On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability

Evangelou, Nikolaos; Wichrowski, Noah J.; Kevrekidis, George; Dietrich, Felix; Kooshkbaghi, Mahdi; McFann, Sarah; Kevrekidis, Ioannis G.

doi:10.1093/pnasnexus/pgac154

Cited by 9 publications

(3 citation statements)

References 39 publications

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“…Depend on the types of model identifiability, there are various examples and techniques to address the issues of model identifiability [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Here, we offer our perspective on this issue.…”

Section: Materials and Methodsmentioning

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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Section: Materials and Methodsmentioning

confidence: 99%

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Phan

Bennett

Patten

2023

Life

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“…However, our model and kinetic data offer a clear test case for data-driven efforts to automate the discovery and estimation of effective parameters in biochemical networks. A recent study took a step in this direction, using low-dimensional description of parameter ensembles generated by local minimization algorithms ( 36 ).…”

Section: Discussionmentioning

confidence: 99%

Phase plane dynamics of ERK phosphorylation

Shvartsman,

McFann,

Wühr

et al. 2023

Journal of Biological Chemistry

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“…Further, they use a special type of auto-encoder known as a Y-shaped conformal auto-encoder to disentangle the unimportant parameter combinations. Finally, the identified effective parameters are mapped back to physical parameters [36].…”

Section: Impact Of Sloppiness In Optimization Experiments Design and ...mentioning

confidence: 99%

Sloppiness: Fundamental study, new formalism and its application in model assessment

Jagadeesan

Raman²,

Tangirala³

2023

PLoS ONE

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Computational modelling of biological processes poses multiple challenges in each stage of the modelling exercise. Some significant challenges include identifiability, precisely estimating parameters from limited data, informative experiments and anisotropic sensitivity in the parameter space. One of these challenges’ crucial but inconspicuous sources is the possible presence of large regions in the parameter space over which model predictions are nearly identical. This property, known as sloppiness, has been reasonably well-addressed in the past decade, studying its possible impacts and remedies. However, certain critical unanswered questions concerning sloppiness, particularly related to its quantification and practical implications in various stages of system identification, still prevail. In this work, we systematically examine sloppiness at a fundamental level and formalise two new theoretical definitions of sloppiness. Using the proposed definitions, we establish a mathematical relationship between the parameter estimates’ precision and sloppiness in linear predictors. Further, we develop a novel computational method and a visual tool to assess the goodness of a model around a point in parameter space by identifying local structural identifiability and sloppiness and finding the most sensitive and least sensitive parameters for non-infinitesimal perturbations. We demonstrate the working of our method in benchmark systems biology models of various complexities. The pharmacokinetic HIV infection model analysis identified a new set of biologically relevant parameters that can be used to control the free virus in an active HIV infection.

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On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability

Cited by 9 publications

References 39 publications

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Phase plane dynamics of ERK phosphorylation

Sloppiness: Fundamental study, new formalism and its application in model assessment

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