| 000 | 02856nab|a22003977a|4500 | ||
|---|---|---|---|
| 001 | 64714 | ||
| 003 | MX-TxCIM | ||
| 005 | 20211217225832.0 | ||
| 008 | 211206s2020 xxu||||| |||| 00| 0 eng d | ||
| 022 | _a1932-6203 (Online) | ||
| 024 | 8 | _ahttps://doi.org/10.1371/journal.pone.0227363 | |
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 100 | 1 |
_aVitkin, E. _925975 |
|
| 245 | 1 | 0 | _aDistributed flux balance analysis simulations of serial biomass fermentation by two organisms |
| 260 |
_aSan Francisco, CA (USA) : _bPublic Library of Science _c2020. |
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| 500 | _aPeer review | ||
| 500 | _aOpen Access | ||
| 520 | _aIntelligent biorefinery design that addresses both the composition of the biomass feedstock as well as fermentation microorganisms could benefit from dedicated tools for computational simulation and computer-assisted optimization. Here we present the BioLego Vn2.0 framework, based on Microsoft Azure Cloud, which supports large-scale simulations of biomass serial fermentation processes by two different organisms. BioLego enables the simultaneous analysis of multiple fermentation scenarios and the comparison of fermentation potential of multiple feedstock compositions. Thanks to the effective use of cloud computing it further allows resource intensive analysis and exploration of media and organism modifications. We use BioLego to obtain biological and validation results, including (1) exploratory search for the optimal utilization of corn biomasses—corn cobs, corn fiber and corn stover—in fermentation biorefineries; (2) analysis of the possible effects of changes in the composition of K. alvarezi biomass on the ethanol production yield in an anaerobic two-step process (S. cerevisiae followed by E. coli); (3) analysis of the impact, on the estimated ethanol production yield, of knocking out single organism reactions either in one or in both organisms in an anaerobic two-step fermentation process of Ulva sp. into ethanol (S. cerevisiae followed by E. coli); and (4) comparison of several experimentally measured ethanol fermentation rates with the predictions of BioLego. | ||
| 546 | _aText in English | ||
| 650 | 7 |
_aFermentation _2AGROVOC _912531 |
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| 650 | 7 |
_aEthanol _2AGROVOC _914591 |
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| 650 | 7 |
_aSaccharomyces cerevisiae _2AGROVOC _925976 |
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| 650 | 7 |
_aMaize _2AGROVOC _91173 |
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| 650 | 7 |
_aGlucose _2AGROVOC _922525 |
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| 650 | 7 |
_aBioenergy _2AGROVOC _92587 |
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| 650 | 7 |
_aOxygen _2AGROVOC _912478 |
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| 700 | 1 |
_aGillis, A. _925977 |
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| 700 | 1 |
_aPolikovsky, M. _925978 |
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| 700 | 1 |
_aBender, B. _925979 |
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| 700 | 1 |
_aGolberg, A. _925980 |
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| 700 | 1 |
_aYakhini, Z. _925981 |
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| 773 | 0 |
_tPLoS ONE _gv. 15, no. 1, e0227363 _dSan Francisco, CA (USA) : Public Library of Science, 2020. _x1932-6203 _wG94957 |
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| 856 | 4 |
_yClick here to access online _uhttps://doi.org/10.1371/journal.pone.0227363 |
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| 942 |
_cJA _n0 _2ddc |
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| 999 |
_c64714 _d64706 |
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