Повышение производительности цикла проектирование-создание-тестирование-обучение систем (DBTL) в синтетической биологии растений (обзор)
Автор: Prasad S.S., Das U.
Журнал: Сельскохозяйственная биология @agrobiology
Рубрика: Обзоры, проблемы
Статья в выпуске: 5 т.59, 2024 года.
Бесплатный доступ
Синтетическая биология растений - это молодая научная дисциплина, которая объединяет принципы инженерии с биологией с целью разработки уникальных систем на основе растений для различных применений, от производства биотоплива до улучшения сельскохозяйственных культур. Эта технология может революционно изменить традиционное сельское хозяйство, содействовать устойчивому развитию и решению глобальных проблем продовольственой безопасности, изменения климата и возобновляемых источников энергии. Цикл проектирование-создание-тестирование-обучение (Design-Build-Test-Learn, DBTL) обеспечивает основу для процесса планирования, разработки, оценки и совершенствования синтетических биологических систем. Он позволяет исследователям итеративно настраивать производительность биологических цепей, что делает этот цикл критически важным инструментом для построения сложных биологических систем с предсказуемым и надежным поведением. Однако на каждом этапе этого цикла могут проявиться узкие места из-за неэффективного проектирования, ограниченного запаса генетических компонентов, технических проблем при разработке и управлении биологическими системами и трудностей в корректном мониторинге производительности системы. Для преодоления узких мест в цикле DBTL можно использовать различные стратегии: совершенствование вычислительных технологий для эффективного проектирования, расширение набора генетических компонентов, повышение точности и масштабируемости приемов редактирования генома и внедрение методов высокопроизводительного скрининга для точного измерения производительности системы. В этой обзорной статье мы обсудим последние достижения в улучшении производительности цикла DBTL для преодоления его узких мест.
Цикл dbtl, мультиомика, биоортагональные реакции, микрофлюидика, изотопные трассеры, loica, crispr
Короткий адрес: https://sciup.org/142243772
IDR: 142243772 | DOI: 10.15389/agrobiology.2024.5.831rus
Список литературы Повышение производительности цикла проектирование-создание-тестирование-обучение систем (DBTL) в синтетической биологии растений (обзор)
- Sala A., Woodruff D.R., Meinzer F.C. Carbon dynamics in trees: feast or famine? Tree Physiology, 2012, 32(6): 764-75 (doi: 10.1093/TREEPHYS/TPR143).
- Liang Z., Chen K., Li T., Zhang Y., Wang Y., Zhao Q., Liu J., Huawei Z., Liu C., Ran Y., Gao C. Efficient DNA-free genome editing of bread wheat using CRISPR/Cas9 ribonucleoprotein complexes. Nature Communications, 2017, 8(1): 14261 (doi: 10.1038/ncomms14261).
- Dlugosz E.M., Lenaghan S.C., Stewart C.N. Jr. A robotic platform for high-throughput protoplast isolation and transformation. Journal of Visualized Experiments, 2016, NA(115): 54300-NA (doi: 10.3791/54300).
- Mortimer J.C. Plant synthetic biology could drive a revolution in biofuels and medicine. Experimental Biology and Medicine, 2019, 244(4): 323-331 (doi: 10.1177/1535370218793890).
- Liu W., Stewart C.N. Plant synthetic biology. Trends in Plant Science, 2015, 20(5): 309-317 (doi: 10.1016/j.tplants.2015.02.004).
- Llorente B., Williams T.C., Goold H.D. The multiplanetary future of plant synthetic biology. Genes, 2018, 9(7): 348 (doi: 10.3390/genes9070348).
- Tothill I.E. Biosensors developments and potential applications in the agricultural diagnosis sector. Computers and Electronics in Agriculture, 2001, 30(1-3): 205-218 (doi: 10.1016/S0168-1699(00)00165-4).
- Boyle P.M., Burrill D.R., Inniss M.C., Agapakis C.M., Deardon A., DeWerd J.G., Gedeon M.A., Quinn J.Y., Paull M.L., Raman A.M., Theilmann, M.R., Wang L., Winn J.C., Medvedik O., Schellenberg K., Haynes K.A., Viel A., Brenner T.J., Church G.M., Shah J.V., Silver P.A. A BioBrick compatible strategy for genetic modification of plants. Journal of Biological Engineering, 2012, 6(1): 8 (doi: 10.1186/1754-1611-6-8).
- Sainsbury F., Lomonossoff G.P. Transient expressions of synthetic biology in plants. Current Opinion in Plant Biology, 2014, 19: 1-7 (doi: 10.1016/j.pbi.2014.02.003).
- Yang Y., Chaffin T.A., Ahkami A.H., Blumwald E., Stewart C.N. Plant synthetic biology innovations for biofuels and bioproducts. Trends in Biotechnology, 2022, 40(12): 1454-1468 (doi: 10.1016/j.tibtech.2022.09.007).
- Chao R., Mishra S., Si T., Zhao H. Engineering biological systems using automated biofoundries. Metabolic Engineering, 2017, 42: 98-108 (doi: 10.1016/J.YMBEN.2017.06.003).
- Kim B.-C.C., Moon C., Jeon B.S., Angenent L.T., Choi Y., Nam K. Shaping a reactor microbiome generating stable n-caproate productivity through Design-Build-Test-Learn approach. Chemical Engineering Journal, 2021, 425: 131587 (doi: 10.1016/j.cej.2021.131587).
- Pham H.L., Ho C.L., Wong A., Lee Y.S., Chang M.W. Applying the design-build-test paradigm in microbiome engineering. Current Opinion in Biotechnology, 2017, 48: 85-93 (doi: 10.1016/j.copbio.2017.03.021).
- Pouvreau B., Vanhercke T., Singh S. From plant metabolic engineering to plant synthetic biology: The evolution of the design/build/test/learn cycle. Plant Science, 2018, 273: 3-12 (doi: 10.1016/j.plantsci.2018.03.035).
- Liao X., Ma H., Tang Y.J. Artificial intelligence: a solution to involution of design–build–test–learn cycle. Current Opinion in Biotechnology, 2022, 75: 102712 (doi: 10.1016/j.copbio.2022.102712).
- Carbonell P., Jervis A.J., Robinson C.J., Yan C., Dunstan M., Swainston N., Vinaixa M., Hollywood K.A., Currin A., Rattray N.J.W., Taylor S., Spiess R., Sung R., Williams A.R., Fellows D., Stanford N.J., Mulherin P., Le Feuvre R., Barran P., Goodacre R., Turner N.J., Goble C., Chen G.G., Kell D.B., Micklefield J., Breitling R., Takano E., Faulon J.L., Scrutton N.S. An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals. Communications Biology, 2018, 1(1): 66 (doi: 10.1038/s42003-018-0076-9).
- Gurdo N., Volke D.C., McCloskey D., Nikel P.I. Automating the design-build-test-learn cycle towards next-generation bacterial cell factories. New Biotechnology, 2023, 74: 1-15 (doi: 10.1016/j.nbt.2023.01.002).
- Opgenorth P., Costello Z., Okada T., Goyal G., Chen Y., Gin J., Benites V., de Raad M., Northen T.R., Deng K., Deutsch S., Baidoo E.E.K., Petzold C.J., Hillson N.J., Garcia Martin H., Beller H.R. Lessons from two design–build–test–learn cycles of dodecanol production in Escherichia coli aided by machine learning. ACS Synthetic Biology, 2019, 8(6): 1337-1351 (doi: 10.1021/acssynbio.9b00020).
- Rizzo P., Chavez B.G., Leite Dias S., D'Auria J.C. Plant synthetic biology: from inspiration to augmentation. Current Opinion in Biotechnology, 2023, 79: 102857 (doi: 10.1016/j.copbio.2022.102857).
- Cummins B., Vrana J., Moseley R.C., Eramian H., Deckard A., Fontanarrosa P., Bryce D., Weston M., Zheng G., Nowak J., Motta F.C., Eslami M., Johnson K.L., Goldman R.P., Myers C.J., Johnson T., Vaughn M.W., Gaffney N., Urrutia J., Gopaulakrishnan S., Biggers V., Higa T.R., Mosqueda L.A., Gameiro M., Gedeon T., Mischaikow K., Beal J., Bartley B., Mitchell T., Nguyen T.T., Roehner N., Haase S.B. Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop. Synth. Biol. (Oxf.), 2023, 8(1): ysad005 (doi: 10.1093/synbio/ysad005).
- Vidal G., Vidal-Céspedes C., Rudge T.J. LOICA: Logical Operators for Integrated Cell Algorithms. In: bioRxiv, Cold Spring Harbor Laboratory, 2021 (doi: 10.1101/2021.09.21.460548).
- HamediRad M., Chao R., Weisberg S., Lian J., Sinha S., Zhao H. Towards a fully automated algorithm driven platform for biosystems design. Nature Communications, 2019, 10(1): 5150 (doi: 10.1038/s41467-019-13189-z).
- Yang Y., Geng B., Song H., Hu M., He Q., Chen S., Bai F., Yang S. [Progress and perspective on development of non-model industrial bacteria as chassis cells for biochemical production in the synthetic biology era]. Sheng wu gong cheng xue bao = Chinese Journal of Biotechnology, 2021, 37(3): 874-910 (doi: 10.13345/j.cjb.200626).
- Feith A., Schwentner A., Teleki A., Favilli L., Blombach B., Takors R. Streamlining the analysis of dynamic 13C-labeling patterns for the metabolic engineering of Corynebacterium glutamicum as l-histidine production host. Metabolites, 2020, 10(11): 458 (doi: 10.3390/metabo10110458).
- Czajka J.J., Banerjee D., Eng T., Menasalvas J., Yan C., Munoz Munoz N., Poirier B.C., Kim Y.-Mo, Baker S.E., Tang Y.J., Mukhopadhyay A. Optimizing a high performing multiplex- CRISPRi P. putida strain with integrated metabolomics and 13C-metabolic flux analyses. In: bioRxiv, Cold Spring Harbor Laboratory, 2021 (doi: 10.1101/2021.12.23.473729).
- Lawson C.E. Retooling microbiome engineering for a sustainable future. mSystems, 2021, 6(4), e0092521 (doi: 10.1128/mSystems.00925-21).
- Vavricka C.J., Hasunuma T., Kondo A. Dynamic metabolomics for engineering biology: accelerating learning cycles for bioproduction. Trends in Biotechnology, 2020, 38(1): 68-82 (doi: 10.1016/j.tibtech.2019.07.009).
- Amer B., Baidoo E.E.K. Omics-driven biotechnology for industrial applications. Frontiers in Bioengineering and Biotechnology, 2021, 9: 613307 (doi: 10.3389/fbioe.2021.613307).
- Kim Y.-M., Petzold C.J., Kerkhoven E.J., Baker S.E. Editorial: Multi-omics technologies for optimizing synthetic biomanufacturing. Frontiers in Bioengineering and Biotechnology, 2021, 9: 818010 (doi: 10.3389/fbioe.2021.818010).
- Pathania R., Srivastava A., Srivastava S., Shukla P. Metabolic systems biology and multi-omics of cyanobacteria: Perspectives and future directions. Bioresource Technology, 2022, 343: 126007 (doi: 10.1016/j.biortech.2021.126007).
- Gurdo N., Volke D.C., Nikel P.I. Merging automation and fundamental discovery into the design–build–test–learn cycle of nontraditional microbes. Trends in Biotechnology, 2022, 40(10): 1148-1159 (doi: 10.1016/j.tibtech.2022.03.004).
- St. John P.C., Bomble Y.J. Approaches to computational strain design in the multiomics era. Frontiers in Microbiology, 2019, 10: 597 (doi: 10.3389/fmicb.2019.00597).
- Pomraning K.R., Dai Z., Munoz N., Kim Y.-M., Gao Y., Deng S., Kim J., Hofstad B.A., Swita M..S, Lemmon T., Collett J.R., Panisko E.A., Webb-Robertson B.M., Zucker J.D., Nicora C.D., De Paoli H., Baker S.E., Burnum-Johnson K.E., Hillson N.J., Magnuson J.K. Integration of proteomics and metabolomics into the design, build, test, learn cycle to improve 3-hydroxypropionic acid production in Aspergillus pseudoterreus. Frontiers in Bioengineering and Biotechnology, 2021, 9: 603832 (doi: 10.3389/fbioe.2021.603832).
- Dinglasan J.L.N., Reeves D.T., Hettich R.L., Doktycz M.J. Liquid chromatography coupled to refractive index or mass spectrometric detection for metabolite profiling in lysate-based cell-free systems. Journal of Visualized Experiments, 2021, (175): 10.3791/62852 (doi: 10.3791/62852).
- Garagounis C., Delkis N., Papadopoulou K.K. Unraveling the roles of plant specialized metabolites: using synthetic biology to design molecular biosensors. New Phytologist, 2021, 231(4): 1338-1352 (doi: 10.1111/nph.17470).
- Whitford C.M., Cruz-Morales P., Keasling J.D., Weber T. The Design-Build-Test-Learn cycle for metabolic engineering of Streptomycetes. Essays in Biochemistry, 2021, 65(2): 261-275 (doi: 10.1042/EBC20200132).
- Lawson C.E., Harcombe W.R., Hatzenpichler R., Lindemann S.R., Löffler F.E., O'Malley M.A., García Martín H., Pfleger B.F., Raskin L., Venturelli O.S., Weissbrodt D.G., Noguera D.R., McMahon K.D. Common principles and best practices for engineering microbiomes. Nature Reviews Microbiology, 2019, 17(12): 725-741 (doi: 10.1038/s41579-019-0255-9).
- Gamboa-Melendez H., Larroude M., Park Y.K., Trebul P., Nicaud J.-M., Ledesma-Amaro R. Synthetic biology to improve the production of lipases and esterases (review). Methods Mol. Biol., 2018, 1835: 229-242 (doi: 10.1007/978-1-4939-8672-9_13).
- Kothamachu V.B., Zaini S., Muffatto F. Role of digital microfluidics in enabling access to laboratory automation and making biology programmable. SLAS Technology, 2020, 25(5): 411-426 (doi: 10.1177/2472630320931794).
- Shih S.C.C., Moraes C. Next generation tools to accelerate the synthetic biology process. Integrative Biology, 2016, 8(5): 585-588 (doi: 10.1039/C6IB90017H).
- Sohrabi S., Kassir N., Keshavarz Moraveji M. Droplet microfluidics: fundamentals and its advanced applications. RSC Advances, 2020, 10(46): 27560-27574 (doi: 10.1039/D0RA04566G).
- Arazoe T., Kondo A., Nishida K. Targeted nucleotide editing technologies for microbial metabolic engineering. Biotechnology Journal, 2018, 13(9): 1700596 (doi: 10.1002/biot.201700596).
- Baltes N.J., Voytas D.F. Enabling plant synthetic biology through genome engineering. Trends in Biotechnology, 2015, 33(2): 120-131 (doi: 10.1016/j.tibtech.2014.11.008).
- Delépine B., Duigou T., Carbonell P., Faulon J.-L. RetroPath2.0: A retrosynthesis workflow for metabolic engineers. Metabolic Engineering, 2018, 45: 158-170 (doi: 10.1016/j.ymben.2017.12.002).
- Carbonell P., Wong J., Swainston N., Takano E., Turner N.J., Scrutton N.S., Kell D.B., Breitling R., Faulon J.L. Selenzyme: enzyme selection tool for pathway design. Bioinformatics, 2018, 34(12): 2153- 2154 (doi: 10.1093/bioinformatics/bty065).
- Swainston N., Dunstan M., Jervis A.J., Robinson C.J., Carbonell P., Williams A.R., Faulon J.L., Scrutton N.S., Kell D.B. PartsGenie: an integrated tool for optimizing and sharing synthetic biology parts. Bioinformatics, 2018, 34(13): 2327-2329 (doi: 10.1093/bioinformatics/bty105).
- Fields-Johnson C., Fike J., Galbraith J., Maguire R., Day S., Zedaker S., Mathis J. Pine sawdust biochar as a potential amendment for establishing trees in Appalachian mine spoils. REFORESTA, 2018, (6): 1-14 (doi: 10.21750/REFOR.6.01.54).
- Yáñez Feliú G., Earle Gómez B., Codoceo Berrocal V., Muñoz Silva M., Nuñez I.N., Matute T.F., Arce Medina A., Vidal G., Vitalis C., Dahlin J, Federici F, Rudge TJ.Flapjack: data management and analysis for genetic circuit characterization. ACS Synthetic Biology, 2021, 10(1): 183-191 (doi: 10.1021/acssynbio.0c00554).
- Orsi E., Claassens N.J., Nikel P.I., Lindner S.N. Growth-coupled selection of synthetic modules to accelerate cell factory development. Nature Communications, 2021, 12(1): 5295 (doi: 10.1038/s41467-021-25665-6).
- Gao Y., Fillmore T.L., Munoz N., Bentley G.J., Johnson C.W., Kim J., Meadows J.A., Zucker J.D., Burnet M.C., Lipton A.K., Bilbao A., Orton D.J., Kim Y.M., Moore R.J., Robinson E.W., Baker S.E., Webb-Robertson B.M., Guss A.M., Gladden J.M., Beckham G.T., Magnuson J.K., Burnum-Johnson K.E. High-throughput large-scale targeted proteomics assays for quantifying pathway proteins in Pseudomonas putida KT2440. Frontiers in Bioengineering and Biotechnology, 2020, 8: 603488 (doi: 10.3389/fbioe.2020.603488).
- Roy S., Radivojevic T., Forrer M., Marti J.M., Jonnalagadda V., Backman T., Morrell W., Plahar H., Kim J., Hillson N., Garcia Martin H. Multiomics data collection, visualization, and utilization for guiding metabolic engineering. Frontiers in Bioengineering and Biotechnology, 2021, 9: 612893 (doi: 10.3389/fbioe.2021.612893).
- Zhang J., Chen Y., Fu L., Guo E., Wang B., Dai L., Si T. Accelerating strain engineering in biofuel research via build and test automation of synthetic biology. Current Opinion in Biotechnology, 2021, 67: 88-98 (doi: 10.1016/j.copbio.2021.01.010).
- Radivojević T., Costello Z., Workman K., Garcia Martin H. A machine learning Automated Recommendation Tool for synthetic biology. Nature Communications, 2020, 11(1): 4879 (doi: 10.1038/s41467-020-18008-4).
- Kamran S., Shahid I., Baig D.N., Rizwan M., Malik K.A., Mehnaz S. Contribution of Zinc solubilizing bacteria in growth promotion and zinc content of wheat. Frontiers in Microbiology, 2017, 8: 2593 (doi: 10.3389/fmicb.2017.02593).
- Araki M., Cox R.S., Makiguchi H., Ogawa T., Taniguchi T., Miyaoku K., Nakatsui M., Hara K.Y., Kondo A. M-path: a compass for navigating potential metabolic pathways. Bioinformatics, 2015, 31(6): 905-911, (10.1093/bioinformatics/btu750).
- Nakazawa S., Imaichi O., Kogure T., Kubota T., Toyoda K., Suda M., Inui M., Ito K., Shirai T., Araki M. History-driven genetic modification design technique using a domain-specific lexical model for the acceleration of DBTL cycles for microbial cell factories. ACS Synthetic Biology, 2021, 10(9): 2308-2317 (doi: 10.1021/acssynbio.1c00234).
- Dunstan M.S., Robinson C.J., Jervis A.J., Yan C., Carbonell P., Hollywood K.A., Currin A., Swainston N., Feuvre R.L., Micklefield J., Faulon J.L., Breitling R., Turner N., Takano E., Scrutton N.S. Engineering Escherichia coli towards de novo production of gatekeeper (2S)- flavanones: naringenin, pinocembrin, eriodictyol and homoeriodictyol. Synthetic Biology, 2020, 5(1): ysaa012 (doi: 10.1093/synbio/ysaa012).
- Ro D.-K., Paradise E.M., Ouellet M., Fisher K.J., Newman K.L., Ndungu J.M., Ho K.A., Eachus R.A., Ham T.S., Kirby J., Chang M.C., Withers S.T., Shiba Y., Sarpong R., Keasling J.D. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature, 2006, 440(7086): 940-943 (doi: 10.1038/nature04640).
- Westfall P.J., Pitera D.J., Lenihan J.R., Eng D., Woolard F.X., Regentin R., Horning T., Tsuruta H., Melis D.J., Owens A., Fickes S., Diola D., Benjamin K.R., Keasling J.D., Leavell M.D., McPhee D.J., Renninger N.S., Newman J.D., Paddon C.J. Production of amorphadiene in yeast, and its conversion to dihydroartemisinic acid, precursor to the antimalarial agent artemisinin. Proceedings of the National Academy of Sciences, 2012, 109(3): E111-118 (doi: 10.1073/pnas.1110740109).
- Chen R., Yang S., Zhang L., Zhou Y.J. Advanced strategies for production of natural products in yeast. iScience, 2020, 23(3): 100879 (doi: 10.1016/j.isci.2020.100879).
- Nielsen J., Keasling J.D. Engineering cellular metabolism. Cell, 2016, 164(6): 1185-1197 (doi: 10.1016/j.cell.2016.02.004).
- Zhu X., Du C., Mohsin A., Yin Q., Xu F., Liu Z., Wang Z., Zhuang Y., Chu J., Guo M., Tian X. An efficient high-throughput screening of high gentamicin-producing mutants based on titer determination using an integrated Computer-Aided Vision Technology and Machine Learning. Analytical Chemistry, 2022, 94(33): 11659-11669 (doi: 10.1021/acs.analchem.2c02289).
- Liu J.-M., Chen L., Jensen P.R., Solem C. Food grade microbial synthesis of the butter aroma compound butanedione using engineered and non-engineered Lactococcus lactis. Metabolic Engineering, 2021, 67: 443-452 (doi: 10.1016/j.ymben.2021.08.006).
- Ramzi A.B., Baharum S.N., Bunawan H., Scrutton N.S. Streamlining natural products biomanufacturing with omics and machine learning driven microbial engineering. Frontiers in Bioengineering and Biotechnology, 2020, 8: 608918 (doi: 10.3389/fbioe.2020.608918).
- Carbonell P., Le Feuvre R., Takano E., Scrutton N.S. In silico design and automated learning to boost next-generation smart biomanufacturing. Synthetic Biology, 2020, 5(1): ysaa020 (doi: 10.1093/synbio/ysaa020).
- Freemont P.S. Synthetic biology industry: data-driven design is creating new opportunities in biotechnology. Emerging Topics in Life Sciences, 2019, 3(5): 651-657 (doi: 10.1042/ETLS20190040).
- Zhu Q., Yu S., Zeng D., Liu H., Wang H., Yang Z., Xie X., Shen R., Tan J., Li H., Zhao X., Zhang Q., Chen Y., Guo J., Chen L., Liu Y.G. Development of “Purple Endosperm Rice” by engineering anthocyanin biosynthesis in the endosperm with a high-efficiency transgene stacking system. Molecular Plant, 2017, 10(7): 918-929 (doi: 10.1016/j.molp.2017.05.008).
- Lin J., Lin W., Ng I. CRISPRa/i with Adaptive Single Guide Assisted Regulation DNA (ASGARD) mediated control of Chlorella sorokiniana to enhance lipid and protein production. Biotechnology Journal, 2022, 17(10): 2100514 (doi: 10.1002/biot.202100514).
- Kuivanen J., Holmström S., Lehtinen B., Penttilä M., Jäntti J. A high-throughput workflow for CRISPR/Cas9 mediated combinatorial promoter replacements and phenotype characterization in yeast. Biotechnology Journal, 2018, 13(9): 1700593 (10.1002/biot.201700593).
- Liu Z., Wang J., Nielsen J. Yeast synthetic biology advances biofuel production. Current Opinion in Microbiology, 2022, 65: 33-39 (doi: 10.1016/j.mib.2021.10.010).
- Czajka J.J., Banerjee D., Eng T., Menasalvas J., Yan C., Munoz N.M., Poirier B.C., Kim Y.M., Baker S.E., Tang Y.J., Mukhopadhyay A. Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production. Metabolic Engineering Communications, 2022, 15: e00206 (doi: 10.1016/j.mec.2022.e00206).
- Vidal G., Vidal-Céspedes C., Rudge T.J. LOICA: integrating models with data for genetic network design automation. ACS Synthetic Biology, 2022, 11(5): 1984-1990 (doi: 10.1021/acssynbio.1c00603).
- Li X.X., Lan C., Li X.X., Hu Z., Jia B. A review on design-build-test-learn cycle to potentiate progress in isoprenoid engineering of photosynthetic microalgae. Bioresource Technology, 2022, 363: 127981 (doi: 10.1016/j.biortech.2022.127981).
- Zhang M., Holowko M.B., Hayman Zumpe H., Ong C.S. Machine learning guided Batched design of a bacterial ribosome binding site. ACS Synthetic Biology, 2022, 11(7): 2314-2326 (doi: 10.1021/acssynbio.2c00015).
- Kamminga T., Slagman S.-J., Martins dos Santos V.A.P., Bijlsma J.J.E., Schaap P.J. Risk-based bioengineering strategies for reliable bacterial vaccine production. Trends in Biotechnology, 2019, 37(8): 805-816 (doi: 10.1016/j.tibtech.2019.03.005).
- Ando D., García Martín H. Genome-scale 13C fluxomics modeling for metabolic engineering of Saccharomyces cerevisiae. Methods Mol. Biol., 2019, 1859: 317-345 (doi: 10.1007/978-1-4939-8757-3_19).
- Sabzevari M., Szedmak S., Penttilä M., Jouhten P., Rousu J. Strain design optimization using reinforcement learning. PLOS Computational Biology, 2022, 18(6): e1010177 (doi: 10.1371/journal.pcbi.1010177).