Trade-offs between gene expression, growth and phenotypic diversity in microbial populations
暂无分享,去创建一个
José Utrilla | José I Jiménez | Juhyun Kim | M. Salvador | J. Utrilla | José I. Jiménez | Juhyun Kim | A. Darlington | Alexander Darlington | Manuel Salvador
[1] Frank J Bruggeman,et al. Taking chances and making mistakes: non-genetic phenotypic heterogeneity and its consequences for surviving in dynamic environments , 2017, Journal of The Royal Society Interface.
[2] J. Audinot,et al. Division-Based, Growth Rate Diversity in Bacteria , 2018, Front. Microbiol..
[3] N. Verstraeten,et al. General Mechanisms Leading to Persister Formation and Awakening. , 2019, Trends in genetics : TIG.
[4] Radhakrishnan Mahadevan,et al. Economics of membrane occupancy and respiro-fermentation , 2011, Molecular systems biology.
[5] Edward J. O'Brien,et al. Quantification and Classification of E. coli Proteome Utilization and Unused Protein Costs across Environments , 2016, PLoS Comput. Biol..
[6] A. Arkin,et al. Contextualizing context for synthetic biology – identifying causes of failure of synthetic biological systems , 2012, Biotechnology journal.
[7] T. Nyström. MicroReview: Growth versus maintenance: a trade‐off dictated by RNA polymerase availability and sigma factor competition? , 2004, Molecular microbiology.
[8] Byung-Kwan Cho,et al. Adaptive laboratory evolution of a genome-reduced Escherichia coli , 2019, Nature Communications.
[9] Bruno M. C. Martins,et al. Microbial individuality: how single-cell heterogeneity enables population level strategies. , 2015, Current opinion in microbiology.
[10] C. Rodríguez-Caso,et al. Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm's law , 2015, Nucleic acids research.
[11] P. Swain,et al. Mechanistic links between cellular trade-offs, gene expression, and growth , 2015, Proceedings of the National Academy of Sciences.
[12] Manuel Porcar,et al. Standards not that standard , 2015, Journal of biological engineering.
[13] Christopher J Petzold,et al. Programming mRNA decay to modulate synthetic circuit resource allocation , 2016, Nature Communications.
[14] R. Aebersold,et al. The quantitative and condition-dependent Escherichia coli proteome , 2015, Nature Biotechnology.
[15] Declan G. Bates,et al. Dynamic allocation of orthogonal ribosomes facilitates uncoupling of co-expressed genes , 2017, Nature Communications.
[16] Thomas E Gorochowski,et al. A Minimal Model of Ribosome Allocation Dynamics Captures Trade-offs in Expression between Endogenous and Synthetic Genes. , 2016, ACS synthetic biology.
[17] G. Stan,et al. Quantifying cellular capacity identifies gene expression designs with reduced burden , 2015, Nature Methods.
[18] Oscar P Kuipers,et al. Microbial bet-hedging: the power of being different. , 2015, Current opinion in microbiology.
[19] Terence Hwa,et al. A global resource allocation strategy governs growth transition kinetics of Escherichia coli , 2017, Nature.
[20] M. A. de Menezes,et al. Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity , 2007, Proceedings of the National Academy of Sciences.
[21] Ruth J. Williams,et al. Queueing up for Enzymatic Processing: Correlated Signaling through Coupled Degradation , 2022 .
[22] Terence Hwa,et al. Bacterial growth laws and their applications. , 2011, Current opinion in biotechnology.
[23] T. Nyström,et al. Negative regulation by RpoS: a case of sigma factor competition , 1998, Molecular microbiology.
[24] I. Moll,et al. Autoregulation of mazEF expression underlies growth heterogeneity in bacterial populations , 2018, Nucleic acids research.
[25] Adam J. Meyer,et al. A ‘resource allocator’ for transcription based on a highly fragmented T7 RNA polymerase , 2014, Molecular systems biology.
[26] Baojun Wang,et al. Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology , 2011, Nature communications.
[27] M. Sørensen,et al. Synthesis of proteins in Escherichia coli is limited by the concentration of free ribosomes. Expression from reporter genes does not always reflect functional mRNA levels. , 1993, Journal of molecular biology.
[28] Ron Weiss,et al. Isocost Lines Describe the Cellular Economy of Genetic Circuits , 2015, Biophysical journal.
[29] Benjamin R. K. Roller,et al. Exploiting rRNA Operon Copy Number to Investigate Bacterial Reproductive Strategies , 2016, Nature Microbiology.
[30] Xiongfeng Dai,et al. (p)ppGpp: the magic governor of bacterial growth economy , 2019, Current Genetics.
[31] David W. Erickson,et al. Quantifying the benefit of a proteome reserve in fluctuating environments , 2017, Nature Communications.
[32] Jan Kok,et al. Bet-hedging during bacterial diauxic shift , 2014, Proceedings of the National Academy of Sciences.
[33] Baojun Wang,et al. Orthogonality and Burdens of Heterologous AND Gate Gene Circuits in E. coli , 2017, ACS synthetic biology.
[34] B. Hungate,et al. Predictive genomic traits for bacterial growth in culture versus actual growth in soil , 2019, The ISME Journal.
[35] Ke Chen,et al. Global Rebalancing of Cellular Resources by Pleiotropic Point Mutations Illustrates a Multi-scale Mechanism of Adaptive Evolution. , 2016, Cell systems.
[36] T. Hwa,et al. Growth-rate-dependent partitioning of RNA polymerases in bacteria , 2008, Proceedings of the National Academy of Sciences.
[37] F. Blattner,et al. Emergent Properties of Reduced-Genome Escherichia coli , 2006, Science.
[38] Matthias Heinemann,et al. Phenotypic bistability in Escherichia coli's central carbon metabolism , 2014, Molecular systems biology.
[39] Alfonso Jaramillo,et al. Empirical model and in vivo characterization of the bacterial response to synthetic gene expression show that ribosome allocation limits growth rate. , 2011, Biotechnology journal.
[40] P. Dennis,et al. Modulation of Chemical Composition and Other Parameters of the Cell at Different Exponential Growth Rates , 2008, EcoSal Plus.
[41] Andrea Y Weiße,et al. Growth Defects and Loss-of-Function in Synthetic Gene Circuits. , 2019, ACS synthetic biology.
[42] B. Levin,et al. A Numbers Game: Ribosome Densities, Bacterial Growth, and Antibiotic-Mediated Stasis and Death , 2017, mBio.
[43] Fuzhong Zhang,et al. Exploiting nongenetic cell-to-cell variation for enhanced biosynthesis. , 2016, Nature chemical biology.
[44] Sophia Hsin-Jung Li,et al. Escherichiacoli translation strategies differ across carbon, nitrogen and phosphorus limitation conditions , 2018, Nature Microbiology.
[45] G. Stan,et al. Burden-driven feedback control of gene expression , 2017, Nature Methods.
[46] Guy-Bart Stan,et al. Host-aware synthetic biology , 2019, Current Opinion in Systems Biology.
[47] T. Hwa,et al. Emergence of robust growth laws from optimal regulation of ribosome synthesis , 2014, Molecular systems biology.
[48] Domitilla Del Vecchio,et al. Resource Competition Shapes the Response of Genetic Circuits. , 2017, ACS synthetic biology.
[49] V. Danos,et al. Sources, propagation and consequences of stochasticity in cellular growth , 2018, Nature Communications.
[50] F. Taddei,et al. Temporal scaling of aging as an adaptive strategy of Escherichia coli , 2019, Science Advances.
[51] Sander K. Govers,et al. Nucleoid Size Scaling and Intracellular Organization of Translation across Bacteria , 2018, Cell.
[52] Johannes Geiselmann,et al. A synthetic growth switch based on controlled expression of RNA polymerase , 2015, Molecular systems biology.
[53] T. Hwa,et al. Interdependence of Cell Growth and Gene Expression: Origins and Consequences , 2010, Science.
[54] Xiongfeng Dai,et al. Growth suppression by altered (p)ppGpp levels results from non-optimal resource allocation in Escherichia coli , 2019, Nucleic acids research.
[55] Stefano Cardinale,et al. Effects of genetic variation on the E. coli host-circuit interface. , 2013, Cell reports.
[56] Stefan Klumpp,et al. A Model for Sigma Factor Competition in Bacterial Cells , 2014, PLoS Comput. Biol..
[57] L. You,et al. Emergent bistability by a growth-modulating positive feedback circuit. , 2009, Nature chemical biology.
[58] U. Alon,et al. Cost of unneeded proteins in E. coli is reduced after several generations in exponential growth. , 2010, Molecular cell.
[59] V. de Lorenzo,et al. The metabolic cost of flagellar motion in Pseudomonas putida KT2440. , 2014, Environmental microbiology.
[60] Howard J. Li,et al. Rapid and tunable post-translational coupling of genetic circuits , 2014, Nature.
[61] D. J. Kiviet,et al. Stochasticity of metabolism and growth at the single-cell level , 2014, Nature.
[62] G. Churchward,et al. Transcription in bacteria at different DNA concentrations , 1982, Journal of bacteriology.
[63] Sam P. Brown,et al. Division of Labor, Bet Hedging, and the Evolution of Mixed Biofilm Investment Strategies , 2017, mBio.
[64] V. Fromion,et al. Bacterial growth rate reflects a bottleneck in resource allocation. , 2011, Biochimica et biophysica acta.
[65] G. Stan,et al. Overloaded and stressed: whole-cell considerations for bacterial synthetic biology. , 2016, Current opinion in microbiology.
[66] R. Takors,et al. Genome reduction boosts heterologous gene expression in Pseudomonas putida , 2015, Microbial Cell Factories.
[67] Mary J Dunlop,et al. Controlling and exploiting cell-to-cell variation in metabolic engineering. , 2019, Current opinion in biotechnology.
[68] Christopher A. Voigt,et al. Genetic circuit performance under conditions relevant for industrial bioreactors. , 2012, ACS synthetic biology.
[69] Zsuzsanna Gyorfy,et al. Engineered ribosomal RNA operon copy-number variants of E. coli reveal the evolutionary trade-offs shaping rRNA operon number , 2015, Nucleic acids research.
[70] Xiaoyuan Wang,et al. Deletion of 76 genes relevant to flagella and pili formation to facilitate polyhydroxyalkanoate production in Pseudomonas putida , 2018, Applied Microbiology and Biotechnology.
[71] D. Richardson,et al. A bet-hedging strategy for denitrifying bacteria curtails their release of N2O , 2018, Proceedings of the National Academy of Sciences.