Monte Carlo Simulations for the Analysis of Non-linear Parameter Confidence Intervals in Optimal Experimental Design

Krausch, Niels; Barz, Tilman; Sawatzki, Annina; Gruber, Mathis; Kamel, Sarah; Neubauer, Peter; Bournazou, Mariano Nicolás Cruz

doi:10.3389/fbioe.2019.00122

Cited by 29 publications

(35 citation statements)

References 53 publications

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“…On the other hand, the FIM has several shortcomings as an estimator of the variance-covariance matrix ( C ) of the model parameters. According to the Cramer-Rao inequality, the FIM is a bound of C under several hypotheses that disregard possible non-linearity in the model [44]. Moreover, the calculation of the FIM and the local sensitivities can be complicated when errors are non-normally distributed, for instance when residuals are heteroscedastic or when they do not follow a normal distribution (e.g.…”

Section: Resultsmentioning

confidence: 99%

On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods

et al. 2019

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The mathematical models used in predictive microbiology contain parameters that must be estimated based on experimental data. Due to experimental uncertainty and variability, they cannot be known exactly and must be reported with a measure of uncertainty (usually a standard deviation). In order to increase precision (i.e. reduce the standard deviation), it is usual to add extra sampling points. However, recent studies have shown that precision can also be increased without adding extra sampling points by using Optimal Experiment Design, which applies optimization and information theory to identify the most informative experiment under a set of constraints. Nevertheless, to date, there has been scarce contributions to know a priori whether an experimental design is likely to provide the desired precision in the parameter estimates. In this article, two complementary methodologies to predict the parameter precision for a given experimental design are proposed. Both approaches are based on in silico simulations, so they can be performed before any experimental work. The first one applies Monte Carlo simulations to estimate the standard deviation of the model parameters, whereas the second one applies the properties of the Fisher Information Matrix to estimate the volume of the confidence ellipsoids. The application of these methods to a case study of dynamic microbial inactivation, showing how they can be used to compare experimental designs and assess their precision, is illustrated. The results show that, as expected, the optimal experimental design is more accurate than the uniform design with the same number of data points. Furthermore, it is demonstrated that, for some heating profiles, the uniform design does not ensure that a higher number of sampling points increases precision. Therefore, optimal experimental designs are highly recommended in predictive microbiology.

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Section: Resultsmentioning

confidence: 99%

On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods

et al. 2019

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“…Regularization of parameter estimation using a subsets selection method [28] was used to ensure a meaningful parameter set and to avoid non-physiological model calibrations. Monte Carlo simulations have been shown to give a good insight into actual, non-linear parameter distribution [31] and were therefore performed to gain a better understanding of the parameter correlation and its variances. In Figure 8, the parameter distributions for E. coli BW25113 ΔglcB (last model calibration cycle) are shown based on Monte Carlo simulations.…”

Section: Parameter Estimationmentioning

confidence: 99%

Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations

et al. 2020

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In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance in larger reactors, has a major influence on the efficiency of the final process. To overcome this, scale-down approaches during screenings that show the real cell factory performance at industrial-like conditions are essential. We present a fully automated robotic facility with 24 parallel mini-bioreactors that is operated by a model-based adaptive input design framework for the characterization of clone libraries under scale-down conditions. The cultivation operation strategies are computed and continuously refined based on a macro-kinetic growth model that is continuously re-fitted to the available experimental data. The added value of the approach is demonstrated with 24 parallel fed-batch cultivations in a mini-bioreactor system with eight different Escherichia coli strains in triplicate. The 24 fed-batch cultivations were run under the desired conditions, generating sufficient information to define the fastest-growing strain in an environment with oscillating glucose concentrations similar to industrial-scale bioreactors.

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“…Regularization of parameter estimation using a subsets selection method [38] was used to ensure a meaningful parameter set. Monte Carlo simulations have shown to give a good insight into the actual, non -linear parameter distribution [40] and were therefore performed to get a better understanding of the parameter correlation. The correlation between all parameters is very weak.…”

Section: Parameter Estimationmentioning

confidence: 99%

Automated Conditional Screening of Escherichia Coli Knockout Mutants in Parallel Adaptive Fed-Batch Cultivations

Hans¹,

Haby²,

Krausch³

et al. 2020

Preprint

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In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance of the selected cell factory in larger reactors, has a major influence on the performance of the final process. To overcome this, scaledown approaches are essential to run screenings that show the real cell factory performance at industrial like conditions. We present a fully automated robotic facility with 24 parallel mini-bioreactors that is operated by a model based adaptive input design framework for the characterization of clone libraries under scale-down conditions. The cultivation operation strategies are computed and continuously refined based on a macro-kinetic growth model that is continuously re-fitted to the available experimental data. The added value of the approach is demonstrated with 24 parallel fed-batch cultivations in a mini-bioreactor system with eight different Escherichia coli strains in triplicate. The 24 fed-batches ran under the desired conditions generating sufficient information to define the fastest growing strain in an environment with varying glucose concentrations similar to industrial scale bioreactors.

show abstract

Monte Carlo Simulations for the Analysis of Non-linear Parameter Confidence Intervals in Optimal Experimental Design

Cited by 29 publications

References 53 publications

On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods

On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods

Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations

Automated Conditional Screening of Escherichia Coli Knockout Mutants in Parallel Adaptive Fed-Batch Cultivations

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