|本期目录/Table of Contents|

[1]郭静,徐自祥,付亚星,等.产电微生物基因组及代谢网络分析[J].应用与环境生物学报,2012,18(06):1075-1084.[doi:10.3724/SP.J.1145.2012.01075]
 GUO Jing,XU Zixiang,FU Yaxing,et al.Analysis on Electricigen Genomes and Metabolic Networks[J].Chinese Journal of Applied & Environmental Biology,2012,18(06):1075-1084.[doi:10.3724/SP.J.1145.2012.01075]
点击复制

产电微生物基因组及代谢网络分析()
分享到:

《应用与环境生物学报》[ISSN:1006-687X/CN:51-1482/Q]

卷:
18卷
期数:
2012年06期
页码:
1075-1084
栏目:
综述
出版日期:
2012-12-25

文章信息/Info

Title:
Analysis on Electricigen Genomes and Metabolic Networks
作者:
郭静 徐自祥 付亚星 刘碧芸 孟静 肖可 付德刚 孙啸
(1东南大学生物电子学国家重点实验室,生物科学与医学工程学院 南京 210009)
(2中国科学院天津工业生物技术研究所,系统微生物工程中国科学院重点实验室 天津 300308)
Author(s):
GUO Jing XU Zixiang FU Yaxing LIU Biyun MENG Jing XIAO Ke FU Degang SUN Xiao
(1State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China)
(2Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China)
关键词:
微生物燃料电池产电微生物比较基因组分析基因表达谱分析代谢网络建模网络优化算法
Keywords:
microbial fuel cell electricigen comparative genome analysis gene expression analysis metabolic network reconstruction network optimization
分类号:
TM911.45 : Q78
DOI:
10.3724/SP.J.1145.2012.01075
文献标志码:
A
摘要:
产电微生物的生物信息学分析是微生物燃料电池(Microbial Fuel Cell,MFC)研究中的关键环节,各种生物信息学分析方法已经开始应用于产电微生物研究. 本文综述了目前产电微生物基因组、功能基因组和代谢网络分析的重要方法,包括基因和基因表达信息分析、基因组和比较基因组分析、代谢网络建模和计算机模拟等,其中,产电微生物代谢网络的构建是联系上游基因组分析和下游基因工程改造的关键,是目前相关研究面临的挑战. 生物信息学分析必将促进干实验与湿实验的紧密结合,促进发现电子转移相关功能基因,解析微生物产电机制,优化代谢网络,由此指导基因工程改造产电微生物,最终提高产电效率. 图1 表3 参86
Abstract:
Bioinformatics research on electricigens is the key process in microbial fuel cell (MFC) research. A variety of bioinformatics analysis methods have been applied in this field. In this review, the mainstream bioinformatics applications are summarized in the studies on electricigen genome, functional genome and metabolic network, such as gene and gene expression analysis, genome and comparative genome analysis, metabolic network reconstruction and in silico simulation. Metabolic network reconstruction is the key part between upstream genome analysis and downstream gene engineering, which is also the challenge of current related researches. Bioinformatics analysis will promote the integration between the dry experiment and wet experiment. It is used to explore the molecular mechanism of electron transfer and find key genes, optimize metabolic network, then genetic engineering methods are applied to achieve the ultimate purpose of improving the efficiency of electricity production. Fig 1, Tab 3, Ref 86

参考文献/References:

1 Potter MC. On the difference of potential due to the vital activity of microorganisms. Proc Univ Durham Phil Soc, 1910, 3: 245~249
2 Potter MC. Electrical effects accompanying the decomposition of organic compounds. Proc R Soc Lond B, 1911, 84: 260~276
3 Lovley D. Microbial fuel cells: novel microbial physiologies and engineering approaches. Energy Biotechnol Biol, 2006, 17 (3): 327~332
4 Rogan BE. Simultaneous wastewater treatment and biological electricity generation. Water Sci Technol, 2005, 52: 31~37
5 Lovley D. Bug juice: havesting electricity with microorganisms. Nat Rev Microbiol, 2006, 4: 497~508
6 Logan BE, Hamelers B, Rozendal R, Schröder U, Keller J, Freguia S, Aelterman P, Verstraete W, Rabaey K. Microbial fuel cells: methodology and technology. Environ Sci & Techonol, 2006, 40 (17): 5181~5192
7 Li DL (李登兰), Hong YG (洪义国), Xu MY (许玫英), Luo HD (罗慧东), Sun GP (孙国萍). Progress in construction of microbial fuel cell. Chin J Appl Environ Biol (应用与环境生物学报), 2008, 14 (1): 147~152
8 Cheng S, Liu H, Logan BE. Increased power generation in a continuous flow MFC with advective flow through the porous anode and reduced electrode spacing. Environ Sci Technol, 2006, 40 (7): 1468~3083
9 Logan BE, Regan JM. Electricity-producing bacterial communities in microbial fuel cells. Trends Microbiol, 2006, 14 (12): 512~5189
10 Phung NT, Lee J, Kang KH, Chang IS, Gadd GM, Kim BH. Analysis of microbial diversity in oligotrophic microbial fuel cells using 16S rDNA sequences. FEMS Microbiol Lett, 2004, 233: 77~82
11 Hong YG (洪义国), Guo J (郭俊), Sun GP (孙国萍). Recent progress in electricigens and microbial fuel cell. Acta Microbiol Sin (微生物学报), 2007, 47 (1): 173~177
12 LiuM (刘敏), ShaoJ (邵军), ZhouB (周奔), Zhou SG (周顺桂), Ni JR (倪晋仁). Progress in research of microbial electricigenic respiration. Chin J Appl Environ Biol (应用与环境生物学报), 2010, 16 (3): 445~452
13 Wan XF, VerBerkmoes NC, McCue LA, Stanek D, Connelly H, Hauser LJ, Wu L, Liu X, Yan T, Leaphart A, Hettich RL, Zhou J, Thompson DK. Transcriptomic and proteomic characterization of the Fur Modulon in the metal-reducing bacterium Shewanella oneidensis. J Bacteriol, 2004, 186 (24): 8385~8400
14 Newton GJ, Mori S, Nakamura R, Hashimoto K, Watanabe K. Analysis of current-generating mechanisms of Shewanella loihica PV-4 and Shewanella oneidensis MR-1 in microbial fuel cell. Appl & Environ Microbiol, 2009, 75 (24): 7674~7681
15 Nagarajan H, Butler JE, Klimes A, Qiu Y, Zengler K, Ward J, Young ND, Methé BA, Palsson B?, Lovley DR, Barrett CL. De novo assembly of the complete genome of an enhanced electricity-producing variant of Geobacter sulfurreducens using only short reads. PLoS ONE, 2010, 5 (6): e10922
16 Qiu Y, Cho BK, Park YS, Lovley D, Palsson B?, Zengler K. Structural and operational complexity of the Geobacter sulfurreducens genome. Genome Res, 2010, 20 (9): 1304~1311
17 Lovley DR, Holmes DE, Nevin KP. Dissimilatory Fe(Ⅲ) and Mn(Ⅳ) reduction. Adv Microb Physiol, 2004, 49: 219~286
18 Tsoi E. Optimizing the metabolism of Geobacter metallireducens, a metal breathing bacteria: [Degree Thesis]. University of Toronto, 2008
19 Risso C, Sun J, DeBoy R, Ismail W, Shrivastava S, Huot H, Kothari S, Daugherty S, Bui O, Schilling CH, Lovley DR, Methé BA. Genome-scale comparison and constraint-based metabolic reconstruction of the facultative anaerobic Fe(Ⅲ)-reducer Rhodoferax ferrireducens. BMC Genomics, 2009, 10: 447
20 Zhang JT (张锦涛), Ni JR (倪晋仁), Zhou SG (周顺桂). Progress in research of microbial fuel cells based on Fe(Ⅲ)-reducing bacteria. Chin J Appl Environ Biol (应用与环境生物学报), 2008, 14 (2): 290~295
21 Overbeek R, Zhuang K, Mahadevan R, Begley T, Butler RM, Choudhuri JV, Chuang HY, Cohoon M, de Crécy-Lagard V, Diaz N, Disz T, Edwards R, Fonstein M, Frank ED, Gerdes S, Glass EM, Goesmann A, Hanson A, Iwata-Reuyl D, Jensen R, Jamshidi N, Krause L, Kubal M, Larsen N, Linke B, McHardy AC, Meyer F, Neuweger H, Olsen G, Olson R, Osterman A, Portnoy V, Pusch GD, Rodionov DA, Rückert C, Steiner J, Stevens R, Thiele I, Vassieva O, Ye Y, Zagnitko O, Vonstein V. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res, 2005, 33: 5691~5702
22 Li L, Stoeckert CJ Jr, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res, 2003, 13 (9): 2178~2189
23 Tatusov RL, Koonin EV, Lipman DJ. A genomic perspective on protein family. Science, 1997, 278 (5338): 631~637
24 Thompson JD, Higgins DG, Gibson TJ. Clustal W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res, 1994, 22 (22): 4673~4680
25 Ronquist F, Huelsenbeck JP. MrBayes 3: bayesian phylogenetic inference under mixed models. Bioinformatics, 2003, 19 (12): 1572~1574
26 Tamura K, Dudley J, Nei M, Kumar S. MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0.
27 Stothard P, Wishart DS. Circular genome visualization and exploration using CG View. Bioinformatics, 2005, 21: 537~539
28 Kouzuma A, Meng XY, Kimura N, Hashimoto K, Watanabe K. Disruption of the putative cell surface polysaccharide biosynthesis gene SO3177 in Shewanella oneidensis MR-1 enhances adhesion to electrodes and current generation in microbial fuel cells. Appl & Environ Microbiol, 2010, 76 (13): 4151~4157
29 Rollefson JB, Stephen CS, Tien M, Bond DR. Identification of an extracellular polysaccharide network essential for cytochrome anchoring and biofilm formation in Geobacter sulfurreducens. J Bacteriol, 2011, 193 (5): 1023~1033
30 Butler JE, Young ND, Lovley DR. Evolution of electron transfer out of the cell: comparative genomics of six Geobacter genomes. BMC Genomics, 2010, 11: 40
31 Zhao JS, Deng Y, Manno D, Hawari J. Shewanella spp. genomic evolution for a cold marine lifestyle and in-situ explosive biodegradation. PLoS ONE, 2010, 5 (2): e9109
32 Cho RJ, Campbell MJ, Winzeler EA, Steinmetz L, Conway A, Wodicka L, Wolfsberg TG, Gabrielian AE, Landsman D, Lockhart DJ, Davis RW. A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell, 1998, 2: 65~73
33 Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, Brown PO. Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell, 2000, 11: 4241~4257
34 Ito T, et al. Toward a protein-protein interaction map of the budding yeast: a comprehensive system to examine two-hybrid interactions in all possible combi-nations between the yeast proteins. Proc Natl Acad Sci USA, 2000: 1143~1147
35 Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams SL, Millar A, Taylor P, Bennett L, Boutilier K, Yang L, Wolting C, Donaldson I, Schandorff S, Shewnarane J, Vo M, Taggart J, Goudreault M, Muskat B, Alfarano C, Dewar D, Lin Z, Michalickova K, Willems AR, Sassi H, Nielsen PA, Rasmussen KJ, Andersen JR, Johansen LE, Hansen LH, Jespersen H, Podtelejnikov A, Nielsen E, Crawford J, Poulsen V, Sørensen BD, Matthiesen J, Hendrickson RC, Gleeson F, Pawson T, Moran MF, Durocher D, Mann M, Hogue CWV, Figeys D, Tyers M. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature, 2002, 415: 180~183
36 Ma X, Lee H, Wang L, Sun F. CGI: a new approach for prioritizing genes by combining gene expression and protein-protein interaction data. Syst Biol, 2007, 23 (2): 215~221
37 Lewin B. Genes VIII. Prentice Hall, 2004
38 Zhang L, Zhou W, Velculescu VE, Kern SE, Hruban RH, Hamilton SR, Vogelstein B, Kinzler KW. Gene expression profiles in normal and cancer cells. Science, 1997, 276 (5316): 1268~1272
39 Stern S, Dror T, Stolovicki E, Brenner N, Braun E. Genome-wide transcriptional plasticity underlies cellular adaptation to novel challenge. Mol Syst Biol, 2007, 3: 106
40 Karlebach G, Shamir R. Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol, 2008, 9: 770~780
41 Xie JM (谢建明). 基于表达数据和基因组信息分析基因调控的方法学研究: [Doctor Degree Thesis]. Nanjing: Southease University (南京: 东南大学), 2009
42 WuB (吴斌), Shen ZY (沈自尹). Analysis of microarray data. World Chin J Digestol (世界华人消化杂志), 2006, 14 (1): 68~74
43 D’haeseleer P, Liang S, Somogyi R. Gene expression data analysis and modelling. Pacific Symposium on Biocomputing, 1999
44 Tai SK, Wu G, Yuan S, Li KC. Genome-wide expression links the electron transfer pathway of Shewanella oneidensis to chemotaxis. BMC Genomics, 2010, 11: 319
45 Gerhold D, Lu M, Xu J, Austin C, Caskey CT, Rushmore T. Monitoring expression of genes involved in drug metabolism and toxicology using DNA microarrays. Physiol Genomics, 2001, 5: 161~170
46 Baldi P, Long AD. A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics, 2001, 17: 509~519
47 Friedman N, Linial M, Nachman I, Pe’er D. Using Bayesian networks to analyze expression data. J Comput Biol, 2000, 7: 601~620
48 Troyanskaya OG, Garber ME, Brown PO, Botstein D, Altman RB. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics, 2002, 18: 1454~1461
49 Efron B, Tibshirani R. Empirical bayes methods and false discovery rates for microarrays. Genet Epidemiol, 2002, 23: 70~86
50 Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA, 2001, 98: 5116~5121
51 Nevin KP, Kim BC, Glaven RH, Johnson JP, Woodard TL, Methé BA, DiDonato RJ Jr, Covalla SF, Franks AE, Liu A, Lovley DR. Anode biofilm transcriptomics reveals outer surface components essential for high density current production in Geobacter sulfurreducens fuel cells. PLoS ONE, 2009, 4 (5): e5628
52 Krushkal J, Yan B, DiDonato LN, Puljic M, Nevin KP, Woodard TL, Adkins RM, Methé BA, Lovley DR. Genome-wide expression profiling in Geobacter sulfurreducens: identification of Fur and RpoS transcription regulatory sites in a relGsu mutant. Funct Integr Genomics, 2007, 7: 229~255
53 Chourey K, Wei W, Wan XF, Thompson DK. Transcriptome analysis reveals response regulator SO2426-mediated gene expression in Shewanella oneidensis MR-1 under chromate challenge. BMC Genomics, 2008, 9: 395
54 Xu ZX (徐自祥), Sun X (孙啸). Research progress of cellular metabolic complex networks. China J Bioinformatics (生物信息学), 2009, 7 (2): 120~124
55 Covert MW, Schilling CH, Famili I, Edwards JS, Goryanin II, Selkov E, Palsson B?. Metabolic modeling of microbial strains in silico. Trends Biochem Sci, 2001, 26 (3): 179~186
56 Stephanopoulos GN. 代谢工程——原理与方法. Zhao XM (赵学明), Bai DM (白冬梅), et al. trans. Beijing: Chemical Industry Press (北京: 化学工业出版社), 2003
57 Patil KR, Rocha I, Förster J, Nielsen J. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics, 2005, 6: 308
58 Thiele I, Palsson B?. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protocols, 2010, 5: 93~121
59 Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LR, Hatzimanikatis V, Palsson B?. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol, 2007, 3: 121
60 Oh YK, Palsson B?, Park SM, Schilling CH, Mahadevan R. Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J Biol Chem, 2007, 282: 28791~28799
61 Sun J, Sayyar B, Butler JE, Pharkya P, Fahland TR, Famili I, Schilling CH, Lovley DR, Mahadevan R. Genome-scale constraint-based modeling of Geobacter metallireducens. BMC Syst Biol, 2009, 3: 15
62 Risso C, Sun J, Zhuang K, DeBoy R, Ismail W, Shrivastava S, Huot H, Kothari S, Daugherty S, Bui O, Schilling CH, Lovley DR, Methé BA. Genome-scale comparison and constraint-based metabolic reconstruction of the facultative anaerobic Fe(III)-reducer Rhodoferax ferrireducens. BMC Genomics, 2009, 10: 447
63 Feist AM, Palsson B?. Characterization of metabolism in the Fe(III)-reducing organism Geobacter sulfurreducens by constraint-based modeling. Appl & Environ Microbiol, 2006, 72: 1558~1568
64 Ma HW, Zeng AP. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics, 2003, 19 (2): 270~277
65 Cvijovic M, Herna RO, Agren R, Dahr N, Vongsangnak W, Nookaew I, Patil KR, Nielsen J. BioMet Toolbox: genome-wide analysis of metabolism. Nucleic Acids Res, 2010, 38: 144~149
66 Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res, 2010, 38: 355~360
67 Zhou TT, Yung KF, Chan CCK, Wang ZH, Zhu YP, He FC. MetaGen: a promising tool for modeling metabolic networks from KEGG. Progr Biochem & Biophys (生物化学与生物物理进展), 2010, 37 (1): 63~68
68 Goffin P, van de Bunt B, Giovane M, Leveau JHJ, Höppener-Ogawa S, Teusink B, Hugenholtz J. Understanding the physiology of Lactobacillus plantarum at zero growth. Mol Syst Biol, 2010, 6: 413
69 Pinchuk GE, Hill EA, Geydebrekht OV, De Ingeniis J, Zhang X, Osterman O, Scott JH, Reed SB, Romine MF, Konopka AE, Beliaev AS, Fredrickson JK, Reed JL. Constraint-based model of Shewanella oneidensis MR-1 metabolism: a tool for data analysis and hypothesis generation. PLoS Comput Biol, 2010, 6 (6): e1000822
70 Orth JD, Thiele I, Palsson B?. What is flux balance analysis? Nat Biotechnol, 2010, 28: 245~248
71 Bushell ME, Sequeira SI, Khannapho C, Zhao H, Chater KF, Butler MJ, Kierzek AM, Avignone-Rossaa CA. The use of genome scale metabolic flux variability analysis for process feed formulation based on an investigation of the effects of the zwf mutation on antibiotic production in Streptomyces coelicolor. Enzyme Microb Technol, 2006, 39: 1347~53
72 Burgard AP, Nikolaev EV, Schilling CH, Maranas CD. Flux coupling analysis of genome-scale metabolic network reconstructions. Genome Res, 2004, 14: 301~312
73 Delgado J, Liao JC. Inverse flux analysis for reduction of acetate excretion in Escherichia coli. Biotechnol Prog, 1997, 13: 361~367
74 Price ND, Papin JA, Schilling CH, Palsson B?. Genome-scale microbial in silico models: the constraints-based approach. Trend Biotechnol, 2003, 21 (4): 162~169
75 Becker SA, Feist AM, Mo ML, Hannum G, Palsson B?, Herrgard MJ. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nat Protocols, 2007, 2: 727~738
76 Park JM, Kim TY, Lee SY. Constraints-based genome-scale metabolic simulation for systems metabolic engineering. Biotechnol Adv, 2009, 27: 979~988
77 Burgard AP, Pharkya P, Maranas CD. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng, 2003, 84 (6): 647~657
78 Izallalen M, Mahadevan R, Burgard A, Postier B, Didonato R Jr, Sun J, Schilling CH, Lovley DR. Geobacter sulfurreducens strain engineered for increased rates of respiration. Metab Engin, 2008, 10: 267~275
79 Ranganathan S, Suthers PF, Maranas CD. OptForce: an optimization procedure for identifying all genetic manipulations leading to targeted overproductions. Comput Biol, 2010, 6 (4): e1000744
80 Pharkya P, Burgard AP, Maranas CD. OptStrain: a computational framework for redesign of microbial production systems. Genome Res, 2004, 14: 2367~2376
81 Palsson B?. Systems biology: properties of reconstructed networks. Cambridge University Press, 2006
82 Balaji S, Babu MM, Iyer LM, Luscombe NM, Aravind L. Comprehensive analysis of combinatorial regulation using the transcriptional regulatory network of yeast. J Mol Biol, 2006, 360: 213~227
83 Herrgard MJ, Lee BS, Portnoy V, Palsson B?. Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Res, 2006, 16: 627~635
84 Covert MW, Xiao N, Chen TJ, Karr JR. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics, 2008, 24 (18): 2044~2050
85 Lee JM, Gianchandani EP, Eddy JA, Papin JA. Dynamic analysis of integrated signaling, metabolic, and regulatory networks. PLoS Comput Biol, 2008, 4 (5): e1000086
86 Ma H, Rosa da Silva M, Sun J, Kumar B, Zeng AP. Reconstruction and structural analysis of metabolic and regulatory networks. In: Choi S ed. Introduction to Systems Biology. Humana Press, 2007

相似文献/References:

[1]李登兰,洪义国,许玫英,等.微生物燃料电池构造研究进展[J].应用与环境生物学报,2008,14(01):147.
 LI Denglan,et al..Progress in Construction of Microbial Fuel Cell[J].Chinese Journal of Applied & Environmental Biology,2008,14(06):147.
[2]张锦涛,倪晋仁,周顺桂.基于铁还原菌的微生物燃料电池研究进展[J].应用与环境生物学报,2008,14(02):290.
 ZHANG Jintao,et al..Progress in Research of Microbial Fuel Cells Based on Fe(III)-reducing Bacteria[J].Chinese Journal of Applied & Environmental Biology,2008,14(06):290.
[3]刘敏,邵军,周奔,等.微生物产电呼吸最新研究进展[J].应用与环境生物学报,2010,16(03):445.[doi:10.3724/SP.J.1145.2010.00445]
 LIU Min,SHAO Jun,ZHOU Ben,et al.Progress in Research of Microbial Electricigenic Respiration[J].Chinese Journal of Applied & Environmental Biology,2010,16(06):445.[doi:10.3724/SP.J.1145.2010.00445]
[4]张雅舒,张礼霞,李大平.微生物燃料电池还原二氧化铅及产电研究[J].应用与环境生物学报,2012,18(05):780.[doi:10.3724/SP.J.1145.2012.00780]
 ZHANG Yashu,ZHANG Lixia,LI Daping.Simultaneous Reduction of Lead Dioxide and Improvement of Bioelectricity Production in Microbial Fuel Cell[J].Chinese Journal of Applied & Environmental Biology,2012,18(06):780.[doi:10.3724/SP.J.1145.2012.00780]
[5]崔旸,苏文涛,高平,等.还原性硫化物微生物燃料电池偶联偶氮染料降解[J].应用与环境生物学报,2012,18(06):978.[doi:10.3724/SP.J.1145.2012.00978]
 CUI Yang,SU Wentao,GAO Ping,et al.Microbial Fuel Cell Coupled Bio-oxidation of Reducing Sulfide with Degradation of Azo Dyes[J].Chinese Journal of Applied & Environmental Biology,2012,18(06):978.[doi:10.3724/SP.J.1145.2012.00978]
[6]刘柯,李大平,王娟.尿液微生物燃料电池研究[J].应用与环境生物学报,2015,21(01):36.[doi:10.3724/SP.J.1145.2014.03030]
 LIU Ke,LI Daping,WANG Juan.Study on urine microbial fuel cell[J].Chinese Journal of Applied & Environmental Biology,2015,21(06):36.[doi:10.3724/SP.J.1145.2014.03030]
[7]华涛,李胜男,周启星,等.生物电化学系统3种典型构型及其应用研究进展[J].应用与环境生物学报,2018,24(03):663.[doi:10.19675/j.cnki.1006-687x.2017.08046]
 HUA Tao,LI Shengnan,ZHOU Qixing,et al.Recent advances in three typical configurations and applications of bioelectrochemical systems[J].Chinese Journal of Applied & Environmental Biology,2018,24(06):663.[doi:10.19675/j.cnki.1006-687x.2017.08046]
[8]蒋沁芮,李泽华,杨暖,等.三维电极微生物燃料电池处理生活污水同步产电性能[J].应用与环境生物学报,2018,24(04):873.[doi:10.19675/j.cnki.1006-687x.2017.11011]
 JIANG Qinrui,LI Zehua,et al.Microbial fuel cell with three-dimensional electrodes for domestic wastewater treatment and electricity generation[J].Chinese Journal of Applied & Environmental Biology,2018,24(06):873.[doi:10.19675/j.cnki.1006-687x.2017.11011]
[9]倪宏宇航,弓雨欣,李祥锴.微生物燃料电池阳极电极的新型材料与修饰方法[J].应用与环境生物学报,2019,25(04):999.[doi:10.19675/j.cnki.1006-687x.2018.12022]
 NI Hongyuhang,GONG Yuxin & LI Xiangkai**.Application of recent modification methods and materials in microbial fuel cell anode electrodes[J].Chinese Journal of Applied & Environmental Biology,2019,25(06):999.[doi:10.19675/j.cnki.1006-687x.2018.12022]

备注/Memo

备注/Memo:
国家自然科学基金项目(No. 51172043)和东南大学生物电子学国家重点实验室开放研究基金资助 Supported by National Natural Science Foundation of China (No. 51172043) and the Open Research Fund of State Key Laboratory of Bioelectronics, Southeast University
更新日期/Last Update: 2012-12-28