BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

King, Zachary A.; Lu, Justin S.; Dräger, Andreas; Miller, Philip; Federowicz, Stephen; Lerman, Joshua A.; Ebrahim, Ali; Palsson, Bernhard Ø.; Lewis, Nathan E.

doi:10.1093/nar/gkv1049

Cited by 787 publications

(795 citation statements)

References 41 publications

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“…The reconstruction was completed in the BiGG Models format (40), enabling standardization and cross-referencing to external databases (bigg.ucsd.edu). Combining an updated annotation, whole-genome RB-TnSeq data, and an advanced representation of light harvesting resulted in a comprehensive phototrophic model.…”

Section: Resultsmentioning

confidence: 99%

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

Broddrick

Rubin

Welkie

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology.cyanobacteria | constraint-based modeling | TCA cycle | photosynthesis | Synechococcus elongatus T he unicellular cyanobacterium Synechococcus elongatus PCC 7942 is being developed as a photosynthetic bioproduction platform for an array of industrial products (1-3). This model strain is attractive for this purpose because of its genetic tractability (4) and its reliance on mainly CO 2 , H 2 O, and light for metabolism, reducing the environmental and economic costs of cultivation. For low-cost, high-volume products, such as biofuels, however, one of the biggest challenges is attaining profitable product yields (5, 6). Genome-scale models (GEMs) of metabolism provide a valuable tool for increasing product titers by optimizing yield in silico and then, reproducing the changes in vivo (7). For instance, GEMs were used to select the optimal synthetic pathway for 3-hydroxypropanoate biosynthesis in Saccharomyces cerevisiae (8). In Escherichia coli, GEM optimization was used to realize heterologous production of 1,4-butanediol synthesis and increase titers three orders of magnitude (9). Although there have been numerous modeling efforts in Synechocystis sp. PCC 6803 (here in referred to as PCC 6803), this organism is highly divergent from S. elongatus, where limited modeling has been done (10).This deficit can partially be explained by the lack of in vivo validation datasets, such as 13 C metabolic flux analysis (MFA), for obligate phototrophs (11). Development of metabolic models of S. elongatus with strong experimental support is necessary to exploit the organism as a bioproduc...

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Section: Resultsmentioning

confidence: 99%

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

Broddrick

Rubin

Welkie

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

112

118

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“…The BIGG database is an online repository of curated genome-scale metabolic models [20]. Currently (Accessed December 2017) the database consists of 84 models from a wide diversity of organisms.…”

Section: Evaluation and Selection Of Model Organismsmentioning

confidence: 99%

In Silico Identification of Microbial Partners to Form Consortia with Anaerobic Fungi

et al. 2018

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Abstract:Lignocellulose is an abundant and renewable resource that holds great promise for sustainable bioprocessing. However, unpretreated lignocellulose is recalcitrant to direct utilization by most microbes. Current methods to overcome this barrier include expensive pretreatment steps to liberate cellulose and hemicellulose from lignin. Anaerobic gut fungi possess complex cellulolytic machinery specifically evolved to decompose crude lignocellulose, but they are not yet genetically tractable and have not been employed in industrial bioprocesses. Here, we aim to exploit the biomass-degrading abilities of anaerobic fungi by pairing them with another organism that can convert the fermentable sugars generated from hydrolysis into bioproducts. By combining experiments measuring the amount of excess fermentable sugars released by the fungal enzymes acting on crude lignocellulose, and a novel dynamic flux balance analysis algorithm, we screened potential consortia partners by qualitative suitability. Microbial growth simulations reveal that the fungus Anaeromyces robustus is most suited to pair with either the bacterium Clostridia ljungdahlii or the methanogen Methanosarcina barkeri-both organisms also found in the rumen microbiome. By capitalizing on simulations to screen six alternative organisms, valuable experimental time is saved towards identifying stable consortium members. This approach is also readily generalizable to larger systems and allows one to rationally select partner microbes for formation of stable consortia with non-model microbes like anaerobic fungi.

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“…To validate our model's results, we also analysed the metabolic networks of 61 prokaryotes obtained from the Biochemical Genetic and Genomic (BiGG) database [50], using the R package Sybil [51]. In these networks, we incorporated information about the linkage of the genes encoding metabolic reactions.…”

Section: (D) Modelling Recombination and Mutation In Metabolic Networkmentioning

confidence: 99%

Phenotypic innovation through recombination in genome-scale metabolic networks

2016

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Recombination is an important source of metabolic innovation, especially in prokaryotes, which have evolved the ability to survive on many different sources of chemical elements and energy. Metabolic systems have a wellunderstood genotype-phenotype relationship, which permits a quantitative and biochemically principled understanding of how recombination creates novel phenotypes. Here, we investigate the power of recombination to create genome-scale metabolic reaction networks that enable an organism to survive in new chemical environments. To this end, we use flux balance analysis, an experimentally validated computational method that can predict metabolic phenotypes from metabolic genotypes. We show that recombination is much more likely to create novel metabolic abilities than random changes in chemical reactions of a metabolic network. We also find that phenotypic innovation is more likely when recombination occurs between parents that are genetically closely related, phenotypically highly diverse, and viable on few rather than many carbon sources. Survival on a new carbon source preferentially involves reactions that are superessential, that is, essential in many metabolic networks. We validate our observations with data from 61 reconstructed prokaryotic metabolic networks. Our systematic and quantitative analysis of metabolic systems helps understand how recombination creates innovation.

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BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

Cited by 787 publications

References 41 publications

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

In Silico Identification of Microbial Partners to Form Consortia with Anaerobic Fungi

Phenotypic innovation through recombination in genome-scale metabolic networks

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