GenSSI: a software toolbox for structural identifiability analysis of biological models

Chis, O.; Banga, Julio R.; Balsa‐Canto, Eva

doi:10.1093/bioinformatics/btr431

Cited by 114 publications

(111 citation statements)

References 9 publications

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“…If so, then there exists a unique dependency between the Lie derivative and a parameter. Thus, the corresponding parameter is structurally identifiable [9,28].…”

Section: Generating Series Approachmentioning

confidence: 99%

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Combined Estimation and Optimal Control of Batch Membrane Processes

et al. 2016

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Abstract:In this paper, we deal with the model-based time-optimal operation of a batch diafiltration process in the presence of membrane fouling. Membrane fouling poses one of the major problems in the field of membrane processes. We model the fouling behavior and estimate its parameters using various methods. Least-squares, least-squares with a moving horizon, recursive least-squares methods and the extended Kalman filter are applied and discussed for the estimation of the fouling behavior on-line during the process run. Model-based optimal non-linear control coupled with parameter estimation is applied in a simulation case study to show the benefits of the proposed approach.

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“…If so, then there exists a unique dependency between the Lie derivative and a parameter. Thus, the corresponding parameter is structurally identifiable [9,28].…”

Section: Generating Series Approachmentioning

confidence: 99%

“…In the case of the detection of non-identifiability, GenSSI also provides information on individual parameters and their global or local identifiability. This information can guide the proper reformulation of the model [9,28].…”

Section: Software Tools For the Identifiability Testmentioning

confidence: 99%

Combined Estimation and Optimal Control of Batch Membrane Processes

et al. 2016

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“…We focus our paper on a self-contained discussion, with examples, of the fact that DC is equivalent to other known concepts. There is, in addition, an extensive literature on parameter identifiability, which may be consulted for more details, mathematical theory, and effective algorithms based on computer algebra methods [4,5,6,7,8,9,10,11,12,13,14,15,16]. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Dynamic compensation, parameter identifiability, and equivariances

Sontag

2016

Preprint

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A recent paper by Karin et al. introduced a mathematical notion called dynamical compensation (DC) of biological circuits. DC was shown to play an important role in glucose homeostasis as well as other key physiological regulatory mechanisms. Karin et al. went on to provide a sufficient condition to test whether a given system has the DC property. Here, we show how DC can be formulated in terms of a well-known concept in systems biology, statistics, and control theory-that of parameter structural non-identifiability. Viewing DC as a parameter identification problem enables one to take advantage of powerful theoretical and computational tools to test a system for DC. We obtain as a special case the sufficient criterion discussed by Karin et al. We also draw connections to system equivalence and to the foldchange detection property. Author summaryA recently introduced mathematical notion called dynamical compensation of biological circuits was shown to play an important role in glucose homeostasis and other key physiological regulatory mechanisms. This paper explains how dynamical compensation can be formulated in terms of a well-known concept in systems biology, statistics, and control theory-that of parameter structural non-identifiability. Viewing dynamical compensation as a parameter identification problem enables one to take advantage of powerful theoretical and computational tools to test a system for dynamical compensation. As a special case, one obtains the sufficient criterion for dynamical compensation. The paper also draws connections to system equivalence and to the fold-change detection property. The non-identifiability characterization brings up an interesting contrast in the way in which one thinks of these properties in the two fields. From the point of view of robustness of behavior, one wishes that parameters do not influence much the response of a system. On the other hand, from the systems and parameter identification point of view, the more that a parameter affects behavior, the easier it is to estimate it, and poor sensitivity is taken as an indication of a poorly parametrized model.

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“…In classical control theory, this concept is essentially related to the notion of persistence of excitation (Ljung, 1999); unfortunately, limitations in the variety and quality of the data make this notion inapplicable for biological applications. In recent years, the topic of identifiability has gained considerable interest in the field of systems biology (Chen et al, 2010;Chis et al, 2011b;Nikerel et al, 2009;Raue et al, 2009Raue et al, , 2011Gutenkunst et al, 2007;Nemcova, 2010;Srinath and Gunawan, 2010;Voit et al, 2006a) and several specialized software packages have been developed to support the modeler (Bellu et al, 2007;Chis et al, 2011a;Maiwald and Timmer, 2008). Despite these efforts, however, no common agreement on definitions and links between structural and practical identifiability exist to date.…”

Section: Introductionmentioning

confidence: 99%

On the identifiability of metabolic network models

et al. 2012

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A major problem for the identification of metabolic network models is parameter identifiability. The identifiability of a parameter consists in the possibility to unambiguously infer the parameter from the data. Identifiability problems may be due to the structure of the model, in particular implicit dependencies between the parameters, or to limitations in the quantity and quality of the available data. We address the detection and resolution of identifiability problems for a class of pseudo-linear models of metabolism, so-called linlog models.Linlog models have the advantage that parameter estimation reduces to linear or orthogonal regression, which facilitates the analysis of identifiability. We develop precise definitions of structural and practical identifiability, and clarify the fundamental relations between these concepts. In addition, we use Singular Value Decomposition (SVD) to detect identifiability problems and reduce the model to an identifiable approximation by a Principal Component Analysis (PCA) approach. The criterion is adapted to real data, which are frequently scarce, incomplete, and noisy. The test of the criterion on a model with simulated data shows that it is capable of correctly identifying the principal components of the data vector. The application to a state-of-the-art dataset on central carbon metabolism in Escherichia coli yields the surprising result that only 4 out of 31 reactions, and 37 out of 100 parameters, are identifiable. This underlines the practical importance of identifiability analysis and model reduction in the modeling of large-scale metabolic networks. Although our approach has been developed in the context of linlog models, it carries over to other pseudo-linear models and provides useful hints for the identifiability analysis of more general classes of nonlinear models of metabolism.

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GenSSI: a software toolbox for structural identifiability analysis of biological models

Cited by 114 publications

References 9 publications

Combined Estimation and Optimal Control of Batch Membrane Processes

Combined Estimation and Optimal Control of Batch Membrane Processes

Dynamic compensation, parameter identifiability, and equivariances

On the identifiability of metabolic network models

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