Distributed flux balance analysis simulations of serial biomass fermentation by two organisms
Autoři:
Edward Vitkin aff001; Amichai Gillis aff003; Mark Polikovsky aff003; Barak Bender aff003; Alexander Golberg aff003; Zohar Yakhini aff001
Působiště autorů:
Department of Computer Science, Technion – Israel Institute of Technology, Haifa, Israel
aff001; IBM Watson Health, Haifa, Israel
aff002; Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv, Israel
aff003; School of Computer Science, The Interdisciplinary Center, Herzliya, Israel
aff004
Vyšlo v časopise:
PLoS ONE 15(1)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0227363
Souhrn
Intelligent 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.
Klíčová slova:
Cloud computing – Ethanol – Fermentation – Glucose – Maize – Oxygen – Saccharomyces cerevisiae – Bioenergy feedstock
Zdroje
1. Bataille C, Åhman M, Neuhoff K, Nilsson LJ, Fischedick M, Lechtenböhmer S, et al. A review of technology and policy deep decarbonization pathway options for making energy-intensive industry production consistent with the Paris Agreement. J Clean Prod. 2018;187: 960–973. doi: 10.1016/J.JCLEPRO.2018.03.107
2. Sy CL, Ubando AT, Aviso KB, Tan RR. Multi-objective target oriented robust optimization for the design of an integrated biorefinery. J Clean Prod. 2018;170: 496–509. doi: 10.1016/j.jclepro.2017.09.140
3. Sorek N, Yeats TH, Szemenyei H, Youngs H, Somerville CR. The implications of lignocellulosic biomass chemical composition for the production of advanced biofuels. Bioscience. 2014;64: 192–201. doi: 10.1093/biosci/bit037
4. Fatih Demirbas M. Biorefineries for biofuel upgrading: A critical review. Appl Energy. 2009;86: S151–S161. doi: 10.1016/j.apenergy.2009.04.043
5. Wang EX, Ding MZ, Ma Q, Dong XT, Yuan YJ. Reorganization of a synthetic microbial consortium for one-step vitamin C fermentation. Microb Cell Fact. 2016;15. doi: 10.1186/s12934-016-0418-6 26809519
6. Masuo S, Zhou S, Kaneko T, Takaya N. Bacterial fermentation platform for producing artificial aromatic amines. Sci Rep. 2016;6. doi: 10.1038/srep25764 27167511
7. Jia X, Liu C, Song H, Ding M, Du J, Ma Q, et al. Design, analysis and application of synthetic microbial consortia. Synth Syst Biotechnol. 2016;1: 109–117. doi: 10.1016/j.synbio.2016.02.001 29062933
8. van Maris AJA, Abbott DA, Bellissimi E, van den Brink J, Kuyper M, Luttik MAH, et al. Alcoholic fermentation of carbon sources in biomass hydrolysates by Saccharomyces cerevisiae: Current status. Antonie van Leeuwenhoek, International Journal of General and Molecular Microbiology. 2006. pp. 391–418. doi: 10.1007/s10482-006-9085-7 17033882
9. Bond-Watts BB, Bellerose RJ, Chang MCY. Enzyme mechanism as a kinetic control element for designing synthetic biofuel pathways. Nat Chem Biol. 2011;7: 222–7. doi: 10.1038/nchembio.537 21358636
10. Widder S, Allen RJ, Pfeiffer T, Curtis TP, Wiuf C, Sloan WT, et al. Challenges in microbial ecology: Building predictive understanding of community function and dynamics. ISME Journal. 2016. pp. 2557–2568. doi: 10.1038/ismej.2016.45 27022995
11. Zomorrodi AR, Maranas CD. OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. Rao, editor. PLoS Comput Biol. 2012;8: e1002363. doi: 10.1371/journal.pcbi.1002363 22319433
12. Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc. 2009;4: 1184–91. doi: 10.1038/nprot.2009.97 19617889
13. van Iersel MP, Pico AR, Kelder T, Gao J, Ho I, Hanspers K, et al. The BridgeDb framework: standardized access to gene, protein and metabolite identifier mapping services. BMC Bioinformatics. 2010;11: 5. doi: 10.1186/1471-2105-11-5 20047655
14. Khandelwal RA, Olivier BG, Röling WFM, Teusink B, Bruggeman FJ. Community Flux Balance Analysis for Microbial Consortia at Balanced Growth. PLoS One. 2013. doi: 10.1371/journal.pone.0064567 23741341
15. Golberg A, Vitkin E, Linshiz G, Khan SA, Hillson NJ, Yakhini Z, et al. Proposed design of distributed macroalgal biorefineries: Thermodynamics, bioconversion technology, and sustainability implications for developing economies. Biofuels, Bioprod Biorefining. 2014;8: 67–82. doi: 10.1002/bbb.1438
16. Vitkin E, Golberg A, Yakhini Z. BioLEGO—a web-based application for biorefinery design and evaluation of serial biomass fermentation. TECHNOLOGY. 2015;3. doi: 10.1142/S2339547815400038
17. Deindoerfer FH, Humphrey AE. Design of Multistage Systems for Simple Fermentation Processes. Ind Eng Chem. 1959;51: 809–812. doi: 10.1021/ie50595a023
18. Wu W-J, Zhang A-H, Peng C, Ren L-J, Song P, Yu Y-D, et al. An efficient multi-stage fermentation strategy for the production of microbial oil rich in arachidonic acid in Mortierella alpina. Bioresour Bioprocess. 2017;4: 8. doi: 10.1186/s40643-017-0138-8 28163995
19. Golberg A, Vitkin E, Yakhini Z. Seaweed biorefineries: exergy efficiency, fermentation and sustainability implications; example of potential production of bioethanol from Kappaphycus alvarezzi in Philippines. Proceedings of ECOS 2015—the 28th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems. 2015. pp. 1–12.
20. Stelling J, Klamt S, Bettenbrock K, Schuster S, Gilles ED. Metabolic network structure determines key aspects of functionality and regulation. Nature. 2002;420: 190–193. doi: 10.1038/nature01166 12432396
21. Vitkin E, Shlomi T. MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks. 2012;13: R111. doi: 10.1186/gb-2012-13-11-r111 23194418
22. Jiang R, Linzon Y, Vitkin E, Yakhini Z, Chudnovsky A, Golberg A. Thermochemical hydrolysis of macroalgae Ulva for biorefinery: Taguchi robust design method. Sci Rep. 2016;6: 27761. doi: 10.1038/srep27761 27291594
23. Orth JD, Conrad TM, Na J, Lerman J a, Nam H, Feist AM, et al. A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011. Mol Syst Biol. 2011;7: 535. doi: 10.1038/msb.2011.65 21988831
24. Raman K, Chandra N. Flux balance analysis of biological systems: applications and challenges. Brief Bioinform. 2009;10: 435–49. doi: 10.1093/bib/bbp011 19287049
25. Mahadevan R, Schilling CHH. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng. 2003;5: 264–276. doi: 10.1016/j.ymben.2003.09.002 14642354
26. Liu T, Khosla C. Genetic Engineering of Escherichia coli for Biofuel Production. Annu Rev Genet. 2010;44: 53–69. doi: 10.1146/annurev-genet-102209-163440 20822440
27. Burgard AP, Pharkya P, Maranas CD. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng. 2003;84: 647–57. doi: 10.1002/bit.10803 14595777
28. Tepper N, Shlomi T. Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics. 2010;26: 536–43. doi: 10.1093/bioinformatics/btp704 20031969
29. Vitkin E, Yakhini Z. Computational Aspects of Metabolic Processes: Modeling, Analysis and Applications. Technion—Israel Institute of Technology. 2018. http://www.cs.technion.ac.il/users/wwwb/cgi-bin/tr-info.cgi/2018/PHD/PHD-2018-12
30. GLPK—GNU Project—Free Software Foundation (FSF).
31. Yeast Extract–Peptone–Dextrose (YPD) Medium (Liquid or Solid). Cold Spring Harb Protoc. 2017;2017: pdb.rec090563.
32. LB (Luria-Bertani) liquid medium. Cold Spring Harb Protoc. 2006;2006: pdb.rec8141.
33. Chemodanov A, Jinjikhashvily G, Habiby O, Liberzon A, Israel A, Yakhini Z, et al. Net primary productivity, biofuel production and CO2 emissions reduction potential of Ulva sp. (Chlorophyta) biomass in a coastal area of the Eastern Mediterranean. Energy Convers Manag. 2017;148: 1497–1507. doi: 10.1016/j.enconman.2017.06.066
34. Shefer S, Israel A, Golberg A, Chudnovsky A. Carbohydrate-based phenotyping of the green macroalga Ulva fasciata using near-infrared spectrometry: Potential implications for marine biorefinery. Bot Mar. 2017;60: 219–228. doi: 10.1515/bot-2016-0039
35. Korzen L, Pulidindi IN, Israel A, Abelson A, Gedanken A. Single step production of bioethanol from the seaweed Ulva rigida using sonication. RSC Adv. 2015;5: 16223–16229. doi: 10.1039/C4RA14880K
36. Trivedi N, Gupta V, Reddy CRK, Jha B. Enzymatic hydrolysis and production of bioethanol from common macrophytic green alga Ulva fasciata Delile. Bioresour Technol. 2013;150: 106–112. doi: 10.1016/j.biortech.2013.09.103 24157682
37. Baldwin AR, Sniegowski MS. Fatty acid compositions of lipids from corn and grain sorghum kernels. J Am Oil Chem Soc. 1951;28: 24–27. doi: 10.1007/BF02639745
38. Mcaloon A, Taylor F, Yee W, Ibsen K, Wooley R. Determining the Cost of Producing Ethanol from Corn Starch and Lignocellulosic Feedstocks. 2000.
39. van Eylen D, van Dongen F, Kabel M, de Bont J. Corn fiber, cobs and stover: Enzyme-aided saccharification and co-fermentation after dilute acid pretreatment. Bioresour Technol. 2011;102: 5995–6004. doi: 10.1016/j.biortech.2011.02.049 21392979
40. McAnulty MJ, Yen JY, Freedman BG, Senger RS. Genome-scale modeling using flux ratio constraints to enable metabolic engineering of clostridial metabolism in silico. BMC Syst Biol. 2012;6: 42. doi: 10.1186/1752-0509-6-42 22583864
41. Heavner BD, Smallbone K, Barker B, Mendes P, Walker LP. Yeast 5—an expanded reconstruction of the Saccharomyces cerevisiae metabolic network. BMC Syst Biol. 2012;6: 55. doi: 10.1186/1752-0509-6-55 22663945
42. Ingle K, Vitkin E, Robin A, Yakhini Z, Mishori D, Golberg A. Macroalgae Biorefinery from Kappaphycus alvarezii: Conversion Modeling and Performance Prediction for India and Philippines as Examples. BioEnergy Res. 2017; 1–11. doi: 10.1007/s12155-017-9874-z
43. Eden E, Lipson D, Yogev S, Yakhini Z. Discovering Motifs in Ranked Lists of DNA Sequences. PLoS Comput Biol. 2007;3: e39. doi: 10.1371/journal.pcbi.0030039 17381235
44. Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics. 2009;10: 48. doi: 10.1186/1471-2105-10-48 19192299
45. Hagman A, Säll T, Piškur J. Analysis of the yeast short-term Crabtree effect and its origin. FEBS J. 2014;281: 4805–4814. doi: 10.1111/febs.13019 25161062
46. Andersent KB, Von Meyenburgt K. Are Growth Rates of Escherichia coli in Batch Cultures Limited by Respiration? J Bacteriol. 1980;144: 114–123. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC294601/pdf/jbacter00571-0128.pdf 6998942
47. Burgard AP, Nikolaev E V, Schilling CH, Maranas CD. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res. 2004;14: 301–12. doi: 10.1101/gr.1926504 14718379
48. Mahadevan R, Edwards JS, Doyle FJ. Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J. 2002;83: 1331–40. doi: 10.1016/S0006-3495(02)73903-9 12202358
Článek vyšel v časopise
PLOS One
2020 Číslo 1
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Proč při poslechu některé muziky prostě musíme tančit?
- Je libo čepici místo mozkového implantátu?
- Chůze do schodů pomáhá prodloužit život a vyhnout se srdečním chorobám
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
Nejčtenější v tomto čísle
- Severity of misophonia symptoms is associated with worse cognitive control when exposed to misophonia trigger sounds
- Chemical analysis of snus products from the United States and northern Europe
- Calcium dobesilate reduces VEGF signaling by interfering with heparan sulfate binding site and protects from vascular complications in diabetic mice
- Effect of Lactobacillus acidophilus D2/CSL (CECT 4529) supplementation in drinking water on chicken crop and caeca microbiome
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy