Abstract
We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.
| Original language | English |
|---|---|
| Article number | e1003465 |
| Journal | PLoS Computational Biology |
| Volume | 10 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Jan 2014 |
| MoE publication type | A1 Journal article-refereed |
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Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species. / Pitkänen, Esa (Corresponding Author); Jouhten, Paula; Hou, Jian; Syed, Muhammad Fahad; Blomberg, Peter; Kludas, Jana; Oja, Merja; Holm, Liisa; Penttilä, Merja; Rousu, Juho; Arvas, Mikko.
In: PLoS Computational Biology, Vol. 10, No. 2, e1003465, 01.01.2014.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species
AU - Pitkänen, Esa
AU - Jouhten, Paula
AU - Hou, Jian
AU - Syed, Muhammad Fahad
AU - Blomberg, Peter
AU - Kludas, Jana
AU - Oja, Merja
AU - Holm, Liisa
AU - Penttilä, Merja
AU - Rousu, Juho
AU - Arvas, Mikko
N1 - CA2: BA3112 CA2: BA3136 CA2: BA311 ISI: BIOCHEMICAL RESEARCH METHODS
PY - 2014/1/1
Y1 - 2014/1/1
N2 - We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.
AB - We introduce a novel computational approach, CoReCo, for comparative metabolic reconstruction and provide genome-scale metabolic network models for 49 important fungal species. Leveraging on the exponential growth in sequenced genome availability, our method reconstructs genome-scale gapless metabolic networks simultaneously for a large number of species by integrating sequence data in a probabilistic framework. High reconstruction accuracy is demonstrated by comparisons to the well-curated Saccharomyces cerevisiae consensus model and large-scale knock-out experiments. Our comparative approach is particularly useful in scenarios where the quality of available sequence data is lacking, and when reconstructing evolutionary distant species. Moreover, the reconstructed networks are fully carbon mapped, allowing their use in 13C flux analysis. We demonstrate the functionality and usability of the reconstructed fungal models with computational steady-state biomass production experiment, as these fungi include some of the most important production organisms in industrial biotechnology. In contrast to many existing reconstruction techniques, only minimal manual effort is required before the reconstructed models are usable in flux balance experiments. CoReCo is available at http://esaskar.github.io/CoReCo/.
UR - http://www.scopus.com/inward/record.url?scp=84895735489&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1003465
DO - 10.1371/journal.pcbi.1003465
M3 - Article
VL - 10
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 2
M1 - e1003465
ER -