OptStrain: a computational framework for redesign of microbial production systems.

By Prit Pharkya, Anthony P. Burgard, Costas D. Maranas. Genome Research, 2004

490 citations recorded by the source corpus.

This paper introduces the hierarchical computational framework OptStrain aimed at guiding pathway modifications, through reaction additions and deletions, of microbial networks for the overproduction of targeted compounds. These compounds may range from electrons or hydrogen in biofuel cell and environmental applications to complex drug precursor molecules. A comprehensive database of biotransformations, referred to as the Universal database (with >5700 reactions), is compiled and regularly updated by downloading and curating reactions from multiple biopathway database sources. Combinatorial optimization is then used to elucidate the set(s) of non-native functionalities, extracted from this Universal database, to add to the examined production host for enabling the desired product formation. Subsequently, competing functionalities that divert flux away from the targeted product are identified and removed to ensure higher product yields coupled with growth. This work represents an advancement over earlier efforts by establishing an integrated computational framework capable of constructing stoichiometrically balanced pathways, imposing maximum product yield requirements, pinpointing the optimal substrate(s), and evaluating different microbial hosts. The range and utility of OptStrain are demonstrated by addressing two very different product molecules. The hydrogen case study pinpoints reaction elimination strategies for improving hydrogen yields using two different substrates for three separate production hosts. In contrast, the vanillin study primarily showcases which non-native pathways need to be added into Escherichia coli. In summary, OptStrain provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.

Subfields: Environmental Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8555a3cb8b32158ec6b15da33c82054c0ac90f6b

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Citations in the corpus

Genome-scale models of bacterial metabolism: reconstruction and applications (2008, methodology): “In this aim, the OptStrain method was designed to systematically suggest additions of reactions to produce novel metabolites (Pharkya et al ., 2004).”
OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities (2012, methodology): “OptCom is solved using the solution methods previously developed for bilevel programs [48,49,50,51] (see Text S1 for details of the optimization formulation and solution).”
The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology (2019, background): “…30 insertions that result in growth-coupling (Burgard et al., 2003; Feist et al., 2010; Jensen 31 18 et al., 2019; Klamt and Mahadevan, 2015; Pharkya et al., 2004; von Kamp and Klamt, 1 2017), and such approaches have been experimentally validated (Alper et al., 2005; 2 Fong et al., 2005a;…”
Evolutionary programming as a platform for in silico metabolic engineering (2005, methodology): “[22,23] (named OptKnock) represents one of the first rational modeling frameworks for suggesting gene knockouts leading to the overproduction of a desired metabolite.”
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods (2012, background): “OptStrain goes further by conducting a novel pathway search within the metabolic network of the host to optimize the balance between reaction addition and deletion 78 .”
An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. (2006, methodology): “Recently, we proposed an integrated framework called OptStrain which extends OptKnock by pinpointing minimal reaction set recombination tasks to confer a desired non-native biochemical production capability on a microbial host (Pharkya et al., 2004).”
OptForce: An Optimization Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions (2010, background): “So far, computational strain design procedures have been applied for a variety of metabolic engineering projects including the overproduction of lactic acid [21,27], succinate [24,28–31], 1,3-propanediol [21], hydrogen [23], amino acids [32], L-lysine [33], L-valine [34], threonine [35], lycopene [3”