EasyVVUQ: A Library for Verification, Validation and Uncertainty
                        Quantification in High Performance Computing

Richardson, Robin A.; Wright, David W.; Edeling, Wouter; Jancauskas, Vytautas; Lakhlili, Jalal; Coveney, Peter V.

doi:10.5334/jors.303

Cited by 47 publications

(33 citation statements)

References 17 publications

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“…We use EasyVVUQ [33,41] from the Verified Exascale Computing for Multiscale Applications (VECMA) toolkit [18], for propagating the input uncertainties through CovidSim. To interface CovidSim with EasyVVUQ, templates from the CovidSim input files are generated.…”

Section: Uncertainty Quantification Resultsmentioning

confidence: 99%

Model uncertainty and decision making: Predicting the Impact of COVID-19 Using the CovidSim Epidemiological Code

Edeling¹,

Sinclair²,

Suleimenova³

et al. 2020

Preprint

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) virus has rapidly spread worldwide since December 2019, and early modelling work of this pandemic has assisted in identifying effective government interventions. The UK government relied in part on the CovidSim model developed by the MRC Centre for Global Infectious Disease Analysis at Imperial College London, to model various non-pharmaceutical intervention strategies, and guide its government policy in seeking to deal with the rapid spread of the COVID-19 pandemic during March and April 2020. CovidSim is subject to different sources of uncertainty, namely parametric uncertainty in the inputs, model structure uncertainty (i.e. missing epidemiological processes) and scenario uncertainty, which relates to uncertainty in the set of conditions under which the model is applied. We have undertaken an extensive parametric sensitivity analysis and uncertainty quantification of the current CovidSim code. From the over 900 parameters that are provided as input to CovidSim, we identified a key subset of 20 parameters to which the code output is most sensitive. We find that the uncertainty in the code is substantial, in the sense that imperfect knowledge in these inputs will be magnified to the outputs, up to the extent of ca. 300%. Most of this uncertainty can be traced back to the sensitivity of three parameters. Compounding this, the model can display significant bias with respect to observed data, such that the output variance does not capture this validation data with high probability. We conclude that quantifying the parametric input uncertainty is not sufficient, and that the effect of model structure and scenario uncertainty cannot be ignored when validating the model in a probabilistic sense.

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Section: Uncertainty Quantification Resultsmentioning

confidence: 99%

Model uncertainty and decision making: Predicting the Impact of COVID-19 Using the CovidSim Epidemiological Code

Edeling¹,

Sinclair²,

Suleimenova³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…More recently, the effective use of highly parallel computing resources has expanded the complexity of biological models that can be simulated ( Rudge et al, 2012 ; Naylor et al, 2017 ; Li et al, 2019 ; Cooper et al, 2020 ). Automated coarse-graining of representations enable faster simulation without impacting on the accuracy of predictions ( Graham et al, 2017 ), while advanced tools allow verification, validation and uncertainty quantification for such simulations ( Richardson et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Toward Engineering Biosystems With Emergent Collective Functions

Gorochowski

Hauert

Kreft

et al. 2020

Front. Bioeng. Biotechnol.

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Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.

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“…More recently, the effective use of highly parallel computing resources has expanded the complexity of biological models that can be simulated (Rudge et al, 2012;Naylor et al, 2017;Li et al, 2019;Cooper et al, 2020). Automated coarse-graining of representations enable faster simulation without impacting on the accuracy of predictions (Graham et al, 2017), while advanced tools allow verification, validation and uncertainty quantification for such simulations (Richardson et al, 2020). 10 performance models coupled to virtual reality allow for multiple researchers to interactively manipulate a system and immediately observe the outcomes of their design decisions.…”

Section: Discussionmentioning

confidence: 99%

Towards Engineering Biosystems with Emergent Collective Functions

Gorochowski¹,

Hauert²,

Kreft³

et al. 2020

Preprint

View full text Add to dashboard Cite

Many complex behaviours in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans multiple length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modelling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modelling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviours offers a means to take synthetic biology beyond single molecules or cells and towards the creation of systems with functions that can only emerge from collectives at multiple scales.

show abstract

EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing

Cited by 47 publications

References 17 publications

Model uncertainty and decision making: Predicting the Impact of COVID-19 Using the CovidSim Epidemiological Code

Model uncertainty and decision making: Predicting the Impact of COVID-19 Using the CovidSim Epidemiological Code

Toward Engineering Biosystems With Emergent Collective Functions

Towards Engineering Biosystems with Emergent Collective Functions

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