Agrawal, D. K., Dolan, E. M., Hernandez, N. E., Blacklock, K. M., Khare, S. D., & Sontag, E. D. (2020). Mathematical Models of Protease-Based Enzymatic Biosensors. ACS Synthetic Biology, 9(2), 198-208. doi:10.1021/acssynbio.9b00279
Arnold, F. H. (2017). Directed Evolution: Bringing New Chemistry to Life. Angewandte Chemie International Edition, 57(16), 4143-4148. doi:10.1002/anie.201708408
Boada, Y., Vignoni, A., & Picó, J. (2017). Engineered Control of Genetic Variability Reveals Interplay among Quorum Sensing, Feedback Regulation, and Biochemical Noise. ACS Synthetic Biology, 6(10), 1903-1912. doi:10.1021/acssynbio.7b00087
[+]
Agrawal, D. K., Dolan, E. M., Hernandez, N. E., Blacklock, K. M., Khare, S. D., & Sontag, E. D. (2020). Mathematical Models of Protease-Based Enzymatic Biosensors. ACS Synthetic Biology, 9(2), 198-208. doi:10.1021/acssynbio.9b00279
Arnold, F. H. (2017). Directed Evolution: Bringing New Chemistry to Life. Angewandte Chemie International Edition, 57(16), 4143-4148. doi:10.1002/anie.201708408
Boada, Y., Vignoni, A., & Picó, J. (2017). Engineered Control of Genetic Variability Reveals Interplay among Quorum Sensing, Feedback Regulation, and Biochemical Noise. ACS Synthetic Biology, 6(10), 1903-1912. doi:10.1021/acssynbio.7b00087
Boada, Y., Vignoni, A., & Picó, J. (2017). Multi-objective optimization for gene expression noise reduction in a synthetic gene circuit * *This work is partially supported by Spanish government and European Union (FEDER-CICYT DPI2014-55276-C5-1). Y.B. thanks grant FPI/2013-3242 of Universitat Politècnica de València, and also thanks the support from the Ayudas para movilidad dentro del Programa para la Formación de Personal Investigador (FPI) de la UPV para estancias 2016. A.V. thanks the Max Planck Society, the CSBD and the MPI-CBG. The authors are grateful to Prof. Dr. Ivo F. Sbalzarini for hosting Y.B in the MOSAIC Group for a research stay, also to Pietro Incadorna from the MOSAIC Group at CSBD for his help in the parallel algorithm implementation, and to Dr. Gilberto Reynoso-Meza from the PPGEPS at Pontifícia Universidade Católica do Paraná for his always helpful comments regarding the MOOD. IFAC-PapersOnLine, 50(1), 4472-4477. doi:10.1016/j.ifacol.2017.08.376
Boada, Y., Vignoni, A., & Pico, J. (2020). Multiobjective Identification of a Feedback Synthetic Gene Circuit. IEEE Transactions on Control Systems Technology, 28(1), 208-223. doi:10.1109/tcst.2018.2885694
Briat, C., Gupta, A., & Khammash, M. (2016). Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks. Cell Systems, 2(1), 15-26. doi:10.1016/j.cels.2016.01.004
Briat, C., & Khammash, M. (2018). Perfect Adaptation and Optimal Equilibrium Productivity in a Simple Microbial Biofuel Metabolic Pathway Using Dynamic Integral Control. ACS Synthetic Biology, 7(2), 419-431. doi:10.1021/acssynbio.7b00188
Carbonell, P., Jervis, A. J., Robinson, C. J., Yan, C., Dunstan, M., Swainston, N., … Scrutton, N. S. (2018). An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals. Communications Biology, 1(1). doi:10.1038/s42003-018-0076-9
Carbonell, P., Parutto, P., Baudier, C., Junot, C., & Faulon, J.-L. (2013). Retropath: Automated Pipeline for Embedded Metabolic Circuits. ACS Synthetic Biology, 3(8), 565-577. doi:10.1021/sb4001273
Ceroni, F., Boo, A., Furini, S., Gorochowski, T. E., Borkowski, O., Ladak, Y. N., … Ellis, T. (2018). Burden-driven feedback control of gene expression. Nature Methods, 15(5), 387-393. doi:10.1038/nmeth.4635
Chae, T. U., Choi, S. Y., Kim, J. W., Ko, Y.-S., & Lee, S. Y. (2017). Recent advances in systems metabolic engineering tools and strategies. Current Opinion in Biotechnology, 47, 67-82. doi:10.1016/j.copbio.2017.06.007
Chen, X., & Liu, L. (2018). Gene Circuits for Dynamically Regulating Metabolism. Trends in Biotechnology, 36(8), 751-754. doi:10.1016/j.tibtech.2017.12.007
Cheng, F., Tang, X.-L., & Kardashliev, T. (2018). Transcription Factor-Based Biosensors in High-Throughput Screening: Advances and Applications. Biotechnology Journal, 13(7), 1700648. doi:10.1002/biot.201700648
Choi, J. H., Keum, K. C., & Lee, S. Y. (2006). Production of recombinant proteins by high cell density culture of Escherichia coli. Chemical Engineering Science, 61(3), 876-885. doi:10.1016/j.ces.2005.03.031
Delépine, B., Libis, V., Carbonell, P., & Faulon, J.-L. (2016). SensiPath: computer-aided design of sensing-enabling metabolic pathways. Nucleic Acids Research, 44(W1), W226-W231. doi:10.1093/nar/gkw305
Dinh, C. V., Chen, X., & Prather, K. L. J. (2020). Development of a Quorum-Sensing Based Circuit for Control of Coculture Population Composition in a Naringenin Production System. ACS Synthetic Biology, 9(3), 590-597. doi:10.1021/acssynbio.9b00451
Doong, S. J., Gupta, A., & Prather, K. L. J. (2018). Layered dynamic regulation for improving metabolic pathway productivity inEscherichia coli. Proceedings of the National Academy of Sciences, 115(12), 2964-2969. doi:10.1073/pnas.1716920115
Evans, C. R., Kempes, C. P., Price-Whelan, A., & Dietrich, L. E. P. (2020). Metabolic Heterogeneity and Cross-Feeding in Bacterial Multicellular Systems. Trends in Microbiology, 28(9), 732-743. doi:10.1016/j.tim.2020.03.008
Gao, C., Xu, P., Ye, C., Chen, X., & Liu, L. (2019). Genetic Circuit-Assisted Smart Microbial Engineering. Trends in Microbiology, 27(12), 1011-1024. doi:10.1016/j.tim.2019.07.005
Goldberg, A. P., Szigeti, B., Chew, Y. H., Sekar, J. A., Roth, Y. D., & Karr, J. R. (2018). Emerging whole-cell modeling principles and methods. Current Opinion in Biotechnology, 51, 97-102. doi:10.1016/j.copbio.2017.12.013
Hsiao, V., Swaminathan, A., & Murray, R. M. (2018). Control Theory for Synthetic Biology: Recent Advances in System Characterization, Control Design, and Controller Implementation for Synthetic Biology. IEEE Control Systems, 38(3), 32-62. doi:10.1109/mcs.2018.2810459
Huyett, L. M., Dassau, E., Zisser, H. C., & Doyle, F. J. (2018). Glucose Sensor Dynamics and the Artificial Pancreas: The Impact of Lag on Sensor Measurement and Controller Performance. IEEE Control Systems, 38(1), 30-46. doi:10.1109/mcs.2017.2766322
Johnson, A. O., Gonzalez-Villanueva, M., Wong, L., Steinbüchel, A., Tee, K. L., Xu, P., & Wong, T. S. (2017). Design and application of genetically-encoded malonyl-CoA biosensors for metabolic engineering of microbial cell factories. Metabolic Engineering, 44, 253-264. doi:10.1016/j.ymben.2017.10.011
Juminaga, D., Baidoo, E. E. K., Redding-Johanson, A. M., Batth, T. S., Burd, H., Mukhopadhyay, A., … Keasling, J. D. (2011). Modular Engineering of l-Tyrosine Production in Escherichia coli. Applied and Environmental Microbiology, 78(1), 89-98. doi:10.1128/aem.06017-11
Koch, M., Pandi, A., Delépine, B., & Faulon, J.-L. (2018). A dataset of small molecules triggering transcriptional and translational cellular responses. Data in Brief, 17, 1374-1378. doi:10.1016/j.dib.2018.02.061
LEONARD, E., YAN, Y., & KOFFAS, M. (2006). Functional expression of a P450 flavonoid hydroxylase for the biosynthesis of plant-specific hydroxylated flavonols in Escherichia coli. Metabolic Engineering, 8(2), 172-181. doi:10.1016/j.ymben.2005.11.001
Lin, J.-L., Wagner, J. M., & Alper, H. S. (2017). Enabling tools for high-throughput detection of metabolites: Metabolic engineering and directed evolution applications. Biotechnology Advances, 35(8), 950-970. doi:10.1016/j.biotechadv.2017.07.005
Liu, D., Mannan, A. A., Han, Y., Oyarzún, D. A., & Zhang, F. (2018). Dynamic metabolic control: towards precision engineering of metabolism. Journal of Industrial Microbiology and Biotechnology, 45(7), 535-543. doi:10.1007/s10295-018-2013-9
Liu, D., Xiao, Y., Evans, B. S., & Zhang, F. (2014). Negative Feedback Regulation of Fatty Acid Production Based on a Malonyl-CoA Sensor–Actuator. ACS Synthetic Biology, 4(2), 132-140. doi:10.1021/sb400158w
Liu, D., & Zhang, F. (2018). Metabolic Feedback Circuits Provide Rapid Control of Metabolite Dynamics. ACS Synthetic Biology, 7(2), 347-356. doi:10.1021/acssynbio.7b00342
Liu, L., Shan, S., Zhang, K., Ning, Z.-Q., Lu, X.-P., & Cheng, Y.-Y. (2008). Naringenin and hesperetin, two flavonoids derived fromCitrus aurantiumup-regulate transcription of adiponectin. Phytotherapy Research, 22(10), 1400-1403. doi:10.1002/ptr.2504
Mahr, R., & Frunzke, J. (2015). Transcription factor-based biosensors in biotechnology: current state and future prospects. Applied Microbiology and Biotechnology, 100(1), 79-90. doi:10.1007/s00253-015-7090-3
Mannan, A. A., Liu, D., Zhang, F., & Oyarzún, D. A. (2017). Fundamental Design Principles for Transcription-Factor-Based Metabolite Biosensors. ACS Synthetic Biology, 6(10), 1851-1859. doi:10.1021/acssynbio.7b00172
McKeague, M., Wong, R. S., & Smolke, C. D. (2016). Opportunities in the design and application of RNA for gene expression control. Nucleic Acids Research, 44(7), 2987-2999. doi:10.1093/nar/gkw151
Nielsen, A. A. K., Der, B. S., Shin, J., Vaidyanathan, P., Paralanov, V., Strychalski, E. A., … Voigt, C. A. (2016). Genetic circuit design automation. Science, 352(6281), aac7341-aac7341. doi:10.1126/science.aac7341
Nikolados, E.-M., Weiße, A. Y., Ceroni, F., & Oyarzún, D. A. (2019). Growth Defects and Loss-of-Function in Synthetic Gene Circuits. ACS Synthetic Biology, 8(6), 1231-1240. doi:10.1021/acssynbio.8b00531
De Paepe, B., Maertens, J., Vanholme, B., & De Mey, M. (2018). Modularization and Response Curve Engineering of a Naringenin-Responsive Transcriptional Biosensor. ACS Synthetic Biology, 7(5), 1303-1314. doi:10.1021/acssynbio.7b00419
Rahigude, A., Bhutada, P., Kaulaskar, S., Aswar, M., & Otari, K. (2012). Participation of antioxidant and cholinergic system in protective effect of naringenin against type-2 diabetes-induced memory dysfunction in rats. Neuroscience, 226, 62-72. doi:10.1016/j.neuroscience.2012.09.026
Rhodius, V. A., Segall‐Shapiro, T. H., Sharon, B. D., Ghodasara, A., Orlova, E., Tabakh, H., … Voigt, C. A. (2013). Design of orthogonal genetic switches based on a crosstalk map of σs, anti‐σs, and promoters. Molecular Systems Biology, 9(1), 702. doi:10.1038/msb.2013.58
Rodriguez, A., Strucko, T., Stahlhut, S. G., Kristensen, M., Svenssen, D. K., Forster, J., … Borodina, I. (2017). Metabolic engineering of yeast for fermentative production of flavonoids. Bioresource Technology, 245, 1645-1654. doi:10.1016/j.biortech.2017.06.043
Segall-Shapiro, T. H., Sontag, E. D., & Voigt, C. A. (2018). Engineered promoters enable constant gene expression at any copy number in bacteria. Nature Biotechnology, 36(4), 352-358. doi:10.1038/nbt.4111
Shi, S., Ang, E. L., & Zhao, H. (2018). In vivo biosensors: mechanisms, development, and applications. Journal of Industrial Microbiology and Biotechnology, 45(7), 491-516. doi:10.1007/s10295-018-2004-x
Shopera, T., He, L., Oyetunde, T., Tang, Y. J., & Moon, T. S. (2017). Decoupling Resource-Coupled Gene Expression in Living Cells. ACS Synthetic Biology, 6(8), 1596-1604. doi:10.1021/acssynbio.7b00119
Siedler, S., Stahlhut, S. G., Malla, S., Maury, J., & Neves, A. R. (2014). Novel biosensors based on flavonoid-responsive transcriptional regulators introduced into Escherichia coli. Metabolic Engineering, 21, 2-8. doi:10.1016/j.ymben.2013.10.011
Snoek, T., Chaberski, E. K., Ambri, F., Kol, S., Bjørn, S. P., Pang, B., … Keasling, J. D. (2019). Evolution-guided engineering of small-molecule biosensors. Nucleic Acids Research, 48(1), e3-e3. doi:10.1093/nar/gkz954
Stevens, J. T., & Carothers, J. M. (2014). Designing RNA-Based Genetic Control Systems for Efficient Production from Engineered Metabolic Pathways. ACS Synthetic Biology, 4(2), 107-115. doi:10.1021/sb400201u
Trantas, E., Panopoulos, N., & Ververidis, F. (2009). Metabolic engineering of the complete pathway leading to heterologous biosynthesis of various flavonoids and stilbenoids in Saccharomyces cerevisiae. Metabolic Engineering, 11(6), 355-366. doi:10.1016/j.ymben.2009.07.004
Wang, R., Cress, B. F., Yang, Z., Hordines, J. C., Zhao, S., Jung, G. Y., … Koffas, M. A. G. (2019). Design and Characterization of Biosensors for the Screening of Modular Assembled Naringenin Biosynthetic Library in Saccharomyces cerevisiae. ACS Synthetic Biology, 8(9), 2121-2130. doi:10.1021/acssynbio.9b00212
Wehrs, M., Tanjore, D., Eng, T., Lievense, J., Pray, T. R., & Mukhopadhyay, A. (2019). Engineering Robust Production Microbes for Large-Scale Cultivation. Trends in Microbiology, 27(6), 524-537. doi:10.1016/j.tim.2019.01.006
Xu, P., Li, L., Zhang, F., Stephanopoulos, G., & Koffas, M. (2014). Improving fatty acids production by engineering dynamic pathway regulation and metabolic control. Proceedings of the National Academy of Sciences, 111(31), 11299-11304. doi:10.1073/pnas.1406401111
Xu, P., Ranganathan, S., Fowler, Z. L., Maranas, C. D., & Koffas, M. A. G. (2011). Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA. Metabolic Engineering, 13(5), 578-587. doi:10.1016/j.ymben.2011.06.008
Yang, Y., Lin, Y., Li, L., Linhardt, R. J., & Yan, Y. (2015). Regulating malonyl-CoA metabolism via synthetic antisense RNAs for enhanced biosynthesis of natural products. Metabolic Engineering, 29, 217-226. doi:10.1016/j.ymben.2015.03.018
Zhou, S., Lyu, Y., Li, H., Koffas, M. A. G., & Zhou, J. (2019). Fine‐tuning the (2
S
)‐naringenin synthetic pathway using an iterative high‐throughput balancing strategy. Biotechnology and Bioengineering, 116(6), 1392-1404. doi:10.1002/bit.26941
Zygmunt, K., Faubert, B., MacNeil, J., & Tsiani, E. (2010). Naringenin, a citrus flavonoid, increases muscle cell glucose uptake via AMPK. Biochemical and Biophysical Research Communications, 398(2), 178-183. doi:10.1016/j.bbrc.2010.06.048
[-]