STRIKE-GOLDD 4.0: user-friendly, efficient analysis of structural identifiability and observability

Díaz-Seoane, Sandra; Barreiro, Xabier Rey; Villaverde, Alejandro F.

doi:10.1093/bioinformatics/btac748

Cited by 18 publications

(15 citation statements)

References 14 publications

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“… Massonis et al (2021) applied it to a NF- κ B model of 30 variables; applying it to larger and/or more complex models can be challenging. However, it should be noted that the algorithmic improvements included in recent versions of the STRIKE-GOLDD toolbox have reduced its computational cost ( Díaz-Seoane et al , 2022 ). The other steps in our procedure have better scalability, and they can be applied to models with hundreds of variables, as shown in Villaverde et al (2022) .…”

Section: Discussionmentioning

confidence: 99%

Improving dynamic predictions with ensembles of observable models

2022

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Motivation Dynamic mechanistic modelling in systems biology has been hampered by the complexity and variability associated with the underlying interactions, and by uncertain and sparse experimental measurements. Ensemble modelling, a concept initially developed in statistical mechanics, has been introduced in biological applications with the aim of mitigating those issues. Ensemble modelling uses a collection of different models compatible with the observed data to describe the phenomena of interest. However, since systems biology models often suffer from lack of identifiability and observability, ensembles of models are particularly unreliable when predicting non-observable states. Results We present a strategy to assess and improve the reliability of a class of model ensembles. In particular, we consider kinetic models described using ordinary differential equations (ODEs) with a fixed structure. Our approach builds an ensemble with a selection of the parameter vectors found when performing parameter estimation with a global optimization metaheuristic. This technique enforces diversity during the sampling of parameter space and it can quantify the uncertainty in the predictions of state trajectories. We couple this strategy with structural identifiability and observability analysis, and when these tests detect possible prediction issues we obtain model reparameterizations that surmount them. The end result is an ensemble of models with the ability to predict the internal dynamics of a biological process. We demonstrate our approach with models of glucose regulation, cell division, circadian oscillations, and the JAK-STAT signalling pathway. Availability The code that implements the methodology and reproduces the results is available at https://doi.org/10.5281/zenodo.6782638 Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Discussionmentioning

confidence: 99%

Improving dynamic predictions with ensembles of observable models

2022

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“…We have implemented both methods in the Matlab toolbox STRIKE-GOLDD [ 22 ]. In the analyses reported in this paper, we have used preferentially the second, most efficient algorithm.…”

Section: Methodsmentioning

confidence: 99%

Structural Identifiability and Observability of Microbial Community Models

2023

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Biological communities are populations of various species interacting in a common location. Microbial communities, which are formed by microorganisms, are ubiquitous in nature and are increasingly used in biotechnological and biomedical applications. They are nonlinear systems whose dynamics can be accurately described by models of ordinary differential equations (ODEs). A number of ODE models have been proposed to describe microbial communities. However, the structural identifiability and observability of most of them—that is, the theoretical possibility of inferring their parameters and internal states by observing their output—have not been determined yet. It is important to establish whether a model possesses these properties, because, in their absence, the ability of a model to make reliable predictions may be compromised. Hence, in this paper, we analyse these properties for the main families of microbial community models. We consider several dimensions and measurements; overall, we analyse more than a hundred different configurations. We find that some of them are fully identifiable and observable, but a number of cases are structurally unidentifiable and/or unobservable under typical experimental conditions. Our results help in deciding which modelling frameworks may be used for a given purpose in this emerging area, and which ones should be avoided.

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“…Implementation Both geometric approaches were implemented in the Matlab toolbox STRIKE_GOLDD 4.0 [25]. The toolbox allows to assess local observability (and structural local identifiability) with the FISPO and ORC-DF algorithms.…”

Section: Geometric Approachmentioning

confidence: 99%

“…The full inhibition function I ac is a major source of nonlinearity in the ADM1-R3. Neglecting model part D (inhibition through pH and ammonia) allows to cut out the first and last factor of I ac , see (25). Consequently, S nh3 can be omitted in the state vector.…”

Section: Neglecting Model Part D -Inhibition Through Ph and Ammoniamentioning

confidence: 99%

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Observability and Identifiability Analyses of Process Models for Agricultural Anaerobic Digestion Plants

Hellmann¹,

Hempel²,

Streif³

et al. 2023

Preprint

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Dynamic operation of anaerobic digestion plants requires advanced process monitoring and control. Different simplifications of the Anaerobic Digestion Model No. 1 (ADM1) have been proposed recently, which appear promising for model-based process automation and state estimation. As a fundamental requirement, observability and identifiability of these models are analyzed in this work, which was pursued through algebraic and geometric analysis. Manual algebraic assessment was successfull for small models such as the ADM1-R4 and simplified versions of the ADM1-R3, which were derived in this context. However, for larger model classes the algebraic approach showed to be insufficient. By contrast, the geometric approach, implemented in the STRIKE_GOLDD toolbox, allowed to show observability for more complex models (including ADM1-R4 and ADM1-R3), employing two independent algorithms. The present study lays the groundwork for state observer design, parameter estimation and advanced control resting upon ADM1-based models.

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STRIKE-GOLDD 4.0: user-friendly, efficient analysis of structural identifiability and observability

Cited by 18 publications

References 14 publications

Improving dynamic predictions with ensembles of observable models

Improving dynamic predictions with ensembles of observable models

Structural Identifiability and Observability of Microbial Community Models

Observability and Identifiability Analyses of Process Models for Agricultural Anaerobic Digestion Plants

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