Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization

Burgard, Anthony P.; Pharkya, Priti; Maranas, Costas D.

doi:10.1002/bit.10803

Cited by 1,137 publications

(995 citation statements)

References 42 publications

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“…The slightly higher acetate accumulation in the batch phase observed for E. coli MDS40 may indicate that overflow metabolism can occur more easily in the deletion strain; however, more precise metabolic studies are necessary to clarify this point. Mathematical models of E. coli have demonstrated higher yields of certain metabolites as a result of gene deletions (Burgard et al, 2003); however, these models did not account for the specific genes deleted to create E. coli MDS40, due to lack of information regarding these genes. In all, the multiple deletion strain performs comparably to its parent strain under industrial conditions.…”

Section: Fed-batch Fermentationmentioning

confidence: 99%

Recombinant protein production in an Escherichia coli reduced genome strain

Sharma

Blattner

Harcum

2007

Metabolic Engineering

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Recently, efforts have been made to improve the properties of E. coli as a recombinant host by 'genomic surgery' -deleting large segments of the E. coli K12 MG1655 genome without scars.These excised segments included K-islands, which contain a high proportion of transposons, insertion sequences, cryptic phage, damaged, and unknown-function genes. The resulting multiple-deletion strain, designated E. coli MDS40, has a 14% (about 700 genes) smaller genome than the parent strain, E. coli MG1655. The multiple-deletion and parent E. coli strains were cultured in fed-batch fermenters to high cell densities on minimal medium to simulate industrial conditions for evaluating growth and recombinant protein production characteristics. Recombinant protein production and by-product levels were quantified at different controlled growth rates. These results indicate that the multiple-deletion strain's growth behavior and recombinant protein productivity closely matched the parent stain. Thus, the multiple-deletion strain E. coli MDS40 provides a suitable foundation for further genomic reduction.

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Section: Fed-batch Fermentationmentioning

confidence: 99%

Recombinant protein production in an Escherichia coli reduced genome strain

Sharma

Blattner

Harcum

2007

Metabolic Engineering

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“…Applications of the E. coli GEM range from pragmatic to theoretical studies, and can be classified into five general categories ( Fig. 3): 1) metabolic engineering [20][21][22][23][24][25][26][27][28][29][30] ; 2) biological discovery [31][32][33][34][35][36][37] ; 3) assessment of phenotypic behavior 19, ; 4) biological network analysis [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] ; and 5) studies of bacterial evolution [80][81][82] . The in silico methods used to probe the E. coli GEM in each study are summarized in Fig.…”

Section: Ask Not What You Can Do For a Reconstruction But What A Recmentioning

confidence: 99%

“…Through the application of computational methods that incorporate linear, mixed integer linear, and non-linear programming, it has been demonstrated that model-directed strain design can lead to increased metabolite production [20][21][22][23][24][25][26][27][28][29][30] . In these studies, the E. coli GEM is principally used to analyze the metabolite production potential of E. coli and identify metabolic interventions needed to enable the production of the product of interest.…”

Section: Applications Of Gems To Metabolic Engineering Of E Colimentioning

confidence: 99%

“…In a combined computational and experimental study, the bi-level optimization algorithm OptKnock 20 and iJR904 17 were utilized to overproduce lactate in E. coli 25 . The algorithm OptKnock optimizes two objective functions, biomass formation and product secretion, to produce strains that will couple the excretion of a desirable product to the growth rate.…”

Section: Applications Of Gems To Metabolic Engineering Of E Colimentioning

confidence: 99%

“…(A) A drawing of a predicted effect from a loss of function mutation in a simple system is shown. Metabolic engineering studies have investigated in silico strain design using E. coli metabolic reconstructions to overproduce desired products [20][21][22][23][24][25][26][27][28][29][30] . (B) Recent studies utilizing the reconstruction in a prospective manner have aimed to use the current biochemical and genetic information included in the metabolic network along with additional data types to drive biological discovery, such as predicting genes encoding for orphan reactions 32,33,[35][36][37] .…”

Section: Supplementary Materialsmentioning

confidence: 99%

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The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

2008

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The number and scope of methods developed to interrogate and use metabolic network reconstructions has significantly expanded since the first review of the use of constraint-based analysis in Nature Biotechnology some 14 years ago. In particular, the Escherichia coli metabolic network reconstruction has reached the genome-scale and has been broadly adapted. Specifically, it has been used to address a broad spectrum of basic and practical applications, falling into five main categories: 1) metabolic engineering, 2) model-directed discovery, 3) interpretations of phenotypic screens, 4) analysis of network properties, and 5) studies of evolutionary processes. With these accomplishments in hand, the field is expected to move forward and seek to further, i) broaden the scope and content of network reconstructions, ii) develop new and novel in silico analysis tools, and iii) expand in adaptation to uses of proximal and distal causation in biology. Taken together, these efforts will solidify a mechanistic genotype-phenotype relationship for microbial metabolism.The availability of reconstructed metabolic networks for microorganisms has increased rapidly in recent years, and a growing number of research groups are reconstructing metabolic networks for organisms of interest 1 . A network reconstruction represents a highly curated set of primary biological information for a particular organism and thus can be considered a biochemically, genetically and genomically structured (BiGG) data base 1, 2 . A curated BiGG data base (de facto a knowledge base) can be converted into a mathematical format (i.e., an in silico model), and used to computationally assess phenotypic properties using a variety of computational methods 2,3 . Genome-scale reconstructions are thus, a key step in quantifying the genotype-phenotype relationship and can be used to 'bring genomes to life' 4 . The purpose of this review is to summarize and classify applications utilizing the E. coli reconstruction to answer a broad spectrum of biological questions. These studies provide both an up to date review of the applications of constraint-based analysis and a guide to similar applications for the growing number of organisms for which genome-scale reconstructions are becoming available. The Key Steps in the Formulation of Genome-scale Metabolic Network ModelsThe four key steps in the formulation and use of genome-scale models are illustrated in Fig. 1. Foundational to the process is the generation of global, or genome-scale, omics data. Omics data, along with legacy information (i.e., the 'bibliome') and small-scale detailed experiments, can be used to define the interactions amongst the biological components that are used to reconstruct organism-specific networks 1 . Network reconstruction is also an iterative, on-going process that continually integrates data in a formal fashion as it becomes available 5 . As a result, a current and well curated genome-scale network reconstruction is a common denominator for those studying systems biology of an or...

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Cell‐specific genome‐scale metabolic modeling of SARS‐CoV‐2‐infected lung to identify antiviral enzymes

Chen,

Wang

2023

FEBS Open Bio

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Computational systems biology plays a key role in the discovery of suitable antiviral targets. We designed a cell‐specific, constraint‐based modeling technique for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2)‐infected lungs. We used the gene sequence of the alpha variant of SARS‐CoV‐2 to build a viral biomass reaction. We also used the mass proportion of lipids between the viral biomass and its host cell to estimate the stoichiometric coefficients of viral lipids in the reaction. We then integrated the viral biomass reaction, the gene expression of the alpha variant of SARS‐CoV‐2, and the generic human metabolic network Recon3D to reconstruct a cell‐specific genome‐scale metabolic model. An antiviral target discovery (AVTD) platform was introduced using this model to identify therapeutic drug targets for combating COVID‐19. The AVTD platform not only identified antiviral genes for eliminating viral replication but also predicted side effects of treatments. Our computational results revealed that knocking out dihydroorotate dehydrogenase (DHODH) might reduce the synthesis rate of cytidine‐5’‐triphosphate and uridine‐5’‐triphosphate, which terminate the viral building blocks of DNA and RNA for SARS‐CoV‐2 replication. Our results also indicated that DHODH is a promising antiviral target that causes minor side effects, which is consistent with the results of recent reports. Moreover, we discovered that the genes that participate in the de novo biosynthesis of glycerophospholipids and ceramides become unidentifiable if the viral biomass reaction does not involve the stoichiometry of lipids.

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Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization

Cited by 1,137 publications

References 42 publications

Recombinant protein production in an Escherichia coli reduced genome strain

Recombinant protein production in an Escherichia coli reduced genome strain

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

Cell‐specific genome‐scale metabolic modeling of SARS‐CoV‐2‐infected lung to identify antiviral enzymes

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