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Experimental design principles for isotopically instationary 13 C labeling experiments
Author(s) -
Nöh Katharina,
Wiechert Wolfgang
Publication year - 2006
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.20803
Subject(s) - metabolic flux analysis , identifiability , computer science , sampling (signal processing) , biological system , flux (metallurgy) , design of experiments , simple (philosophy) , biochemical engineering , transient (computer programming) , data mining , machine learning , chemistry , mathematics , statistics , engineering , biology , biochemistry , philosophy , organic chemistry , filter (signal processing) , epistemology , metabolism , computer vision , operating system
13 C metabolic flux analysis (MFA) is a well‐established tool in Metabolic Engineering that found numerous applications in recent years. However, one strong limitation of the current method is the requirement of an—at least approximate—isotopic stationary state at sampling time. This requirement leads to a principle lower limit for the duration of a 13 C labeling experiment. A new methodological development is based on repeated sampling during the instationary transient of the 13 C labeling dynamics. The statistical and computational treatment of such instationary experiments is a completely new terrain. The computational effort is very high because large differential equations have to be solved and, moreover, the intracellular pool sizes play a significant role. For this reason, the present contribution works out principles and strategies for the experimental design of instationary experiments based on a simple example network. Hereby, the potential of isotopically instationary experiments is investigated in detail. Various statistical results on instationary flux identifiability are presented and possible pitfalls of experimental design are discussed. Finally, a framework for almost optimal experimental design of isotopically instationary experiments is proposed which provides a practical guideline for the analysis of large‐scale networks. © 2006 Wiley Periodicals, Inc.