Structural and practical identifiability analysis in bioengineering: a beginner's guide.

Advancements in digital technology have brought modelling to the forefront in many disciplines from healthcare to architecture. Mathematical models, often represented using parametrised sets of ordinary differential equations, can be used to characterise different processes. To infer possible estimates for the unknown parameters, these models are usually calibrated using associated experimental data. Structural and practical identifiability analyses are a key component that should be assessed prior to parameter estimation. This is because identifiability analyses can provide insights as to whether or not a parameter can take on single, multiple, or even infinitely or countably many values which will ultimately have an impact on the reliability of the parameter estimates. Also, identifiability analyses can help to determine whether the data collected are sufficient or of good enough quality to truly estimate the parameters or if more data or even reparameterization of the model is necessary to proceed with the parameter estimation process. Thus, such analyses also provide an important role in terms of model design (structural identifiability analysis) and the collection of experimental data (practical identifiability analysis). Despite the popularity of using data to estimate the values of unknown parameters, structural and practical identifiability analyses of these models are often overlooked. Possible reasons for non-consideration of application of such analyses may be lack of awareness, accessibility, and usability issues, especially for more complicated models and methods of analysis. The aim of this study is to introduce and perform both structural and practical identifiability analyses in an accessible and informative manner via application to well established and commonly accepted bioengineering models. This will help to improve awareness of the importance of this stage of the modelling process and provide bioengineering researchers with an understanding of how to utilise the insights gained from such analyses in future model development.

Cole-Cole modeling of real-time capacitance data for estimation of cell physiological properties in recombinant Escherichia coli cultivation.

Real-time estimation of physiological properties of the cell during recombinant protein production would ensure enhanced process monitoring. In this study, we explored the application of dielectric spectroscopy to track the fed-batch phase of recombinant Escherichia coli cultivation for estimating the physiological properties, namely, cell diameter and viable cell concentration (VCC). The scanning capacitance data from the dielectric spectroscopy were pre-processed using moving average. Later, it was modeled through a nonlinear theoretical Cole-Cole model and further solved using a global evolutionary genetic algorithm (GA). The parameters obtained from the GA were further applied for the estimation of the aforementioned physiological properties. The offline cell diameter and cell viability data were obtained from particle size analyzer and flow cytometry measurements to validate the Cole-Cole model. The offline VCC was calculated from the cell viability % from flow cytometry data and dry cell weight concentration. The Cole-Cole model predicted the cell diameter and VCC with an error of 1.03% and 7.72%, respectively. The proposed approach can enable the operator to take real-time process decisions to achieve desired productivity and product quality.