Please wait a minute...

中国生物工程杂志

CHINA BIOTECHNOLOGY
中国生物工程杂志
中国生物工程杂志  2022, Vol. 42 Issue (1/2): 128-138    DOI: 10.13523/j.cb.2108068
综述     
基于热力学原理约束的代谢网络模型研究进展及其应用*
虞思倩1,夏建业1,**(),庄英萍1,2
1 华东理工大学 生物反应器工程国家重点实验室 上海 200237
2 华东理工大学青岛创新研究院 青岛 266102
Research Progress and Application of Metabolic Network Model Constrained by Thermodynamic Principles
YU Si-qian1,XIA Jian-ye1,**(),ZHUANG Ying-ping1,2
1 State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
2 Qingdao Innovation Research Institute, East China University of Science and Technology, Qingdao 266102, China
 全文: PDF(2571 KB)   HTML
摘要：

高通量组学技术为人们研究生命系统组件提供了细节数据,通过对基因组、转录组、蛋白质组及代谢组等不同生命层级间相互作用的研究,推动了生化反应网络的重建——基因组规模代谢网络模型(genome scale of metabolic network model, GSMM)。GSMM作为系统生物学领域中研究生命系统的基本手段,表现出与传统还原论相反的整体化思想,它允许将细胞复杂的生命过程作为一个完整的系统来研究。通量平衡分析(flux balance analysis, FBA)作为GSMM的主流方法,因其自身依赖的约束有限,通常难以得到唯一的最优解。而热力学与生物代谢密不可分,目前除将多组学数据作为附加约束引入GSMM模型外,添加热力学约束也已成为有效减小解空间的手段。首先对热力学约束引入GSMM模型的方法及各种方法的优势与不足进行了综述,然后对获得相关热力学参数的方法和工具进行了梳理汇总。最后,介绍了整合多组学及热力学综合约束的GSMM模型以及基于热力学原理约束的模型在实际中的应用,并对如何应用热力学约束提高GSMM模型模拟准确性进行了展望。
关键词： 基因组规模代谢网络模型;  热力学;  约束分析;  吉布斯自由能    
Abstract:

High-throughput omics technology provided detailed data for studying life system components. Through the interaction of components among the genome, transcriptome, proteome, and metabolome, it promoted the construction of the genome scale of metabolic network model (GSMM). GSMM, as a commonly used tool in systems biology, allows the complex life process of cells to be studied as a whole system, so it shows more holistic thinking contrary to traditional reductionism. Flux balance analysis (FBA), as the mainstream method of GSMM, is usually difficult to obtain the unique optimal solution due to enough constraints. Thermodynamics is closely related to biological metabolism, so in addition to introducing multiple omics data into the GSMM as additional constraints, adding thermodynamic constraints has also become an effective way to reduce the solution space further. This paper first reviews the method of introducing thermodynamic constraints into the GSMM and the advantages and disadvantages of the methods themselves, and then summarizes the methods and tools for obtaining relevant thermodynamic parameters. Finally, this review introduces the metabolic network model integrating multi-omics and thermodynamic constraints and discusses the practical application of the model based on thermodynamic principle constraints, and puts forward a prospect on how to apply thermodynamic constraints to improve the accuracy of the GSMM.
Key words: Genome-scale metabolic network model(GSMM)    Thermodynamics    Constraint analysis    Gibbs free-energy
收稿日期: 2021-08-29 出版日期: 2022-03-03
ZTFLH:  Q819  
基金资助: * 国家重点研发计划(2020YFA0908300);国家自然科学基金面上项目(21776082);青岛市生物制造行业科学研究智库联合基金(QDSWZK202004)
通讯作者: 夏建业     E-mail: jyxia@ecust.edu.cn
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
虞思倩
夏建业
庄英萍

引用本文:

虞思倩,夏建业,庄英萍. 基于热力学原理约束的代谢网络模型研究进展及其应用*[J]. 中国生物工程杂志, 2022, 42(1/2): 128-138.

YU Si-qian,XIA Jian-ye,ZHUANG Ying-ping. Research Progress and Application of Metabolic Network Model Constrained by Thermodynamic Principles. China Biotechnology, 2022, 42(1/2): 128-138.

链接本文:

https://manu60.magtech.com.cn/biotech/CN/10.13523/j.cb.2108068    或    https://manu60.magtech.com.cn/biotech/CN/Y2022/V42/I1/2/128

方法 相同点 不同点 实现方法的工具 求解类型
NET 1.需要热力学参数△rG0和△fG0的输入;
2.需要代谢组数据的输入;
3.参数通过热力学第二定律进行耦合:△rG=△r Go+RT ln Q;
4.目的都是为了去除热力学上不可行的反应或途径;
5.均适于基因组规模的分析		 1.需要预先确定通量的方向;
2.可对输入的代谢组学数据进行热力学一致性质量检验		 Matlab:anNET 非线性规划
(NLP)
TFA 1.需对可逆反应进行拆分,得到正、反两个反应;
2.引入了离散变量zi对反应方向进行分配		 Matlab:matTFA
Python:pyTFA		 混合整数线性规划
(MILP)
表1  基于热力学参数的约束分析方法NET、TFA的对比信息
图1  获取热力学参数的数据库及工具
图2  热力学相关参数和分析方法的发展历程
[1] Edwards J S, Palsson B O. Systems properties of the Haemophilus influenzae Rd metabolic genotype. Journal of Biological Chemistry, 1999, 274(25):17410-17416.
doi: 10.1074/jbc.274.25.17410 pmid: 10364169
[2] Lieven C, Beber M E, Olivier B G, et al. Memote: a community driven effort towards a standardized genome-scale metabolic model test suite. bioRxiv, 2018.DOI: 10.1101/350991.
doi: 10.1101/350991
[3] Orth J D, Thiele I, Palsson B O. What is flux balance analysis? Nature Biotechnology, 2010, 28(3):245-248.
doi: 10.1038/nbt.1614
[4] Adadi R, Volkmer B, Milo R, et al. Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters. PLoS Computational Biology, 2012, 8(7):e1002575.
doi: 10.1371/journal.pcbi.1002575
[5] Yizhak K, Benyamini T, Liebermeister W, et al. Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics, 2010, 26(12):i255-i260.
doi: 10.1093/bioinformatics/btq183
[6] Shen F Z, Sun R L, Yao J, et al. OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling. PLoS Computational Biology, 2019, 15(3):e1006835.
doi: 10.1371/journal.pcbi.1006835
[7] Zur H, Ruppin E, Shlomi T. iMAT: an integrative metabolic analysis tool. Bioinformatics, 2010, 26(24):3140-3142.
doi: 10.1093/bioinformatics/btq602
[8] Chandrasekaran S, Price N D. Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. PNAS, 2010, 107(41):17845-17850.
doi: 10.1073/pnas.1005139107 pmid: 20876091
[9] Zeng H, Yang A D. Bridging substrate intake kinetics and bacterial growth phenotypes with flux balance analysis incorporating proteome allocation. Scientific Reports, 2020, 10:4283.
doi: 10.1038/s41598-020-61174-0
[10] Yang L, Yurkovich J T, King Z A, et al. Modeling the multi-scale mechanisms of macromolecular resource allocation. Current Opinion in Microbiology, 2018, 45:8-15.
doi: 10.1016/j.mib.2018.01.002
[11] Sánchez B J, Zhang C, Nilsson A, et al. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Molecular Systems Biology, 2017, 13(8):935.
doi: 10.15252/msb.20167411
[12] Soh K C, Hatzimanikatis V. Constraining the flux space using thermodynamics and integration of metabolomics data. Methods in Molecular Biology (Clifton, N J), 2014, 1191:49-63.
[13] 杨雪, 张培基, 毛志涛, 等. 多约束代谢网络模型的研究进展. 生物工程学报, 2021, 38(4):1-14.
Yang X, Zhang P J, Mao Z T, et al. Research progress of multi-constrained metabolic model. Chin J Biotech, 2021, 38(4):1-14.
[14] Lewis N E, Nagarajan H, Palsson B O. Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nature Reviews Microbiology, 2012, 10(4):291-305.
doi: 10.1038/nrmicro2737
[15] Schellenberger J, Lewis N E, Palsson B O. Elimination of thermodynamically infeasible loops in steady-state metabolic models. Biophysical Journal, 2011, 100(3):544-553.
doi: S0006-3495(10)05225-2 pmid: 21281568
[16] Hoppe A, Hoffmann S, Holzhütter H G. Including metabolite concentrations into flux balance analysis: thermodynamic realizability as a constraint on flux distributions in metabolic networks. BMC Systems Biology, 2007, 1(1):1-12.
doi: 10.1186/1752-0509-1-1
[17] Holzhütter H G. The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. European Journal of Biochemistry, 2004, 271(14):2905-2922.
doi: 10.1111/(ISSN)1432-1033
[18] Qian H, Beard D A, Liang S D. Stoichiometric network theory for nonequilibrium biochemical systems. European Journal of Biochemistry, 2003, 270(3):415-421.
doi: 10.1046/j.1432-1033.2003.03357.x
[19] Beard D A, Liang S D, Qian H. Energy balance for analysis of complex metabolic networks. Biophysical Journal, 2002, 83(1):79-86.
doi: 10.1016/S0006-3495(02)75150-3
[20] Zamboni N, Kümmel A, Heinemann M. anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data. BMC Bioinformatics, 2008, 9:199.
doi: 10.1186/1471-2105-9-199 pmid: 18416814
[21] Kümmel A, Panke S, Heinemann M. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Molecular Systems Biology, 2006, 2:2006.0034.
[22] Henry C S, Broadbelt L J, Hatzimanikatis V. Thermodynamics-based metabolic flux analysis. Biophysical Journal, 2007, 92(5):1792-1805.
doi: 10.1529/biophysj.106.093138
[23] Zhu Y, Song J N, Xu Z X, et al. Development of thermodynamic optimum searching (TOS) to improve the prediction accuracy of flux balance analysis. Biotechnology and Bioengineering, 2013, 110(3):914-923.
doi: 10.1002/bit.24739
[24] Noor E, Bar-Even A, Flamholz A, et al. Pathway thermodynamics highlights kinetic obstacles in central metabolism. PLoS Computational Biology, 2014, 10(2):e1003483.
doi: 10.1371/journal.pcbi.1003483
[25] Noor E. Removing both internal and unrealistic energy-generating cycles in flux balance analysis. arXiv, 2018.[2022-01-17]. https://mail.cstnet.cn/coremail/XT5/index.jsp?sid=JAIiUSKKLCSMlPdlmApNrMxgpimIcUqi#mail.read%7C%7B%22fid%22%3A1%2C%22mid%22%3A%2210%3A1tbiCgkGE1z4kSwbWQAAsN%22%2C%22mboxa%22%3A%22%22%2C%22start%22%3A7%7D.
[26] Edwards J S, Palsson B O. The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities. PNAS, 2000, 97(10):5528-5533.
pmid: 10805808
[27] Price N D, Famili I, Beard D A, et al. Extreme pathways and kirchhoff's second law. Biophysical Journal, 2002, 83(5):2879-2882.
doi: 10.1016/S0006-3495(02)75297-1
[28] Zomorrodi A R, Suthers P F, Ranganathan S, et al. Mathematical optimization applications in metabolic networks. Metabolic Engineering, 2012, 14(6):672-686.
doi: 10.1016/j.ymben.2012.09.005 pmid: 23026121
[29] Gogolewski K, Kostecki M, Gambin A. Low entropy sub-networks prevent the integration of metabolomic and transcriptomic data. Entropy, 2020, 22(11):1238.
doi: 10.3390/e22111238
[30] Park J O, Rubin S A, Xu Y F, et al. Metabolite concentrations, fluxes and free energies imply efficient enzyme usage. Nature Chemical Biology, 2016, 12(7):482-489.
doi: 10.1038/nchembio.2077
[31] Henry C S, Jankowski M D, Broadbelt L J, et al. Genome-scale thermodynamic analysis of Escherichia coli metabolism. Biophysical Journal, 2006, 90(4):1453-1461.
doi: 10.1529/biophysj.105.071720
[32] Ataman M, Hatzimanikatis V. Heading in the right direction: thermodynamics-based network analysis and pathway engineering. Current Opinion in Biotechnology, 2015, 36:176-182.
doi: 10.1016/j.copbio.2015.08.021 pmid: 26360871
[33] Mavrovouniotis M L. Estimation of standard Gibbs energy changes of biotransformations. Journal of Biological Chemistry, 1991, 266(22):14440-14445.
pmid: 1860851
[34] Salvy P, Fengos G, Ataman M, et al. pyTFA and matTFA: a Python package and a matlab toolbox for thermodynamics-based flux analysis. Bioinformatics, 2019, 35(1):167-169.
[35] Molenaar D, van Berlo R, de Ridder D, et al. Shifts in growth strategies reflect tradeoffs in cellular economics. Molecular Systems Biology, 2009, 5:323.
doi: 10.1038/msb.2009.82 pmid: 19888218
[36] Hsu P P, Sabatini D M. Cancer cell metabolism: Warburg and beyond. Cell, 2008, 134(5):703-707.
doi: 10.1016/j.cell.2008.08.021
[37] Varma A, Palsson B O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Applied and Environmental Microbiology, 1994, 60(10):3724-3731.
doi: 10.1128/aem.60.10.3724-3731.1994 pmid: 7986045
[38] Hermsen R, Okano H, You C H, et al. A growth-rate composition formula for the growth of E. coli on co-utilized carbon substrates. Molecular Systems Biology, 2015, 11(4):801.
doi: 10.15252/msb.20145537 pmid: 25862745
[39] Narang A, Pilyugin S S. Bacterial gene regulation in diauxic and non-diauxic growth. Journal of Theoretical Biology, 2007, 244(2):326-348.
pmid: 16989865
[40] Wang X, Xia K, Yang X J, et al. Growth strategy of microbes on mixed carbon sources. Nature Communications, 2019, 10:1279.
doi: 10.1038/s41467-019-09261-3 pmid: 30894528
[41] Basan M, Hui S, Okano H, et al. Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature, 2015, 528(7580):99-104.
doi: 10.1038/nature15765
[42] Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics, 2009, 10(1):57-63.
doi: 10.1038/nrg2484
[43] Kim M, Rai N, Zorraquino V, et al. Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli. Nature Communications, 2016, 7:13090.
doi: 10.1038/ncomms13090
[44] Ebrahim A, Brunk E, Tan J, et al. Multi-omic data integration enables discovery of hidden biological regularities. Nature Communications, 2016, 7:13091.
doi: 10.1038/ncomms13091 pmid: 27782110
[45] Pandey V, Hadadi N, Hatzimanikatis V. Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models. PLoS Computational Biology, 2019, 15(5):e1007036.
doi: 10.1371/journal.pcbi.1007036
[46] Salvy P, Hatzimanikatis V. The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models. Nature Communications, 2020, 11:30.
doi: 10.1038/s41467-019-13818-7
[47] Lerman J A, Hyduke D R, Latif H, et al. In silico method for modelling metabolism and gene product expression at genome scale. Nature Communications, 2012, 3:929.
doi: 10.1038/ncomms1928
[48] Goldberg R N, Tewari Y B, Bhat T N. Thermodynamics of enzyme-catalyzed reactions:a database for quantitative biochemistry. Bioinformatics, 2004, 20(16):2874-2877.
pmid: 15145806
[49] Simon R H M. Estimation of critical properties of organic compounds by the method of group contributions. American Institute of Chemical Engineers, 1956, 2(3):12S.
[50] Gani R. Group contribution-based property estimation methods: advances and perspectives. Current Opinion in Chemical Engineering, 2019, 23:184-196.
doi: 10.1016/j.coche.2019.04.007
[51] Alberty R A. Calculation of standard transformed formation properties of biochemical reactants and standard apparent reduction potentials of half reactions. Archives of Biochemistry and Biophysics, 1998, 358(1):25-39.
pmid: 9750161
[52] Jankowski M D, Henry C S, Broadbelt L J, et al. Group contribution method for thermodynamic analysis of complex metabolic networks. Biophysical Journal, 2008, 95(3):1487-1499.
doi: 10.1529/biophysj.107.124784 pmid: 18645197
[53] Noor E, Bar-Even A, Flamholz A, et al. An integrated open framework for thermodynamics of reactions that combines accuracy and coverage. Bioinformatics, 2012, 28(15):2037-2044.
doi: 10.1093/bioinformatics/bts317
[54] Noor E, Haraldsdóttir H S, Milo R, et al. Consistent estimation of Gibbs energy using component contributions. PLoS Computational Biology, 2013, 9(7):e1003098.
doi: 10.1371/journal.pcbi.1003098
[55] Du B, Zhang Z, Grubner S, et al. Temperature-dependent estimation of Gibbs energies using an updated group-contribution method. Biophysical Journal, 2018, 114(11):2691-2702.
doi: 10.1016/j.bpj.2018.04.030
[56] Yasemi M, Jolicoeur M. Modelling cell metabolism: a review on constraint-based steady-state and kinetic approaches. Processes, 2021, 9(2):322.
doi: 10.3390/pr9020322
[57] Rother K, Hoffmann S, Bulik S, et al. IGERS: inferring Gibbs energy changes of biochemical reactions from reaction similarities. Biophysical Journal, 2010, 98(11):2478-2486.
doi: 10.1016/j.bpj.2010.02.052
[58] Finley S D, Broadbelt L J, Hatzimanikatis V. Thermodynamic analysis of biodegradation pathways. Biotechnology and Bioengineering, 2009, 103(3):532-541.
doi: 10.1002/bit.v103:3
[59] Hadadi N, Ataman M, Hatzimanikatis V, et al. Molecular thermodynamics of metabolism: quantum thermochemical calculations for key metabolites. Physical Chemistry Chemical Physics, 2015, 17(16):10438-10453.
doi: 10.1039/c4cp05825a pmid: 25799954
[60] Jinich A, Rappoport D, Dunn I, et al. Quantum chemical approach to estimating the thermodynamics of metabolic reactions. Scientific Reports, 2014, 4:7022.
doi: 10.1038/srep07022
[61] Flamholz A, Noor E, Bar-Even A, et al. eQuilibrator:the biochemical thermodynamics calculator. Nucleic Acids Research, 2012, 40(D1):D770-D775.
doi: 10.1093/nar/gkr874
[62] Beber M E, Gollub M G, Mozaffari D, et al. eQuilibrator 3.0: a platform for the estimation of thermodynamic constants. arXiv, 2021.[2022-01-17]. https://www.researchgate.net/publication/349704198_eQuilibrator_30__a_platform_for_the_estimation_of_thermodynamic_constants.
[63] Lieven C, Beber M E, Olivier B G, et al. MEMOTE for standardized genome-scale metabolic model testing. Nature Biotechnology, 2020, 38(3):272-276.
doi: 10.1038/s41587-020-0446-y
[64] Lloyd C J, Ebrahim A, Yang L, et al. COBRAme: a computational framework for genome-scale models of metabolism and gene expression. PLoS Computational Biology, 2018, 14(7):e1006302.
doi: 10.1371/journal.pcbi.1006302
[65] Ebrahim A, Lerman J A, Palsson B O, et al. COBRApy: COnstraints-based reconstruction and analysis for Python. BMC Systems Biology, 2013, 7:74.
doi: 10.1186/1752-0509-7-74 pmid: 23927696
[66] Salvy P, Hatzimanikatis V. Emergence of diauxie as an optimal growth strategy under resource allocation constraints in cellular metabolism. bioRxiv, 2020.DOI: 10.1101/2020.07.15.204420.
doi: 10.1101/2020.07.15.204420
[67] Qi Y M, Wang H, Chen X D, et al. Altered metabolic strategies: elaborate mechanisms adopted by Oenococcus oeni in response to acid stress. Journal of Agricultural and Food Chemistry, 2021, 69(9):2906-2918.
doi: 10.1021/acs.jafc.0c07599
[68] Lewis J E, Costantini F, Mims J, et al. Genome-scale modeling of NADPH-driven β-lapachone sensitization in head and neck squamous cell carcinoma. Antioxidants & Redox Signaling, 2018, 29(10):937-952.
[69] Chiappino-Pepe A, Tymoshenko S, Ataman M, et al. Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks. PLoS Computational Biology, 2017, 13(3):e1005397.
doi: 10.1371/journal.pcbi.1005397
[70] Tokic M, Hatzimanikatis V, Miskovic L. Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies. Biotechnology for Biofuels, 2020, 13:33.
doi: 10.1186/s13068-020-1665-7
[71] Andreozzi S, Miskovic L, Hatzimanikatis V. iSCHRUNK - in silico approach to characterization and reduction of uncertainty in the kinetic models of genome-scale metabolic networks. Metabolic Engineering, 2016, 33:158-168.
doi: S1096-7176(15)00129-9 pmid: 26474788
[72] Andreozzi S, Chakrabarti A, Soh K C, et al. Identification of metabolic engineering targets for the enhancement of 1, 4-butanediol production in recombinant E. coli using large-scale kinetic models. Metabolic Engineering, 2016, 35:148-159.
doi: S1096-7176(16)00021-5 pmid: 26855240
[73] Suthers P F, Foster C J, Sarkar D, et al. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Metabolic Engineering, 2021, 63:13-33.
doi: 10.1016/j.ymben.2020.11.013
[74] Jinich A, Sanchez-Lengeling B, Ren H N, et al. A mixed quantum chemistry/machine learning approach for the fast and accurate prediction of biochemical redox potentials and its large-scale application to 315 000 redox reactions. ACS Central Science, 2019, 5(7):1199-1210.
doi: 10.1021/acscentsci.9b00297
[75] Li G, Hu Y T, Zrimec J, et al. Bayesian genome scale modelling identifies thermal determinants of yeast metabolism. Nature Communications, 2021, 12:190.
doi: 10.1038/s41467-020-20338-2
[76] Rychel K, Sastry A V, Palsson B O. Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome. Nature Communications, 2020, 11:6338.
doi: 10.1038/s41467-020-20153-9
[77] Yang J H, Wright S N, Hamblin M, et al. A white-box machine learning approach for revealing antibiotic mechanisms of action. Cell, 2019, 177(6):1649-1661.e9.
doi: 10.1016/j.cell.2019.04.016
[78] Min S, Lee B, Yoon S. Deep learning in bioinformatics. Briefings in Bioinformatics, 2017, 18(5):851-869.
[79] Leung M K K, Delong A, Alipanahi B, et al. Machine learning in genomic medicine: a review of computational problems and data sets. Proceedings of the IEEE, 2016, 104(1):176-197.
[80] Angermueller C, P?rnamaa T, Parts L, et al. Deep learning for computational biology. Molecular Systems Biology, 2016, 12(7):878.
doi: 10.15252/msb.20156651 pmid: 27474269
[81] Schinn S M, Morrison C, Wei W, et al. A genome-scale metabolic network model and machine learning predict amino acid concentrations in Chinese Hamster Ovary cell cultures. Biotechnology and Bioengineering, 2021, 118(5):2118-2123.
doi: 10.1002/bit.v118.5
[1] 张虎,赵亮,陈义,高保燕,胡强,张成武. 微藻异养高密度培养研究进展与发展趋势*[J]. 中国生物工程杂志, 2022, 42(3): 110-123.
[2] 张毅,王陈,石晶晶,陈学军,张瑞华,靳倩,石童,李丽琴. 稳定表达人源GABAAR-CHO细胞株的建立[J]. 中国生物工程杂志, 2022, 42(3): 38-46.
[3] 廖天赐,郑婷,沈林園,赵叶,牛丽莉,张顺华,朱砺. tsRNAs的作用机制及其在相关疾病中的潜在应用*[J]. 中国生物工程杂志, 2022, 42(3): 82-90.
[4] 董慧霞,侯占铭. 尖孢镰刀菌亚麻专化型Folprp4基因参与调控菌丝生长和分生孢子发生[J]. 中国生物工程杂志, 2022, 42(3): 13-26.
[5] 蒲飞洋,李易聪,王慧慧,冯茜莉,李倬,马忠仁,周建华. 同义密码子使用模式对蛋白产物表达及构象形成的影响*[J]. 中国生物工程杂志, 2022, 42(3): 91-98.
[6] 许慧,丁健,史仲平. 地衣芽孢杆菌BF-002高产芽孢的氮源流加工艺研究*[J]. 中国生物工程杂志, 2022, 42(3): 47-54.
[7] 林健芬,罗顺. CHO细胞基因组中稳定hot spot位点研究进展[J]. 中国生物工程杂志, 2022, 42(3): 72-81.
[8] 吴娟,徐宁,张胜花,张小丹,刘缘圆,葛永怡. GST pull-down筛选冠突曲霉MAT的互作蛋白*[J]. 中国生物工程杂志, 2022, 42(3): 1-12.
[9] 冯晓莹,孟倩,陈巍,余磊,黄卫人. 类器官芯片在医学研究中的应用进展*[J]. 中国生物工程杂志, 2022, 42(1/2): 112-118.
[10] 赵炳杰,郭岩彬. 食用菌多糖的提取纯化及生物活性研究进展*[J]. 中国生物工程杂志, 2022, 42(1/2): 146-159.
[11] 吴东,肖枫,袁敏,陈禹保,张宏翔. 2011~2020年我国生物医学领域科技成果转化分析[J]. 中国生物工程杂志, 2022, 42(1/2): 191-201.
[12] 陈涛,刘志华,李霞,谢泽雄. 抑制剂耐受性酵母底盘细胞的设计与构建*[J]. 中国生物工程杂志, 2022, 42(1/2): 1-13.
[13] 李金花,白雨凡,马春兰,季秀玲,魏云林. 噬菌体温度适应性的研究进展*[J]. 中国生物工程杂志, 2022, 42(1/2): 139-145.
[14] 章真,刘晓军,陈夏,姚丽萍,张荣庆. 微藻生物技术在碳中和的应用与展望*[J]. 中国生物工程杂志, 2022, 42(1/2): 160-173.
[15] 戢传富,王璐,苟敏,宋文枫,夏子渊,汤岳琴. 黄原胶生物合成及分子调控*[J]. 中国生物工程杂志, 2022, 42(1/2): 46-57.
主管：中国科学院 　主办：中国科学院文献情报中心 　中国生物技术发展中心 　中国生物工程学会