Benchmarking tools for a priori identifiability analysis

Barreiro, Xabier Rey; Villaverde, Alejandro F.

doi:10.1093/bioinformatics/btad065

Cited by 25 publications

(20 citation statements)

References 55 publications

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“…In such a situation, we will be unable to estimate the value of the individual parameters irrespective of the number of measurements. Tools to assess structural identifiability of ODEs are reviewed in [36], including DAISY [37], GENSSI2 [38] and the StructuralIdentifiability Julia package [39].…”

Section: Introductionmentioning

confidence: 99%

Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences

Murphy,

Maclaren,

Simpson

2024

J. R. Soc. Interface.

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Throughout the life sciences, we routinely seek to interpret measurements and observations using parametrized mechanistic mathematical models. A fundamental and often overlooked choice in this approach involves relating the solution of a mathematical model with noisy and incomplete measurement data. This is often achieved by assuming that the data are noisy measurements of the solution of a deterministic mathematical model, and that measurement errors are additive and normally distributed. While this assumption of additive Gaussian noise is extremely common and simple to implement and interpret, it is often unjustified and can lead to poor parameter estimates and non-physical predictions. One way to overcome this challenge is to implement a different measurement error model. In this review, we demonstrate how to implement a range of measurement error models in a likelihood-based framework for estimation, identifiability analysis and prediction, called profile-wise analysis. This frequentist approach to uncertainty quantification for mechanistic models leverages the profile likelihood for targeting parameters and understanding their influence on predictions. Case studies, motivated by simple caricature models routinely used in systems biology and mathematical biology literature, illustrate how the same ideas apply to different types of mathematical models. Open-source Julia code to reproduce results is available on GitHub.

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Section: Introductionmentioning

confidence: 99%

Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences

Murphy,

Maclaren,

Simpson

2024

J. R. Soc. Interface.

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“…In a recent paper this model served as a benchmark problem for an identifiability test and 13 software packages were compared on this particular problem. Results of the comparison showed that seven packages were not even capable to compute a result because of the model's complexity (Barreiro & Villaverde, 2023). From the remaining six packages, the fastest performance was 35.7 s, while the worst performance was 40.7 h. The StrucID package (that was not included in the benchmark paper) was capable of computing the results presented in Figure 5 in approximately 2 s.…”

Section: Discussionmentioning

confidence: 99%

Computing parameter identifiability and other structural properties for natural resource models

Stigter¹

2023

Natural Resource Modeling

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For dynamic natural resource models a few fundamental questions are often omitted in practice such as (i) “Is it possible to calibrate the model?” and (ii) “Can we reconstruct all unknown dynamic state variables given a certain data record?” Other related questions are (iii) “Does the system allow the state variables to be controlled (or managed) to desired optimal values (e.g., in case of maximum sustainable yield)?” In this paper we highlight and discuss a software tool (the StrucID App) that allows a rapid evaluation of these fundamental and structural model properties that are important to study before a calibration and subsequent simulation of the model.

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Section: Introductionmentioning

confidence: 99%

“…Tools for assessing structural identifiability are well established for deterministic, ordinary differential equation (ODE) models [11][12][13][14]. The advent of open-source and even web-based software [15] to automate the otherwise tedious analysis has ingrained questions related to structural identifiability into the inference process for ODE models [14]. Methods originate with differential-algebra-based approaches [16], which for linear or polynomial systems assess identifiability through a so-called input-output relation: a set of monic polynomials in the derivatives of observed variables, the coefficients of which form the set of identifiable parameter combinations.…”

Section: Introductionmentioning

confidence: 99%

Structural identifiability analysis of linear reaction–advection–diffusion processes in mathematical biology

Browning,

Taşcă,

Falcó

et al. 2024

Proc. R. Soc. A.

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Effective application of mathematical models to interpret biological data and make accurate predictions often requires that model parameters are identifiable. Approaches to assess the so-called structural identifiability of models are well established for ordinary differential equation models, yet there are no commonly adopted approaches that can be applied to assess the structural identifiability of the partial differential equation (PDE) models that are requisite to capture spatial features inherent to many phenomena. The differential algebra approach to structural identifiability has recently been demonstrated to be applicable to several specific PDE models. In this brief article, we present general methodology for performing structural identifiability analysis on partially observed reaction–advection–diffusion PDE models that are linear in the unobserved quantities. We show that the differential algebra approach can always, in theory, be applied to such models. Moreover, despite the perceived complexity introduced by the addition of advection and diffusion terms, consideration of spatial analogues of non-spatial models cannot exacerbate structural identifiability. We conclude by discussing future possibilities and the computational cost of performing structural identifiability analysis on more general PDE models.

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Benchmarking tools for a priori identifiability analysis

Cited by 25 publications

References 55 publications

Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences

Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences

Computing parameter identifiability and other structural properties for natural resource models

Structural identifiability analysis of linear reaction–advection–diffusion processes in mathematical biology

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