Protamine dosing in cardiac surgery

Vandenheuvel, Michaël; Vandewiele, Korneel; Somer, Filip De; Wouters, Patrick

doi:10.1177/02676591231154116

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…We perform profile likelihood-based practical identifiability analysis using the ProfileLikelihood.jl [79] package. Table 1 shows the resulting confidence intervals for all estimated model parameters are finite, thus proving the practical identifiability of the model based on our experimental data [80].…”

Section: Practical Identifiability Analysismentioning

confidence: 99%

In silico model development and optimization of in vitro lung cell population growth

Mostofinejad,

Romero,

Brinson

et al. 2024

PLoS ONE

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Tissue engineering predominantly relies on trial and error in vitro and ex vivo experiments to develop protocols and bioreactors to generate functional tissues. As an alternative, in silico methods have the potential to significantly reduce the timelines and costs of experimental programs for tissue engineering. In this paper, we propose a methodology to formulate, select, calibrate, and test mathematical models to predict cell population growth as a function of the biochemical environment and to design optimal experimental protocols for model inference of in silico model parameters. We systematically combine methods from the experimental design, mathematical statistics, and optimization literature to develop unique and explainable mathematical models for cell population dynamics. The proposed methodology is applied to the development of this first published model for a population of the airway-relevant bronchio-alveolar epithelial (BEAS-2B) cell line as a function of the concentration of metabolic-related biochemical substrates. The resulting model is a system of ordinary differential equations that predict the temporal dynamics of BEAS-2B cell populations as a function of the initial seeded cell population and the glucose, oxygen, and lactate concentrations in the growth media, using seven parameters rigorously inferred from optimally designed in vitro experiments.

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Section: Practical Identifiability Analysismentioning

confidence: 99%