Parametric uncertainty propagation for robust dynamic optimization of biological networks

© 2016 American Automatic Control Council (AACC). An important trend in biochemical industry is the search for a more sustainable operation. This requires an improved design and operation of processes with respect to economic, environmental and safety aspects. For an accurate macroscopic process control, multi-scale models based on microscopic systems biology insights are increasingly used. However, model uncertainty as well as external disturbances are inevitably present. Hence, there is a need for approaches which can guarantee process safety despite this presence of uncertainty in the model parameters. Therefore, techniques for the propagation of the parametric uncertainties to the states are vital for a robust process control. In this work, the potential of the polynomial chaos expansion approach is investigated for optimal control of a four step linear pathway with Michaelis-Menten kinetics by manipulating the enzyme expression rates.

ISBN:9781467386838

Publication year:2016

BOF-keylabel:yes

IOF-keylabel:yes

Authors from:Private, Higher Education