ITEM METADATA RECORD
Title: Dynamic optimization of biological networks under parametric uncertainty
Authors: Nimmegeers, Philippe
Telen, Dries
Logist, Filip
Van Impe, Jan # ×
Issue Date: 31-Aug-2016
Publisher: BioMed Central Ltd.
Series Title: BMC Systems Biology vol:10 issue:86 pages:1-20
Abstract: Background: Micro-organisms play an important role in various industrial sectors (including biochemical, food and pharmaceutical industries). A profound insight in the biochemical reactions inside micro-organisms enables an improved biochemical process control. Biological networks are an important tool in systems biology for incorporating microscopic level knowledge. Biochemical processes are typically dynamic and the cells have often more than one objective which are typically conflicting, e.g., minimising the energy consumption while maximizing the production of a specific metabolite. Therefore multi-objective optimization is needed to compute trade-offs between those conflicting objectives. In model-based optimization, one of the inherent problems is the presence of uncertainty. In biological processes, this uncertainty can be present due to, e.g., inherent biological variability. Not taking this uncertainty into account, possibly leads to the violation of constraints and erroneous estimates of the actual objective function(s). To account for the variance in model predictions and compute a prediction interval, this uncertainty should be taken into account during process optimization. This leads to a challenging optimization problem under uncertainty, which requires a robustified solution.
Results: Three techniques for uncertainty propagation: linearization, sigma points and polynomial chaos expansion, are compared for the dynamic optimization of biological networks under parametric uncertainty. These approaches are compared in two case studies: (i) a three-step linear pathway model in which the accumulation of intermediate metabolites has to be minimized and (ii) a glycolysis inspired network model in which a multiobjective optimization problem is considered, being the minimization of the enzymatic cost and the minimization of the end time before reaching a minimum extracellular metabolite concentration. A Monte Carlo simulation procedure has been applied for the assessment of the constraint violations. For the multi-objective case study one Pareto point has been considered for the assessment of the constraint violations. However, this analysis can be performed for any Pareto point.
Conclusions: The different uncertainty propagation strategies each offer a robustified solution under parametric uncertainty. When making the trade-off between computation time and the robustness of the obtained profiles, the sigma points and polynomial chaos expansion strategies score better in reducing the percentage of constraint violations. This has been investigated for a normal and a uniform parametric uncertainty distribution. The polynomial chaos expansion approach allows to directly take prior knowledge of the parametric uncertainty distribution into account.
ISSN: 1752-0509
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Sustainable Chemical Process Technology TC, Technology Campuses Ghent and Aalst
Sustainable Chemical Process Technology TC
UC Limburg - miscellaneous
Sustainable Chemical Process Technology TC, Technology Campus De Nayer Sint-Katelijne-Waver
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
BMC_Systems.pdf Published 2100KbAdobe PDFView/Open

 


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science