| Title |
Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM
|
|---|---|
| Published in |
PLoS Computational Biology, July 2013
|
| DOI | 10.1371/journal.pcbi.1003126 |
| Pubmed ID | |
| Authors |
Jonathan M. Dreyfuss, Jeremy D. Zucker, Heather M. Hood, Linda R. Ocasio, Matthew S. Sachs, James E. Galagan |
| Abstract |
The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 152 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 1% |
| Netherlands | 1 | <1% |
| Ireland | 1 | <1% |
| Denmark | 1 | <1% |
| United States | 1 | <1% |
| Luxembourg | 1 | <1% |
| Unknown | 145 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 43 | 28% |
| Researcher | 28 | 18% |
| Student > Master | 19 | 13% |
| Student > Bachelor | 13 | 9% |
| Student > Doctoral Student | 8 | 5% |
| Other | 19 | 13% |
| Unknown | 22 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 64 | 42% |
| Biochemistry, Genetics and Molecular Biology | 30 | 20% |
| Computer Science | 8 | 5% |
| Engineering | 7 | 5% |
| Chemical Engineering | 6 | 4% |
| Other | 13 | 9% |
| Unknown | 24 | 16% |