Modeling and Model Simplification to Facilitate Biological Insights and Predictions

Eriksson, Olivia; Tegnér, Jesper

doi:10.1007/978-3-319-21296-8_12

Cited by 7 publications

(1 citation statement)

References 81 publications

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“…The reduced order model developed in this study enables us to perform detailed process optimization for neotissue growth, which is novel in the field of stem cell bioprocessing. Different techniques for model reduction exist, including variable lumping, timescale separation, sensitivity analysis and model reformulation (Eriksson and Tegnér 2016). Variable lumping is the process of reducing the dimensions of a system by merging variables with similar properties (e.g.…”

Section: Introductionmentioning

confidence: 99%

Maximizing neotissue growth kinetics in a perfusion bioreactor: An in silico strategy using model reduction and Bayesian optimization

Mehrian

Guyot

Papantoniou

et al. 2017

Biotech & Bioengineering

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In regenerative medicine, computer models describing bioreactor processes can assist in designing optimal process conditions leading to robust and economically viable products. In this study, we started from a (3D) mechanistic model describing the growth of neotissue, comprised of cells, and extracellular matrix, in a perfusion bioreactor set-up influenced by the scaffold geometry, flow-induced shear stress, and a number of metabolic factors. Subsequently, we applied model reduction by reformulating the problem from a set of partial differential equations into a set of ordinary differential equations. Comparing the reduced model results to the mechanistic model results and to dedicated experimental results assesses the reduction step quality. The obtained homogenized model is 10 fold faster than the 3D version, allowing the application of rigorous optimization techniques. Bayesian optimization was applied to find the medium refreshment regime in terms of frequency and percentage of medium replaced that would maximize neotissue growth kinetics during 21 days of culture. The simulation results indicated that maximum neotissue growth will occur for a high frequency and medium replacement percentage, a finding that is corroborated by reports in the literature. This study demonstrates an in silico strategy for bioprocess optimization paying particular attention to the reduction of the associated computational cost.

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Section: Introductionmentioning

confidence: 99%

Maximizing neotissue growth kinetics in a perfusion bioreactor: An in silico strategy using model reduction and Bayesian optimization

Mehrian

Guyot

Papantoniou

et al. 2017

Biotech & Bioengineering

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An Introduction to Uncertainty in the Development of Computational Models of Biological Processes

Geris

Gomez-Cabrero

2015

Uncertainty in Biology

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This chapter aims to provide an introduction to the different ways in which uncertainty can be dealt with computational modelling of biological processes. The first step is model establishment under uncertainty. Once models have been established, data can further be used to select which of the proposed models best meets the predefined criteria. Subsequently, parameter values can be optimized for a specific model configuration. Sensitivity analyses allow to assess the influence of the previous choices on the model output. Additionally, model adaptation permits to focus on specific aspects of the model without losing its global predictive capacity. Finally, predictions with the established models should also consider the effect of uncertainty in the model development process.

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Sensitivity Analysis by Design of Experiments

Schepdael

Carlier

Geris

2015

Uncertainty in Biology

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The design of experiments (DOE) is a valuable method for studying the influence of one or more factors on the outcome of computer experiments. There is no limit to the number of times a computer experiment can be run, but they are often time-consuming. Moreover, the number of parameters in a computer model is often very large and the range of variation for each of these parameters is often quite extensive. The DOE provides the statistical tools necessary for choosing a minimum amount of parameter combinations resulting in as much information as possible about the computer model. In this chapter, several designs and analysing methods are explained. At the end of the chapter, these designs and methods are applied to a mechanobiological model describing tooth movement.

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Modeling and Model Simplification to Facilitate Biological Insights and Predictions

Cited by 7 publications

References 81 publications

Maximizing neotissue growth kinetics in a perfusion bioreactor: An in silico strategy using model reduction and Bayesian optimization

Maximizing neotissue growth kinetics in a perfusion bioreactor: An in silico strategy using model reduction and Bayesian optimization

An Introduction to Uncertainty in the Development of Computational Models of Biological Processes

Sensitivity Analysis by Design of Experiments

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