Sequential design of dynamic experiments in modeling for optimization of biological processes

Abstract

Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speed up the development and scaling up of innovative bioprocesses. A methodology for model-based design of dynamic experiments in modeling for optimization is proposed and successfully applied to the optimization of a fed-batch bioreactor related to the production of r-interleukin-11 whose DNA has been cloned in an E. coli strain. A library of tendency models is used to increasingly bias bioreactor operating conditions towards an optimum. Parametric uncertainty of tendency models is iteratively reduced using Global Sensitivity Analysis (GSA). At each iteration, the ´most informative´ tendency model is used for designining the next dynamic experiment. Model selection is based on minimizing an error measure which separates parametric uncertainty from structural errors to trade-off exploration with exploitation.