An automated approach to build process models by detecting biological phenomena in (fed-)batch experiments

This paper presents a method to detect appropriate process models from an automated analysis of (fed-)batch fermentations. After a data reconciliation the influence of feeding and sampling on the measurement trends is numerically compensated, in order to prepare the data sets for an automated detection of biological phenomena. At this, a probabilistic method is used to divide the compensated curves into several sections, called episodes, according to their qualitative behavior, i.e. increasing, decreasing, constant, or zero. This framework allows to calculate the probability of biological phenomena that reveal crucial information about the underlying reaction network. According to the probability level of the detected reactions a number of structured models is automatically proposed.An experimental validation of the described approach is shown using real fermentation data from fed-batches of Paenibacillus polymyxa, resulting in a simple but structured process model.