Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

Vongsangnak, Wanwipa; Olsen, Peter Bjarke; Hansen, Kim Toft; Krogsgaard, Steen; Nielsen, Jens

doi:10.1186/1471-2164-9-245

Cited by 80 publications

(92 citation statements)

References 36 publications

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“…In this research, we focused on using MeGaFiller to fill metabolic gaps in the reconstructed metabolic network of A. oryzae iWV1314 [3]. However, we have also run MeGafiller on the other four networks, those for S. cerevisiae iIN800 [2], A. niger iMA871 [5], A. nidulans iHD666 [4], as well as S. coelicolor iIB711 [6].…”

Section: Data Sourcesmentioning

confidence: 99%

“…Finally, candidate gene (if any) is manually validated by querying with annotation databases. These gap filling methods have been successfully used to fill some of the gaps in the metabolic networks of Escherichia coli [19] and A. oryzae [3]. Despite that, many gaps still remain in these networks.…”

Section: Metabolic Gaps and Their Implicationsmentioning

confidence: 99%

“…For examples, in the reconstructed metabolic networks of yeast Saccharomyces cerevisiae [2], filamentous fungi Aspergillus oryzae [3], Aspergillus nidulans [4], and Aspergillus niger [5], and bacterium Streptomyces coelicolor [6], between 6% to 19% of the biochemical reactions are metabolic gaps.…”

Section: Metabolic Gaps and Their Implicationsmentioning

confidence: 99%

“…other related organisms) are based on the profile of the protein family, such as GFAOP [3] and Reed et al [19]. In these methods, the first step is to identify the specific protein family for the metabolic gap.…”

Section: Metabolic Gaps and Their Implicationsmentioning

confidence: 99%

“…In recent years, there has been an effort to reconstruct the genomescale metabolic networks for hundreds species [2][3][4][5][6][7]. In principle, the reconstruction of metabolic networks is an iterative multi-stage process [8,9], which starts from gene annotation, and goes all the way to network development.…”

Section: Introductionmentioning

confidence: 99%

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Megafiller: A Retrofitted Protein Function Predictor for Filling Gaps in Metabolic Networks

Nguyen¹,

Vongsangnak²,

Shen³

et al. 2014

J Proteomics Bioinform

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BackgroundThe metabolic network of a cell is the complete set of interconnected metabolic processes that determine the physiological and biochemical properties of the cell. In recent years, metabolic networks have enormously contributed to our understanding of metabolic genotype and phenotype relationship. This leads to important applications through systems biology and metabolic engineering. Recently, metaproteome-scale metabolic network reconstruction has also emerged as a promising and challenging approach for investigating the metabolism of microbial communities [1].In recent years, there has been an effort to reconstruct the genomescale metabolic networks for hundreds species [2][3][4][5][6][7]. In principle, the reconstruction of metabolic networks is an iterative multi-stage process [8,9], which starts from gene annotation, and goes all the way to network development. Several sophisticated techniques have been developed for metabolic network reconstruction [10][11][12][13][14][15]. Metabolic Gaps and their ImplicationsHowever, most of the reconstructed networks remain incomplete, namely there are significant numbers of metabolic gaps [16,17]. A metabolic gap in a network for genome (G) is a metabolic reaction (R) (described by its EC number) that is present in the network. But the annotation through the network reconstruction methods have failed to find the corresponding gene in G that is responsible for that reaction R. We distinguish two types of metabolic gaps: local metabolic gap where the corresponding gene responsible for R can be found in other related organisms and global metabolic gap where the corresponding gene responsible for R has not been found in any known organism or have not been so annotated in any genome. [5], and bacterium Streptomyces coelicolor [6], between 6% to 19% of the biochemical reactions are metabolic gaps. Metabolic gaps impede downstream biological analysis of these metabolic networks. For examples, in the reconstructed metabolic networks of yeast Saccharomyces cerevisiae [2], filamentous fungi Aspergillus oryzae [3], Aspergillus nidulans [4], and Aspergillus nigerFilling these metabolic gaps (and thus, enhancing these networks) is the most time-consuming task that may take years to complete since there is a lot of manual curation involved [16][17][18]. This can be a bottleneck for gaining high quality metabolic networks. Our work therefore proposes to fill those metabolic gaps by considering new algorithmic methods. Current Metabolic Gap Filling MethodsCurrent direct methods for filling local metabolic gaps (i.e. genes that are un-annotated in the target organism, but have been found in Abstract Background: A bottleneck in investigating the cellular metabolism and physiology of organisms is the presence of metabolic gaps in the genome-scale metabolic networks. Metabolic gaps are reactions in the network that the corresponding genes have not yet been identified. Previous gap filling methods are generally based on identifying protein family in related organisms and then use ...

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Section: Data Sourcesmentioning

confidence: 99%

Section: Metabolic Gaps and Their Implicationsmentioning

confidence: 99%

Section: Metabolic Gaps and Their Implicationsmentioning

confidence: 99%

Section: Metabolic Gaps and Their Implicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Megafiller: A Retrofitted Protein Function Predictor for Filling Gaps in Metabolic Networks

Nguyen¹,

Vongsangnak²,

Shen³

et al. 2014

J Proteomics Bioinform

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Enzymes, Detergent

Maurer¹

2010

Encyclopedia of Industrial Biotechnology

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Enzymes used in laundry and dish‐wash detergents represent one of the major application fields of enzymes. The hydrolases used in detergents are considered essential for removal of stains originating from nature. Some enzymes like cellulases do also contribute to antigraying effects by pigment removal and antiredeposition activity. Enzymes (proteases) were first introduced in laundry detergents at the end of the 1960s. Since then, they have become an important topic for product differentiation and performance claims. Thus the work on improving enzyme performance in terms of improved performance and stability in storage and process has become a major field of enzyme development. All relevant technologies for enzyme engineering have been applied in the meantime, resulting in a patent landscape of extreme complexity. In order to allow for efficient and economic production of technical enzymes, classical strain improvement has been combined with recombinant production systems early on. Detergent enzymes represent the only example of biocatalysis performed in a household setting. Since the potential for inhalation sensitization has been identified with detergent proteases on workers, the risk assessment both, for occupational as well as for the consumer setting has been a key topic that has been successfully managed since 1970. The upcoming field of synthetic biology promises to deliver further improvement of production systems and new enzymatic activities.

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Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

Masoudi‐Nejad

Salehzadeh‐Yazdi

Akbari‐Birgani

et al. 2013

Biological Knowledge Discovery Handbook

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INTRODUCTIONGenome-scale metabolic reconstruction is the ultimate goal of system biologists for finding out the genotype-phenotype relationship. For achieving this purpose, we need enough knowledge about the metabolic pathways and genome annotation. During the present study, we aim to describe the foundational concepts, central to omics data, model formulation, history, method, and applications of metabolic network reconstruction as well as some related sources. In Section 45.2, we describe omics data and high-throughput technologies for gaining these data. In section 45.3, we present different ways for metabolic network modeling. In section 45.4, we summarize the history of genome-scale modeling as one of the most important methods for metabolic network modeling. In sections 45.5 and 45.6, we elucidate how genome-scale metabolic models could be generated and what their applications are. Finally, in section 45.7, we review biochemical pathways and genome annotation databases. OMICs ERAInnovative omics technologies such as genomics, transcriptomics, proteomics, and metabolomics facilitate a strategy toward the simultaneous analysis of the large number of genes, transcripts, proteins, and metabolites. Therefore, a huge volume of data has generated about the make-up of cells and their behavior at various cellular levels and different environmental conditions, which enable us to reconstruct genome-scale biomolecular networks (e.g., transcriptional regulatory networks, interactomic networks, and metabolic networks)Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data, First Edition. Edited by Mourad Elloumi and Albert Y. Zomaya.

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Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

Cited by 80 publications

References 36 publications

Megafiller: A Retrofitted Protein Function Predictor for Filling Gaps in Metabolic Networks

Megafiller: A Retrofitted Protein Function Predictor for Filling Gaps in Metabolic Networks

Enzymes, Detergent

Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

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