Identification of active constraints in dynamic flux balance analysis

This study deals with the calibration of dynamic metabolic flux models that are formulated as the maximization of an objective subject to constraints. Two approaches were applied for identifying the constraints from data. In the first approach a minimal active number of limiting constraints is found...

Full description

Saved in:
Bibliographic Details
Published inBiotechnology progress Vol. 33; no. 1; pp. 26 - 36
Main Authors Nikdel, Ali, Budman, Hector
Format Journal Article
LanguageEnglish
Published United States Wiley Subscription Services, Inc 01.01.2017
Subjects
Algorithms
Bordetella pertussis
Computer Simulation
constraints
dynamic flux balance model
E coli
Escherichia coli - genetics
Escherichia coli - metabolism
Metabolic Networks and Pathways - genetics
Models, Biological
Sensitivity analysis
Systems Biology
dynamic flux balance model
Bordetella pertussis
constraints
E. coli
Online AccessGet full text

Cover

More Information
Summary:This study deals with the calibration of dynamic metabolic flux models that are formulated as the maximization of an objective subject to constraints. Two approaches were applied for identifying the constraints from data. In the first approach a minimal active number of limiting constraints is found based on data that are assumed to be bounded within sets whereas, in the second approach, the limiting constraints are found based on parametric sensitivity analysis. The ability of these approaches to finding the active limiting constraints was verified through their application to two case studies: an in‐silico (simulated) data‐based study describing the growth of E. coli and an experimental data‐based study for Bordetella pertussis (B. pertussis). © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:26–36, 2017
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:8756-7938
1520-6033
DOI:10.1002/btpr.2388