Author(s):
Vilaça, Paulo 
; Maia, Paulo ; Rocha, Miguel
Date: 2011
Persistent ID: http://hdl.handle.net/1822/14344
Origin: RepositóriUM - Universidade do Minho
Document details
A study on the robustness of strain optimization algorithms
Description
5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011), ISBN 978-3-642-19913-4 In recent years, there have been considerable advances in the use of genome-scale metabolic models to provide accurate phenotype simulation methods, which in turn enabled the development of efficient strain optimization algorithms for Metabolic Engineering. In this work, we address some of the limitations of previous studies regarding strain optimization algorithms, mainly its use of Flux Balance Analysis in the simulation layer.We perform a thorough analysis of previous results by relying on Flux Variability Analysis and on alternative methods for phenotype simulation, such as ROOM. This last method is also used in the simulation layer, as a basis for optimization, and the results obtained are also the target of thorough analysis and comparison with previous ones.
5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011), ISBN 978-3-642-19913-4 In recent years, there have been considerable advances in the use of genome-scale metabolic models to provide accurate phenotype simulation methods, which in turn enabled the development of efficient strain optimization algorithms for Metabolic Engineering. In this work, we address some of the limitations of previous studies regarding strain optimization algorithms, mainly its use of Flux Balance Analysis in the simulation layer.We perform a thorough analysis of previous results by relying on Flux Variability Analysis and on alternative methods for phenotype simulation, such as ROOM. This last method is also used in the simulation layer, as a basis for optimization, and the results obtained are also the target of thorough analysis and comparison with previous ones.
Document Type
Article
Language English
Language English
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