Web-Based Structural Identifiability Analyzer

Ilmer, Ilia; Ovchinnikov, Alexey; Pogudin, Gleb

doi:10.1007/978-3-030-85633-5_17

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…The problem of assessing identifiability has been studied since the 1970-s [3]. Since then, a number of different approaches have been proposed (see [11,30] and references therein) and several software packages and webapps have been developed [4,34,38,43]. However, many models of practical interest remain out of reach with the existing tools.…”

Section: Identifiabilitymentioning

confidence: 99%

“…One popular approach to assess identifiability is the input-output relations approach proposed in [44] (recently used, e.g., in [50,58,65]) and relies on theory of differential algebra. It has been implemented in DAISY [4], COMBOS [43], and Structural Identifiability Toolbox [34]. First, the software computes a set of generators of the field of definition F of the ideal of the input-output relations, that is, the minimal subfield of the field of rational functions in parameters sufficient to write down the generators of this ideal [48, Definition 2.5].…”

Section: Identifiabilitymentioning

confidence: 99%

“…The condition is not checked by DAISY or COMBOS. However, it has been checked manually in several case studies [18,61,64] with about a dozen of monomials and can be checked automatically by [34] if the number of monomials does not exceed a hundred.…”

Section: Identifiabilitymentioning

confidence: 99%

“…Note that the last two items above can be used with any other elimination algorithms (e.g., used in [4,34,43]). The resulting algorithm is implemented in Julia and available in the STRUCTURALIDENTIFIABILITY package 1 which is a part of the SciML (scientific machine learning) ecosystem.…”

Section: Probability Of Correctnessmentioning

confidence: 99%

“…However, in the benchmarks we consider in this paper, the number N in Lemma 5.6 can be as large as 2600 (in Example 6.2). For the problems of this size, the algorithms computing the derivatives of the outputs up to this order symbolically (such as in [34,47]) are not efficient enough. We propose Algorithm 7, which, together with the enhancement from Remark 5.8, can compute the rank of a specialization of the Wronskian from Lemma 5.6 in several minutes for all the examples we consider.…”

Section: Consider Any Pointmentioning

confidence: 99%

See 4 more Smart Citations

Differential elimination for dynamical models via projections with applications to structural identifiability

Dong,

Goodbrake,

Harrington

et al. 2021

Preprint

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Elimination of unknowns in a system of differential equations is often required when analysing (possibly nonlinear) dynamical systems models, where only a subset of variables are observable. One such analysis, identifiability, often relies on computing input-output relations via differential algebraic elimination. Determining identifiability, a natural prerequisite for meaningful parameter estimation, is often prohibitively expensive for medium to large systems due to the computationally expensive task of elimination.We propose an algorithm that computes a description of the set of differential-algebraic relations between the input and output variables of a dynamical system model. The resulting algorithm outperforms general-purpose software for differential elimination on a set of benchmark models from literature.We use the designed elimination algorithm to build a new randomized algorithm for assessing structural identifiability of a parameter in a parametric model. A parameter is said to be identifiable if its value can be uniquely determined from input-output data assuming the absence of noise and sufficiently exciting inputs. Our new algorithm allows the identification of models that could not be tackled before.Our implementation is publicly available as a Julia package at https://github.com/SciML/StructuralIdentifiability.jl.

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Section: Identifiabilitymentioning

confidence: 99%

Section: Identifiabilitymentioning

confidence: 99%

Section: Identifiabilitymentioning

confidence: 99%

Section: Probability Of Correctnessmentioning

confidence: 99%

Section: Consider Any Pointmentioning

confidence: 99%

See 3 more Smart Citations

Differential elimination for dynamical models via projections with applications to structural identifiability

Dong,

Goodbrake,

Harrington

et al. 2021

Preprint

Self Cite

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Modeling self-propagating malware with epidemiological models

Chernikova,

Gozzi,

Perra

et al. 2023

Appl Netw Sci

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Self-propagating malware (SPM) is responsible for large financial losses and major data breaches with devastating social impacts that cannot be understated. Well-known campaigns such as WannaCry and Colonial Pipeline have been able to propagate rapidly on the Internet and cause widespread service disruptions. To date, the propagation behavior of SPM is still not well understood. As result, our ability to defend against these cyber threats is still limited. Here, we address this gap by performing a comprehensive analysis of a newly proposed epidemiological-inspired model for SPM propagation, the Susceptible-Infected-Infected Dormant-Recovered (SIIDR) model. We perform a theoretical analysis of the SIIDR model by deriving its basic reproduction number and studying the stability of its disease-free equilibrium points in a homogeneous mixed system. We also characterize the SIIDR model on arbitrary graphs and discuss the conditions for stability of disease-free equilibrium points. We obtain access to 15 WannaCry attack traces generated under various conditions, derive the model’s transition rates, and show that SIIDR fits the real data well. We find that the SIIDR model outperforms more established compartmental models from epidemiology, such as SI, SIS, and SIR, at modeling SPM propagation.

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Benchmarking tools for a priori identifiability analysis

Barreiro

Villaverde

2023

Bioinformatics

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Motivation The theoretical possibility of determining the state and parameters of a dynamic model by measuring its outputs is given by its structural identifiability and observability. These properties should be analysed before attempting to calibrate a model, but their a priori analysis can be challenging, requiring symbolic calculations that often have a high computational cost. In recent years a number of software tools have been developed for this task, mostly in the systems biology community. These tools have vastly different features and capabilities, and a critical assessment of their performance is still lacking. Results Here we present a comprehensive study of the computational resources available for analysing structural identifiability. We consider 13 software tools developed in 7 programming languages and evaluate their performance using a set of 25 case studies created from 21 models. Our results reveal their strengths and weaknesses, provide guidelines for choosing the most appropriate tool for a given problem, and highlight opportunities for future developments. Availability https://github.com/Xabo-RB/Benchmarking_files.

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Web-Based Structural Identifiability Analyzer

Cited by 10 publications

References 18 publications

Differential elimination for dynamical models via projections with applications to structural identifiability

Differential elimination for dynamical models via projections with applications to structural identifiability

Modeling self-propagating malware with epidemiological models

Benchmarking tools for a priori identifiability analysis

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