In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production

Bro, Christoffer; Regenberg, Birgitte; Förster, Jochen; Nielsen, Jens

doi:10.1016/j.ymben.2005.09.007

Cited by 320 publications

(228 citation statements)

References 32 publications

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“…The modeling work presented here represents a first step in in silico design of microbial consortia for efficient consumption of pentose and hexose sugars derived from lignocellulosic biomass. Additional improvements may be achieved through in silico implementation of metabolic engineering strategies, in particular gene insertions experimentally shown to increase ethanol production in S. cerevisiae (Bro et al, 2006) and E. coli (Ohta et al, 1991). Future work will focus on experimental validation of the model assumptions and predictions for the E. coli/S.…”

Section: Discussionmentioning

confidence: 99%

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Hanly

Henson

2010

Biotech & Bioengineering

127

131

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Sequential uptake of pentose and hexose sugars that compose lignocellulosic biomass limits the ability of pure microbial cultures to efficiently produce value-added bioproducts. In this work, we used dynamic flux balance modeling to examine the capability of mixed cultures of substrate-selective microbes to improve the utilization of glucose/xylose mixtures and to convert these mixed substrates into products. Co-culture simulations of Escherichia coli strains ALS1008 and ZSC113, engineered for glucose and xylose only uptake respectively, indicated that improvements in batch substrate consumption observed in previous experimental studies resulted primarily from an increase in ZSC113 xylose uptake relative to wild-type E. coli. The E. coli strain ZSC113 engineered for the elimination of glucose uptake was computationally co-cultured with wild-type Saccharomyces cerevisiae, which can only metabolize glucose, to determine if the co-culture was capable of enhanced ethanol production compared to pure cultures of wild-type E. coli and the S. cerevisiae strain RWB218 engineered for combined glucose and xylose uptake. Under the simplifying assumption that both microbes grow optimally under common environmental conditions, optimization of the strain inoculum and the aerobic to anaerobic switching time produced an almost twofold increase in ethanol productivity over the pure cultures. To examine the effect of reduced strain growth rates at non-optimal pH and temperature values, a break even analysis was performed to determine possible reductions in individual strain substrate uptake rates that resulted in the same predicted ethanol productivity as the best pure culture.

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Section: Discussionmentioning

confidence: 99%

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Hanly

Henson

2010

Biotech & Bioengineering

127

131

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“…Combinatorial enzyme deletion phenotypes can be explored systematically by constraining specific enzyme-reaction fluxes to zero (for example, Edwards and Palsson 2000;Forster et al 2003). This approach provides reasonable approximations of genomewide biochemical processes in several model organisms (Edwards and Palsson 2000;Duarte et al 2004;Bro et al 2006;Hjersted et al 2007;Oh et al 2007;Resendis-Antonio et al 2007;Becker and Palsson 2008;Motter et al 2008).…”

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confidence: 99%

Systems-Level Engineering of Nonfermentative Metabolism in Yeast

Kennedy

Boyle²,

Waks

et al. 2009

Genetics

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We designed and experimentally validated an in silico gene deletion strategy for engineering endogenous one-carbon (C1) metabolism in yeast. We used constraint-based metabolic modeling and computer-aided gene knockout simulations to identify five genes (ALT2, FDH1, FDH2, FUM1, and ZWF1), which, when deleted in combination, predicted formic acid secretion in Saccharomyces cerevisiae under aerobic growth conditions. Once constructed, the quintuple mutant strain showed the predicted increase in formic acid secretion relative to a formate dehydrogenase mutant ( fdh1 fdh2), while formic acid secretion in wild-type yeast was undetectable. Gene expression and physiological data generated post hoc identified a retrograde response to mitochondrial deficiency, which was confirmed by showing Rtg1-dependent NADH accumulation in the engineered yeast strain. Formal pathway analysis combined with gene expression data suggested specific modes of regulation that govern C1 metabolic flux in yeast. Specifically, we identified coordinated transcriptional regulation of C1 pathway enzymes and a positive flux control coefficient for the branch point enzyme 3-phosphoglycerate dehydrogenase (PGDH).Together, these results demonstrate that constraint-based models can identify seemingly unrelated mutations, which interact at a systems level across subcellular compartments to modulate flux through nonfermentative metabolic pathways.

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“…This growth expectation is in part based on the on-going reconstruction of additional cellular processes, such as transcriptional regulation and protein production. Computations based on genome-scale models are also beginning to influence other areas of industrial microbiology such as generation of renewable energy [88][89][90] and bioremediation 89 .…”

Section: Applications Of Gems To Metabolic Engineering Of E Colimentioning

confidence: 99%

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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In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production

Cited by 320 publications

References 32 publications

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Dynamic flux balance modeling of microbial co‐cultures for efficient batch fermentation of glucose and xylose mixtures

Systems-Level Engineering of Nonfermentative Metabolism in Yeast

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

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