External control of microbial populations for bioproduction: A modeling and optimization viewpoint
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[1] U. Alon,et al. Optimality and sub-optimality in a bacterial growth law , 2017, Nature Communications.
[2] Jibin Sun,et al. Biomanufacturing: history and perspective , 2017, Journal of Industrial Microbiology & Biotechnology.
[3] Julius von Kügelgen,et al. A bacterial size law revealed by a coarse-grained model of cell physiology , 2019, PLoS Comput. Biol..
[4] Morten Otto Alexander Sommer,et al. Synthetic addiction extends the productive life time of engineered Escherichia coli populations , 2018, Proceedings of the National Academy of Sciences.
[5] Jens Nielsen,et al. Construction of mini‐chemostats for high‐throughput strain characterization , 2019, Biotechnology and bioengineering.
[6] Mary J Dunlop,et al. Controlling and exploiting cell-to-cell variation in metabolic engineering. , 2019, Current opinion in biotechnology.
[7] Jason S Crater,et al. Scale-up of industrial microbial processes , 2018, FEMS microbiology letters.
[8] Andrea Y Weiße,et al. Growth Defects and Loss-of-Function in Synthetic Gene Circuits. , 2019, ACS synthetic biology.
[9] Christopher A. Voigt,et al. Synthetic biology 2020–2030: six commercially-available products that are changing our world , 2020, Nature Communications.
[10] Kristala L. J. Prather,et al. Dynamic regulation of metabolic flux in engineered bacteria using a pathway-independent quorum-sensing circuit , 2017, Nature Biotechnology.
[11] R. Kitney,et al. Developing synthetic biology for industrial biotechnology applications , 2020, Biochemical Society transactions.
[12] T. Hwa,et al. Interdependence of Cell Growth and Gene Expression: Origins and Consequences , 2010, Science.
[13] Jared E. Toettcher,et al. Optogenetic regulation of engineered cellular metabolism for microbial chemical production , 2018, Nature.
[14] G. Stan,et al. Burden-driven feedback control of gene expression , 2017, Nature Methods.
[15] S. Goyal,et al. An Automated Tabletop Continuous Culturing System with Multicolor Fluorescence Monitoring for Microbial Gene Expression and Long-Term Population Dynamics. , 2021, ACS synthetic biology.
[16] Jakob Ruess,et al. Optimal control of an artificial microbial differentiation system for protein bioproduction , 2019, 2019 18th European Control Conference (ECC).
[17] Zanmin Hu,et al. The Potential for Microalgae as Bioreactors to Produce Pharmaceuticals , 2016, International journal of molecular sciences.
[18] A light tunable differentiation system for the creation and control of consortia in yeast , 2021, Nature communications.
[19] B. Teusink,et al. Shifts in growth strategies reflect tradeoffs in cellular economics , 2009, Molecular systems biology.
[20] F. Bertaux,et al. Enhancing bioreactor arrays for automated measurements and reactive control with ReacSight , 2020, Nature Communications.
[21] Philipp Thomas. Intrinsic and extrinsic noise of gene expression in lineage trees , 2019, Scientific Reports.
[22] U. Alon,et al. Optimality and evolutionary tuning of the expression level of a protein , 2005, Nature.
[23] Richard M. Murray,et al. Future systems and control research in synthetic biology , 2018, Annu. Rev. Control..
[24] Johannes Geiselmann,et al. A synthetic growth switch based on controlled expression of RNA polymerase , 2015, Molecular systems biology.
[25] Pascal Hersen,et al. The Promise of Optogenetics for Bioproduction: Dynamic Control Strategies and Scale-Up Instruments , 2020, Bioengineering.
[26] Matthew Deaner,et al. Recent advancements in fungal-derived fuel and chemical production and commercialization. , 2019, Current opinion in biotechnology.
[27] T. Hwa,et al. Emergence of robust growth laws from optimal regulation of ribosome synthesis , 2014, Molecular systems biology.
[28] P. Swain,et al. Mechanistic links between cellular trade-offs, gene expression, and growth , 2015, Proceedings of the National Academy of Sciences.
[29] J. Keasling,et al. Microbial engineering for the production of advanced biofuels , 2012, Nature.
[30] Jean-Luc Gouzé,et al. Dynamical Allocation of Cellular Resources as an Optimal Control Problem: Novel Insights into Microbial Growth Strategies , 2016, PLoS Comput. Biol..
[31] Jean-Luc Gouzé,et al. Optimal control of bacterial growth for the maximization of metabolite production , 2018, Journal of Mathematical Biology.
[32] Tom Ellis,et al. The second decade of synthetic biology: 2010–2020 , 2020, Nature Communications.
[33] N. Barkai,et al. Rethinking cell growth models. , 2016, FEMS yeast research.
[34] Lorenzo Duso,et al. Stochastic reaction networks in dynamic compartment populations , 2020, Proceedings of the National Academy of Sciences.
[35] Fuzhong Zhang,et al. Control strategies to manage trade-offs during microbial production , 2020, Current opinion in biotechnology.
[36] J. Ruess,et al. Beyond the chemical master equation: Stochastic chemical kinetics coupled with auxiliary processes , 2021, PLoS Comput. Biol..
[37] Christina D. Smolke,et al. Synthetic biology strategies for microbial biosynthesis of plant natural products , 2019, Nature Communications.
[38] H. Alper,et al. Applications, challenges, and needs for employing synthetic biology beyond the lab , 2021, Nature Communications.
[39] Eric Klavins,et al. A Low Cost, Customizable Turbidostat for Use in Synthetic Circuit Characterization , 2014, ACS synthetic biology.
[40] Xiaomei Lv,et al. Sequential control of biosynthetic pathways for balanced utilization of metabolic intermediates in Saccharomyces cerevisiae. , 2015, Metabolic engineering.
[41] F. Rudolf,et al. A shuttle vector series for precise genetic engineering of Saccharomyces cerevisiae , 2016, Yeast.
[42] Brandon G Wong,et al. Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER , 2018, Nature Biotechnology.