Optimization based automated curation of metabolic reconstructions

Kumar, Vinay Satish; Dasika, Madhukar S.; Maranas, Costas D.

doi:10.1186/1471-2105-8-212

Cited by 285 publications

(222 citation statements)

References 29 publications

Supporting

Mentioning

219

Contrasting

Order By: Relevance

“…Metabolic network gaps may be filled algorithmically by optimizing for shortest path connections [22] or through other penalties, such as prioritizing reactions catalyzed by enzymes that have higher homology for functional genomic annotations [23]. Either approach generates a list of reactions that permit network flux computationally, but are only predicted to have the modeled biological function.…”

Section: More Explicit Annotation Of Metabolic Network Reconstructionmentioning

confidence: 99%

Transparency in metabolic network reconstruction enables scalable biological discovery

Heavner

Price

2015

Current Opinion in Biotechnology

View full text Add to dashboard Cite

Reconstructing metabolic pathways has long been a focus of active research. Now, draft models can be generated from genomic annotation and used to simulate metabolic fluxes of mass and energy at the whole-cell scale. This approach has led to an explosion in the number of functional metabolic network models. However, more models have not led to expanded coverage of metabolic reactions known to occur in the biosphere. Thus, there exists opportunity to reconsider the process of reconstruction and model derivation to better support the less-scalable investigative processes of biocuration and experimentation. Realizing this opportunity to improve our knowledge of metabolism requires developing new tools that make reconstructions more useful by highlighting metabolic network knowledge limitations to guide future research.

show abstract

Section: More Explicit Annotation Of Metabolic Network Reconstructionmentioning

confidence: 99%

Transparency in metabolic network reconstruction enables scalable biological discovery

Heavner

Price

2015

Current Opinion in Biotechnology

View full text Add to dashboard Cite

show abstract

“…Genome-wide metabolic models often have difficulties modelling organelle metabolism (Satish Kumar et al 2007). However, since organelles compartmentalize their metabolic reactions, they can be abstracted from the rest of cellular metabolism.…”

Section: Subcellular Engineeringmentioning

confidence: 99%

Harnessing nature's toolbox: regulatory elements for synthetic biology

Boyle

Silver

2009

J. R. Soc. Interface.

View full text Add to dashboard Cite

Synthetic biologists seek to engineer complex biological systems composed of modular elements. Achieving higher complexity in engineered biological organisms will require manipulating numerous systems of biological regulation: transcription; RNA interactions; protein signalling; and metabolic fluxes, among others. Exploiting the natural modularity at each level of biological regulation will promote the development of standardized tools for designing biological systems.

show abstract

“…This algorithm provides a means of identifying the minimal set of reactions that must be made reversible or added to the model in order to activate as many gene-associated reactions in the model as possible. The constraints of the optimization problem resemble the constraints for existing classical gapfilling approaches (Satish Kumar et al, 2007;Kumar and Maranas, 2009).…”

Section: Model Pathway-gapfillingmentioning

confidence: 99%

Improved Evidence-Based Genome-Scale Metabolic Models for Maize Leaf, Embryo, and Endosperm

Seaver¹,

Bradbury²,

Frelin³

et al. 2016

Crop Breeding

View full text Add to dashboard Cite

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.

show abstract

Optimization based automated curation of metabolic reconstructions

Cited by 285 publications

References 29 publications

Transparency in metabolic network reconstruction enables scalable biological discovery

Transparency in metabolic network reconstruction enables scalable biological discovery

Harnessing nature's toolbox: regulatory elements for synthetic biology

Improved Evidence-Based Genome-Scale Metabolic Models for Maize Leaf, Embryo, and Endosperm

Contact Info

Product

Resources

About