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Optimization of bioprocess productivity based on metabolic‐genetic network models with bilevel dynamic programming
Author(s) -
Jabarivelisdeh Banafsheh,
Waldherr Steffen
Publication year - 2018
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.26599
Subject(s) - flux balance analysis , bioprocess , metabolic network , metabolic engineering , biochemical engineering , computer science , productivity , bilevel optimization , process (computing) , constraint (computer aided design) , genetic algorithm , optimization problem , computational biology , engineering , biology , gene , machine learning , genetics , economics , mechanical engineering , macroeconomics , algorithm , chemical engineering , operating system
One of the main goals of metabolic engineering is to obtain high levels of a microbial product through genetic modifications. To improve the productivity of such a process, the dynamic implementation of metabolic engineering strategies has been proven to be more beneficial compared to static genetic manipulations in which the gene expression is not controlled over time, by resolving the trade‐off between growth and production. In this work, a bilevel optimization framework based on constraint‐based models is applied to identify an optimal strategy for dynamic genetic and process level manipulations to increase productivity. The dynamic enzyme‐cost flux balance analysis (deFBA) as underlying metabolic network model captures the network dynamics and enables the analysis of temporal regulation in the metabolic‐genetic network. We apply our computational framework to maximize ethanol productivity in a batch process with Escherichia coli . The results highlight the importance of integrating the genetic level and enzyme production and degradation processes for obtaining optimal dynamic gene and process manipulations.