Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/55602

TitleA review of dynamic modeling approaches and their application in computational strain optimization for metabolic engineering
Author(s)Kim, Luis
Rocha, Miguel
Maia, Paulo
Keywordsdynamic modeling
strain optimization
phenotype prediction
metabolic engineering
hybrid modeling
Issue date2018
PublisherFrontiers Media
JournalFrontiers in Microbiology
CitationKim, Luis; Rocha, Miguel; Maia, Paulo, A review of dynamic modeling approaches and their application in computational strain optimization for metabolic engineering. Frontiers in Microbiology, 9, 1690-1690, 2018
Abstract(s)Mathematical modeling is a key process to describe the behavior of biological networks. One of the most difficult challenges is to build models that allow quantitative predictions of the cells' states along time. Recently, this issue started to be tackled through novel in silico approaches, such as the reconstruction of dynamic models, the use of phenotype prediction methods, and pathway design via efficient strain optimization algorithms. The use of dynamic models, which include detailed kinetic information of the biological systems, potentially increases the scope of the applications and the accuracy of the phenotype predictions. New efforts in metabolic engineering aim at bridging the gap between this approach and other different paradigms of mathematical modeling, as constraint-based approaches. These strategies take advantage of the best features of each method, and deal with the most remarkable limitationthe lack of available experimental informationwhich affects the accuracy and feasibility of solutions. Parameter estimation helps to solve this problem, but adding more computational cost to the overall process. Moreover, the existing approaches include limitations such as their scalability, flexibility, convergence time of the simulations, among others. The aim is to establish a trade-off between the size of the model and the level of accuracy of the solutions. In this work, we review the state of the art of dynamic modeling and related methods used for metabolic engineering applications, including approaches based on hybrid modeling. We describe approaches developed to undertake issues regarding the mathematical formulation and the underlying optimization algorithms, and that address the phenotype prediction by including available kinetic rate laws of metabolic processes. Then, we discuss how these have been used and combined as the basis to build computational strain optimization methods for metabolic engineering purposes, how they lead to bi-level schemes that can be used in the industry, including a consideration of their limitations.
TypeArticle
URIhttp://hdl.handle.net/1822/55602
DOI10.3389/fmicb.2018.01690
ISSN1664-302X
e-ISSN1664-302X
Publisher versionhttp://journal.frontiersin.org/journal/microbiology
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series

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