Computer-Aided Modeling of Chemical and Biological Systems: Methods, Tools, and Applications

Rapid process synthesis supported by a unified modular software framework

Although known to be very powerful, the widespread application of model-based techniques is still significantly hampered in the area of bio-processes. Reasons for this situation can be found along the whole chain to set up and implement such approaches. In a time-consuming step, models are typically hand-crafted. Whether alternatives of better models exist to actually fulfill the final goals is undocumented, most often even unknown. In a next step, model-based process control methods are hand-coded in an error-prone procedure. For many of these methods given in the literature, only simulation studies are shown, leaving the interested reader with the unanswered question whether the implementation of a specific method in a real process is viable. As the potentially time-consuming implementation of such a method presents a risk for a rapid process development, promising candidates may be overlooked. To remediate this unsatisfactory situation, a combination of theoretical methods and information technology is proposed here. By an exemplarily realized software tool, it is shown how such an environment helps to promote model-based optimization, supervision, and control of bio-processes and allows for an inexpensive test of new ideas as well in real-life experiments. The contribution concentrates on an overview of a possible software architecture with respect to necessary methods and a meaningful information strategy, highlighting some of the more crucial building blocks. Experimental results exploiting parts of the proposed methods are given for a yeast strain synthesizing a product of industrial interest.

Automatic identification of structured process models based on biological phenomena detected in (fed-)batch experiments

In this paper, we present a set of methods to automatically propose structured process models from an automated analysis of (fed-)batch experiments. Therefore, the measurements are numerically compensated for the influence of feeding and sampling, and the qualitative behavior of the measurements is revealed. As measurements from fermentations are inherently noisy, we introduce a method that divides the compensated curves into several episodes in a probabilistic framework to better handle these shortcomings. The probability of biological phenomena that reveal crucial information about the underlying reaction network is calculated. Since the phenomena detection is measurement-driven, its reliability depends on the measurement situation, e.g., the number of samples taken and experiments considered, measurement noise, etc. We show a possible approach to test the uncertainty of the phenomena detection against these influences. Finally, model structures are proposed automatically based on the detected biological phenomena. An experimental validation of the approach is shown, using real fermentation data from fed-batch cultivations of Streptomyces tendae.