«Sequential Design of Dynamic Experiments in Modeling for Optimization of Biological Processes». Abstract 28-4

MARIANO D. CRISTALDI; RICARDO J. A. GRAU; ERNESTO C. MARTINEZ

Salvador, Bahía (Brasil)

Simposio; PSE’09. 10th International Symposium on Process Systems Engineering; 2009

USP-Universidade de Sao Paulo

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 recombinant interleukin-11 (rIL-11) whose DNA sequence has been cloned in an Escherichia 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 certain’ tendency model is used for designining the next dynamic experiment. Model selection is based on an error measure which separates parametric uncertainty from structural errors to trade-off exploration with exploitation.rIL-11) whose DNA sequence has been cloned in an Escherichia 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 certain’ tendency model is used for designining the next dynamic experiment. Model selection is based on an error measure which separates parametric uncertainty from structural errors to trade-off exploration with exploitation.rIL-11) whose DNA sequence has been cloned in an Escherichia 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 certain’ tendency model is used for designining the next dynamic experiment. Model selection is based on an error measure which separates parametric uncertainty from structural errors to trade-off exploration with exploitation.