GlobalSensitivity.jl: Performant and Parallel Global
Sensitivity Analysis with Julia

Dixit, Vaibhav; Rackauckas, Chris

doi:10.21105/joss.04561

Cited by 28 publications

(25 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further details regarding the origins of these limits can be found in S2 Text. The model and global sensitivity analyses were implemented in Julia [26] using the DifferentialEquations.jl and GlobalSensitivity.jl libraries, making use of powerful ODE solvers and diagnostics [27,28]. For the Sobol sensitivity analysis, IC50 was fixed to a value of 0.01 and was not considered uncertain.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Pharmacodynamic model of PARP1 inhibition and global sensitivity analyses can lead to cancer biomarker discovery

Mertins¹,

Isenberg²,

Reyes³

et al. 2023

Preprint

View full text Add to dashboard Cite

Pharmacodynamic models provide inroads to understanding key mechanisms of action and may significantly improve patient outcomes in cancer with improved ability to determine therapeutic benefit. Additionally, these models may also lead to insights into potential biomarkers that can be utilized for prediction in prognosis and therapeutic decisions. As an example of this potential, here we present an advanced computational Ordinary Differential Equation (ODE) model of PARP1 signalling and downstream effects due to its inhibition. The model has been validated experimentally and further evaluated through a global sensitivity analysis. The sensitivity analysis uncovered two model parameters related to protein synthesis and degradation rates that were also found to contribute the most variability to the therapeutic prediction. Because this variability may define cancer patient subpopulations, we interrogated genomic, transcriptomic, and clinical databases, to uncover a biomarker that may correspond to patient outcomes in the model. In particular, GSPT2, a GTPase with translation function, was discovered and if mutations serve to alter catalytic activity, its presence may explain the variability in the model′s parameters. This work offers an analysis of ODE models, inclusive of model development, sensitivity analysis, and ensuing experimental data analysis, and demonstrates the utility of this methodology in uncovering biomarkers in cancer.

show abstract

Section: Methodsmentioning

confidence: 99%

“…As indicated above, the model was represented by a system of coupled, non-linear ODEs containing 27 parameters (S3 Table ). We provide lower and upper bounds in S4 [27,28].…”

Section: Sensitivity Analysismentioning

confidence: 99%

Pharmacodynamic model of PARP1 inhibition and global sensitivity analyses can lead to cancer biomarker discovery

Mertins¹,

Isenberg²,

Reyes³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Meanwhile, a large variance suggests that the influence of a parameter is non-linear or results from interactions with other parameters. The Morris sensitivity coefficients were computed using the GlobalSensitivity.jl package [30].…”

Section: Morris Sensitivity Analysismentioning

confidence: 99%

Modeling and Analysis of a Cell-Free Gluconate Responsive Biosensor

Adhikari

Murti

Narayanan

et al. 2023

Preprint

View full text Add to dashboard Cite

Cell-free synthetic systems are composed of the parts required for transcription and translation processes in a buffered solution. Thus, unlike living cells, cell-free systems are amenable to rapid adjustment of the reaction composition and easy sampling. Further, because cellular growth and maintenance requirements are absent, all resources can go toward synthesizing the product of interest. Recent improvement in key performance metrics, such as yield, reaction duration, and portability, has increased the space of possible applications open to cell-free systems and lowered the time required to design-build-test new circuitry. One promising application area is biosensing. This study describes developing and modeling a D-gluconate biosensor circuit operating in a reconstituted cell-free system. Model parameters were estimated using time-resolved measurements of the mRNA and protein concentration with and without the addition of D-gluconate. Sensor performance was predicted using the model for D-gluconate concentrations not used in model training. The model predicted the transcription and translation kinetics and the dose response of the circuit over several orders of magnitude of D-gluconate concentration. Global sensitivity analysis of the model parameters gave detailed insight into the operation of the sensor circuit. Taken together, this study reported an in-depth, systems-level analysis of a D-gluconate biosensor circuit operating in a reconstituted cell-free system. This circuit could be used directly to estimate D-gluconate or as a subsystem in a more extensive synthetic gene expression program.

show abstract

“…The primary sources of uncertainty that affect a system may be determined by performing a basic sensitivity analysis, e.g., using a tool such as the GlobalSensitivity package (Dixit and Rackauckas, 2022) in Julia.…”

Section: Sources Of Uncertaintymentioning

confidence: 99%

HMC Techniques for Reducing the Uncertainty of Gas-Lifted Oil Field Model

Jayamanne¹,

Lie²

2023

MIC

View full text Add to dashboard Cite

Parametric model uncertainties could have a high impact on the predictive capabilities of a model. When process measurements become available, these uncertainties may be reduced using parameter estimation techniques. Estimation techniques founded on the Bayesian framework in particular are powerful: they produce a probability density function (PDF) of the estimated parameter rather than a single point estimate.In this paper, we consider a gas lifted oil field model whose predictions are highly sensitive to uncertainty in its parameters. We apply Markov Chain Monte Carlo (MCMC) methods, which follow the Bayesian paradigm, to estimate these parameters, and thereby reduce the uncertainty in the model predictions; two different algorithms, Hamiltonian Monte Carlo (HMC) and No-U-Turn Sampler (NUTS), are used. The probabilistic programming language (PPL), Turing in Julia is used for implementation. Monte Carlo simulations and/or data retrodiction is performed prior to and post parameter estimation, to evaluate the uncertainty in model predictions; the outcomes are compared to determine the efficacy of parameter estimation. Results show that the computed posterior distributions yield model predictions that are in close agreement with the observations, and that model uncertainty is effectively reduced.

show abstract

GlobalSensitivity.jl: Performant and Parallel Global Sensitivity Analysis with Julia

Cited by 28 publications

References 23 publications

Pharmacodynamic model of PARP1 inhibition and global sensitivity analyses can lead to cancer biomarker discovery

Pharmacodynamic model of PARP1 inhibition and global sensitivity analyses can lead to cancer biomarker discovery

Modeling and Analysis of a Cell-Free Gluconate Responsive Biosensor

HMC Techniques for Reducing the Uncertainty of Gas-Lifted Oil Field Model

Contact Info

Product

Resources

About