Cause and cure of sloppiness in ordinary differential equation models

Tönsing, Christian; Timmer, Jens; Kreutz, Clemens

doi:10.1103/physreve.90.023303

Cited by 35 publications

(40 citation statements)

References 44 publications

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“…This point has so far been controversial. On the one hand, a number of researchers have expressed critical or sceptical views about the utility of the concept [2,24,43,48]. On the other hand, it has been argued that sloppiness can not only explain why tools such as principal component analysis (PCA) and the LevenbergMarquardt algorithm are effective, but also that it is the phenomenon that enables biological evolution; and, ultimately, that sloppiness is the reason why science is possible and the universe is comprehensible [46].…”

Section: Discussionmentioning

confidence: 99%

“…In [43] the origin of sloppiness was traced back to the structure of the sensitivity matrix, which contains the sensitivities of the model outputs with respect to the parameters. Experimental design was proposed as a way of reducing sloppiness, concluding that the intensity of the effect is highly dependent on the available data, thus challenging the universality of the property.…”

Section: Introductionmentioning

confidence: 99%

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On the relationship between sloppiness and identifiability

Chis

Villaverde

Banga

et al. 2016

Mathematical Biosciences

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Dynamic models of biochemical networks are often formulated as sets of non-linear ordinary differential equations, whose states are the concentrations or abundances of the network components. They typically have a large number of kinetic parameters, which must be determined by calibrating the model with experimental data. In recent years it has been suggested that dynamic systems biology models are universally sloppy, meaning that the values of some parameters can be perturbed by several orders of magnitude without causing significant changes in the model output. This observation has prompted calls for focusing on model predictions rather than on parameters. In this work we examine the concept of sloppiness, investigating its links with the long-established notions of structural and practical identifiability. By analysing a set of case studies we show that sloppiness is not equivalent to lack of identifiability, and that sloppy models can be identifiable. Thus, using sloppiness to draw conclusions about the possibility of estimating parameter values can be misleading. Instead, structural and practical identifiability analyses are better tools for assessing the confidence in parameter estimates. Furthermore, we show that, when designing new experiments to decrease parametric uncertainty, designs that optimize practical identifiability criteria are more informative than those that minimize sloppiness.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the relationship between sloppiness and identifiability

Chis

Villaverde

Banga

et al. 2016

Mathematical Biosciences

View full text Add to dashboard Cite

show abstract

“…Sloppiness provides a viewpoint for studying how distinguishable models are, and how they can be reduced. Several papers have clarified the relation between sloppiness and identifiability [3,13,51,62]. It is now understood that sloppiness is related to practical rather than structural identifiability, and that it is not equivalent to unidentifiability of any kind, meaning that sloppy models can indeed be identifiable.…”

Section: Sloppiness Dynamical Compensation and Structural Identifiamentioning

confidence: 99%

Observability and Structural Identifiability of Nonlinear Biological Systems

Villaverde¹

2019

Complexity

105

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Observability is a modelling property that describes the possibility of inferring the internal state of a system from observations of its output. A related property, structural identifiability, refers to the theoretical possibility of determining the parameter values from the output. In fact, structural identifiability becomes a particular case of observability if the parameters are considered as constant state variables. It is possible to simultaneously analyse the observability and structural identifiability of a model using the conceptual tools of differential geometry. Many complex biological processes can be described by systems of nonlinear ordinary differential equations, and can therefore be analysed with this approach. The purpose of this review article is threefold: (I) to serve as a tutorial on observability and structural identifiability of nonlinear systems, using the differential geometry approach for their analysis; (II) to review recent advances in the field; and (III) to identify open problems and suggest new avenues for research in this area.

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“…In addition to not being able to obtain good solutions, parameter estimation can lead to entire domains of combinations of parameter values that yield essentially equivalent solutions. This issue of sloppiness has been discussed frequently in recent times (e.g., Refs ). Thus, in some cases, there are no good solutions, and in other cases, there are arguably too many.…”

Section: Step 4: Estimation Of Parameter Values For the Process Reprementioning

confidence: 99%

The best models of metabolism

Voit

2017

WIREs Mechanisms of Disease

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Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models.

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Cause and cure of sloppiness in ordinary differential equation models

Cited by 35 publications

References 44 publications

On the relationship between sloppiness and identifiability

On the relationship between sloppiness and identifiability

Observability and Structural Identifiability of Nonlinear Biological Systems

The best models of metabolism

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