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Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation.

ABSTRACT: The concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. However, the original definition of dynamical compensation amounts to lack of structural identifiability. This is relevant if model parameters need to be estimated, as is often the case in biological modelling. Care should we taken when using an unidentifiable model to extract biological insight: the estimated values of structurally unidentifiable parameters are meaningless, and model predictions about unmeasured state variables can be wrong. Taking this into account, we explore alternative definitions of dynamical compensation that do not necessarily imply structural unidentifiability. Accordingly, we show different ways in which a model can be made identifiable while exhibiting dynamical compensation. Our analyses enable the use of the new concept of dynamical compensation in the context of parameter identification, and reconcile it with the desirable property of structural identifiability.

SUBMITTER: Villaverde AF

PROVIDER: S-EPMC5724898 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation.

Villaverde Alejandro F AF Banga Julio R JR

PLoS computational biology 20171129 11

The concept of dynamical compensation has been recently introduced to describe the ability of a biological system to keep its output dynamics unchanged in the face of varying parameters. However, the original definition of dynamical compensation amounts to lack of structural identifiability. This is relevant if model parameters need to be estimated, as is often the case in biological modelling. Care should we taken when using an unidentifiable model to extract biological insight: the estimated v ...[more]

PMID: 29186132

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Project description:The recent coronavirus disease (COVID-19) outbreak has dramatically increased the public awareness and appreciation of the utility of dynamic models. At the same time, the dissemination of contradictory model predictions has highlighted their limitations. If some parameters and/or state variables of a model cannot be determined from output measurements, its ability to yield correct insights – as well as the possibility of controlling the system – may be compromised. Epidemic dynamics are commonly analysed using compartmental models, and many variations of such models have been used for analysing and predicting the evolution of the COVID-19 pandemic. In this paper we survey the different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information. We address the problem using the control theoretic concepts of structural identifiability and observability. Since some parameters can vary during the course of an epidemic, we consider both the constant and time-varying parameter assumptions. We analyse the structural identifiability and observability of all of the models, considering all plausible choices of outputs and time-varying parameters, which leads us to analyse 255 different model versions. We classify the models according to their structural identifiability and observability under the different assumptions and discuss the implications of the results. We also illustrate with an example several alternative ways of remedying the lack of observability of a model. Our analyses provide guidelines for choosing the most informative model for each purpose, taking into account the available knowledge and measurements. Highlights • Structural identifiability and observability are desirable model properties.• They describe a model’s ability to inform about unmeasured parameters and states.• We collect and analyse hundreds of compartmental models of the COVID-19 pandemics.• We show which parameters and states can be determined from output measurements.• We discuss how to choose the most informative model for the available knowledge.

Dataset Information

Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation.

Publications

Dynamical compensation and structural identifiability of biological models: Analysis, implications, and reconciliation.

Similar Datasets

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