant population sizes impossible. Second, a different critical drug administration rate exists that brings the overall growth rate of the drug-sensitive population to zero, thereby worsening model parameter identifiability.
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      Combined State and Parameter Identifiability for a Model of Drug-Resistant Cancer Dynamics

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    contributor authorDoosthosseini, Mahsa
    contributor authorFathy, Hosam
    date accessioned2022-02-06T05:27:06Z
    date available2022-02-06T05:27:06Z
    date copyright7/28/2021 12:00:00 AM
    date issued2021
    identifier issn0022-0434
    identifier otherds_143_11_111005.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4278052
    description abstractThis article analyzes the combined parameter and state identifiability for a model of a cancerous tumor's growth dynamics. The model describes the impact of drug administration on the growth of two populations of cancer cells: a drug-sensitive population and a drug-resistant population. The model's dynamic behavior depends on the underlying values of its state variables and parameters, including the initial sizes and growth rates of the drug-sensitive and drug-resistant populations, respectively. The article's primary goal is to use Fisher identifiability analysis to derive and analyze the Cramér–Rao theoretical bounds on the best-achievable accuracy with which this estimation can be performed locally. This extends previous work by the authors, which focused solely on state estimation accuracy. This analysis highlights two key scenarios where estimation accuracy is particularly poor. First, a critical drug administration rate exists where the model's state observability is lost, thereby making the independent estimation of the drug-sensitive and drug-resistant population sizes impossible. Second, a different critical drug administration rate exists that brings the overall growth rate of the drug-sensitive population to zero, thereby worsening model parameter identifiability.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleCombined State and Parameter Identifiability for a Model of Drug-Resistant Cancer Dynamics
    typeJournal Paper
    journal volume143
    journal issue11
    journal titleJournal of Dynamic Systems, Measurement, and Control
    identifier doi10.1115/1.4051646
    journal fristpage0111005-1
    journal lastpage0111005-11
    page11
    treeJournal of Dynamic Systems, Measurement, and Control:;2021:;volume( 143 ):;issue: 011
    contenttypeFulltext
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