2016
DOI: 10.1101/056044
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JuPOETs: A Constrained Multiobjective Optimization Approach to Estimate Biochemical Model Ensembles in the Julia Programming Language

Abstract: Background: Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence int… Show more

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Cited by 3 publications
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“…as a single objective. The best fit set estimated by DOPS served as the starting point 152 for multiobjective ensemble generation using Pareto Optimal Ensemble Technique in the 153 Julia programming language (JuPOETs) [27]. JuPOETs is a multiobjective approach 154 which integrates simulated annealing with Pareto optimality to estimate model 155 ensembles on or near the optimal tradeoff surface between competing training 156 objectives.…”
mentioning
confidence: 99%
“…as a single objective. The best fit set estimated by DOPS served as the starting point 152 for multiobjective ensemble generation using Pareto Optimal Ensemble Technique in the 153 Julia programming language (JuPOETs) [27]. JuPOETs is a multiobjective approach 154 which integrates simulated annealing with Pareto optimality to estimate model 155 ensembles on or near the optimal tradeoff surface between competing training 156 objectives.…”
mentioning
confidence: 99%
“…In addition to rate and saturation constants appearing in the signal transduction equations, we estimate the transcript/protein specific correction factors α j , β j , δ j and δ j and control coefficients from these data. We used the Pareto Optimal Ensemble Technique (JuPOETs) multiobjective optimization framework in combination with leave-one-out cross-validation to estimate an ensemble of model parameters [20, 96]. Model parameter values were adjusted to minimize the residual between simulations and experimental measurements.…”
Section: Methodsmentioning
confidence: 99%