Abstract
During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in E. coli, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.
Highlights
Verified relations between process parameters and cell-specific characteristics is the key knowledge to develop robust and scalable bioprocesses [1]
We show a generic Monte Carlo error propagation approach to obtain a realistic error estimate on both the regressor and predictor variables based on real measurement errors and how to use them, to determine uncertainty in subsequent regression analysis
E. coli strain was cultivated on defined media given in [28], and different exponential glycerol feed profiles were applied during growth, whereas the inducing lactose feed was kept the same for all experiments
Summary
Verified relations between process parameters and cell-specific characteristics is the key knowledge to develop robust and scalable bioprocesses [1]. This knowledge is often based on regression analysis of historical data, where all possible relations are inspected [2]. The calculation of biomass-specific rates is based on different data sources, including online signals and offline measurements and their timely changes [7]. The use of smoothing and spline fits might be a good way to obtain smooth time derivatives of noisy measurements [8], but their usage is critical with a low number of measurements with the risk of smoothing out important biological events. Due to the low number of measured samples, the finite differences of two subsequent measurements are most commonly used in biotechnology
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