A Predictive Reaction-Diffusion Based Model of <i>E.coli</i>Colony Growth Control

He, Changhan; Bayakhmetov, Samat; Harris, Duane C.; Kuang, Yang; Wang, Xiao

doi:10.1109/lcsys.2020.3046612

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…One example of the use of this simple model is by Claret et al [ 20 ]. In clinical settings, estimates of cancer growth are available over time, either by direct methods, such as imaging or indirect estimates from tumor proxy [ 21 , 22 , 23 ]. The clinical data would then come in pairs of

, where

is the cancer population measured at time

.…”

Section: Introductionmentioning

confidence: 99%

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Phan

Bennett

Patten

2023

Life

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Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.

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