{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T16:47:55Z","timestamp":1769878075597,"version":"3.49.0"},"reference-count":102,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T00:00:00Z","timestamp":1632787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T00:00:00Z","timestamp":1632787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The rising consensus that the cell can dynamically allocate its resources provides an interesting angle for discovering the governing principles of cell growth and metabolism. Extensive efforts have been made in the past decade to elucidate the relationship between resource allocation and phenotypic patterns of microorganisms. Despite these exciting developments, there is still a lack of explicit comparison between potentially competing propositions and a lack of synthesis of inter-related proposals and findings.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this work, we have reviewed resource allocation-derived principles, hypotheses and mathematical models to recapitulate important achievements in this area. In particular, the emergence of resource allocation phenomena is deciphered by the putative tug of war between the cellular objectives, demands and the supply capability. Competing hypotheses for explaining the most-studied phenomenon arising from resource allocation, i.e. the overflow metabolism, have been re-examined towards uncovering the potential physiological root cause. The possible link between proteome fractions and the partition of the ribosomal machinery has been analysed through mathematical derivations. Finally, open questions are highlighted and an outlook on the practical applications is provided. It is the authors\u2019 intention that this review contributes to a clearer understanding of the role of resource allocation in resolving bacterial growth strategies, one of the central questions in microbiology.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>We have shown the importance of resource allocation in understanding various aspects of cellular systems. Several important questions such as the physiological root cause of overflow metabolism and the correct interpretation of \u2018protein costs\u2019 are shown to remain open. As the understanding of the mechanisms and utility of resource application in cellular systems further develops, we anticipate that mathematical modelling tools incorporating resource allocation will facilitate the circuit-host design in synthetic biology.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-04382-3","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T19:06:39Z","timestamp":1632855999000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis"],"prefix":"10.1186","volume":"22","author":[{"given":"Hong","family":"Zeng","sequence":"first","affiliation":[]},{"given":"Reza","family":"Rohani","sequence":"additional","affiliation":[]},{"given":"Wei E.","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5974-247X","authenticated-orcid":false,"given":"Aidong","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,28]]},"reference":[{"key":"4382_CR1","doi-asserted-by":"publisher","DOI":"10.1038\/msb.2009.82","author":"D Molenaar","year":"2009","unstructured":"Molenaar D, van Berlo R, de Ridder D, Teusink B. Shifts in growth strategies reflect tradeoffs in cellular economics. Mol Syst Biol. 2009. https:\/\/doi.org\/10.1038\/msb.2009.82.","journal-title":"Mol Syst Biol"},{"key":"4382_CR2","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1126\/science.1192588","volume":"330","author":"M Scott","year":"2010","unstructured":"Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T. Interdependence of cell growth and gene expression: origins and consequences. Science. 2010;330:1099\u2013102. https:\/\/doi.org\/10.1126\/science.1192588.","journal-title":"Science"},{"key":"4382_CR3","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1038\/nature12446","volume":"500","author":"C You","year":"2013","unstructured":"You C, Okano H, Hui S, Zhang Z, Kim M, Gunderson CW, et al. Coordination of bacterial proteome with metabolism by cyclic AMP signalling. Nature. 2013;500:301\u20136. https:\/\/doi.org\/10.1038\/nature12446.","journal-title":"Nature"},{"key":"4382_CR4","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1038\/nature15765","volume":"528","author":"M Basan","year":"2015","unstructured":"Basan M, Hui S, Okano H, Zhang Z, Shen Y, Williamson JR, et al. Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature. 2015;528:99\u2013104. https:\/\/doi.org\/10.1038\/nature15765.","journal-title":"Nature"},{"key":"4382_CR5","doi-asserted-by":"publisher","DOI":"10.15252\/msb.20145697","author":"S Hui","year":"2015","unstructured":"Hui S, Silverman JM, Chen SS, Erickson DW, Basan M, Wang J, et al. Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Mol Syst Biol. 2015. https:\/\/doi.org\/10.15252\/msb.20145697.","journal-title":"Mol Syst Biol"},{"key":"4382_CR6","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1038\/nature24299","volume":"551","author":"DW Erickson","year":"2017","unstructured":"Erickson DW, Schink SJ, Patsalo V, Williamson JR, Gerland U, Hwa T. A global resource allocation strategy governs growth transition kinetics of Escherichia coli. Nature. 2017;551:119. https:\/\/doi.org\/10.1038\/nature24299.","journal-title":"Nature"},{"key":"4382_CR7","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1042\/BST20160436","volume":"45","author":"A Goelzer","year":"2017","unstructured":"Goelzer A, Fromion V. Resource allocation in living organisms. Biochem Soc Trans. 2017;45:945\u201352.","journal-title":"Biochem Soc Trans"},{"key":"4382_CR8","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1016\/j.mib.2018.01.002","volume":"45","author":"L Yang","year":"2018","unstructured":"Yang L, Yurkovich JT, King ZA, Palsson BO. Modeling the multi-scale mechanisms of macromolecular resource allocation. Curr Opin Microbiol. 2018;45:8\u201315. https:\/\/doi.org\/10.1016\/j.mib.2018.01.002.","journal-title":"Curr Opin Microbiol"},{"key":"4382_CR9","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.mib.2018.02.008","volume":"45","author":"M Basan","year":"2018","unstructured":"Basan M. Resource allocation and metabolism: the search for governing principles. Curr Opin Microbiol. 2018;45:77\u201383. https:\/\/doi.org\/10.1016\/j.mib.2018.02.008.","journal-title":"Curr Opin Microbiol"},{"key":"4382_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s00018-019-03380-2","author":"DH de Groot","year":"2019","unstructured":"de Groot DH, Lischke J, Muolo R, Planqu\u00e9 R, Bruggeman FJ, Teusink B. The common message of constraint-based optimization approaches: overflow metabolism is caused by two growth-limiting constraints. Cell Mol Life Sci. 2019. https:\/\/doi.org\/10.1007\/s00018-019-03380-2.","journal-title":"Cell Mol Life Sci"},{"key":"4382_CR11","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1002\/(SICI)1097-0290(19981020)60:2<230::AID-BIT10>3.0.CO;2-Q","volume":"60","author":"J Pramanik","year":"1998","unstructured":"Pramanik J, Keasling JD. Effect of Escherichia coli biomass composition on central metabolic fluxes predicted by a stoichiometric model. Biotechnol Bioeng. 1998;60:230\u20138. https:\/\/doi.org\/10.1002\/(SICI)1097-0290(19981020)60:2%3c230::AID-BIT10%3e3.0.CO;2-Q.","journal-title":"Biotechnol Bioeng"},{"key":"4382_CR12","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1002\/bit.22802","volume":"107","author":"H Taymaz-Nikerel","year":"2010","unstructured":"Taymaz-Nikerel H, Borujeni AE, Verheijen PJT, Heijnen JJ, van Gulik WM. Genome-derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometry. Biotechnol Bioeng. 2010;107:369\u201381. https:\/\/doi.org\/10.1002\/bit.22802.","journal-title":"Biotechnol Bioeng"},{"key":"4382_CR13","volume-title":"Physiology of the bacterial cell","author":"FC Neidhardt","year":"1990","unstructured":"Neidhardt FC, Ingraham JL, Schaechter M. Physiology of the bacterial cell. Sunderland: Sinauer Associates; 1990."},{"key":"4382_CR14","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1016\/j.bbagen.2011.05.014","volume":"1810","author":"A Goelzer","year":"2011","unstructured":"Goelzer A, Fromion V. Bacterial growth rate reflects a bottleneck in resource allocation. Biochim Biophys Acta Gen Subj. 2011;1810:978\u201388.","journal-title":"Biochim Biophys Acta Gen Subj"},{"key":"4382_CR15","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1038\/s41467-020-14751-w","volume":"11","author":"H Dourado","year":"2020","unstructured":"Dourado H, Lercher MJ. An analytical theory of balanced cellular growth. Nat Commun. 2020;11:1226. https:\/\/doi.org\/10.1038\/s41467-020-14751-w.","journal-title":"Nat Commun"},{"key":"4382_CR16","doi-asserted-by":"publisher","first-page":"e1007559","DOI":"10.1371\/journal.pcbi.1007559","volume":"16","author":"DH de Groot","year":"2020","unstructured":"de Groot DH, Hulshof J, Teusink B, Bruggeman FJ, Planqu\u00e9 R. Elementary growth modes provide a molecular description of cellular self-fabrication. PLoS Comput Biol. 2020;16:e1007559. https:\/\/doi.org\/10.1371\/journal.pcbi.1007559.","journal-title":"PLoS Comput Biol"},{"key":"4382_CR17","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/1475-2859-4-14","volume":"4","author":"G Gosset","year":"2005","unstructured":"Gosset G. Improvement of Escherichia coli production strains by modification of the phosphoenolpyruvate:sugar phosphotransferase system. Microb Cell Fact. 2005;4:14. https:\/\/doi.org\/10.1186\/1475-2859-4-14.","journal-title":"Microb Cell Fact"},{"key":"4382_CR18","first-page":"172","volume":"5","author":"GL Rosano","year":"2014","unstructured":"Rosano GL, Ceccarelli EA. Recombinant protein expression in Escherichia coli: advances and challenges. Front Microbiol. 2014;5:172.","journal-title":"Front Microbiol"},{"key":"4382_CR19","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.biotechadv.2018.12.007","volume":"37","author":"K Shimizu","year":"2019","unstructured":"Shimizu K, Matsuoka Y. Regulation of glycolytic flux and overflow metabolism depending on the source of energy generation for energy demand. Biotechnol Adv. 2019;37:284\u2013305. https:\/\/doi.org\/10.1016\/j.biotechadv.2018.12.007.","journal-title":"Biotechnol Adv"},{"key":"4382_CR20","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1016\/j.copbio.2011.03.007","volume":"22","author":"VA Portnoy","year":"2011","unstructured":"Portnoy VA, Bezdan D, Zengler K. Adaptive laboratory evolution\u2014harnessing the power of biology for metabolic engineering. Curr Opin Biotechnol. 2011;22:590\u20134. https:\/\/doi.org\/10.1016\/j.copbio.2011.03.007.","journal-title":"Curr Opin Biotechnol"},{"key":"4382_CR21","doi-asserted-by":"publisher","first-page":"14123","DOI":"10.1038\/ncomms14123","volume":"8","author":"BD Towbin","year":"2017","unstructured":"Towbin BD, Korem Y, Bren A, Doron S, Sorek R, Alon U. Optimality and sub-optimality in a bacterial growth law. Nat Commun. 2017;8:14123. https:\/\/doi.org\/10.1038\/ncomms14123.","journal-title":"Nat Commun"},{"key":"4382_CR22","doi-asserted-by":"publisher","first-page":"882","DOI":"10.15252\/msb.20166998","volume":"12","author":"JL Radzikowski","year":"2016","unstructured":"Radzikowski JL, Vedelaar S, Siegel D, Ortega \u00c1D, Schmidt A, Heinemann M. Bacterial persistence is an active \u03c3S stress response to metabolic flux limitation. Mol Syst Biol. 2016;12:882. https:\/\/doi.org\/10.15252\/msb.20166998.","journal-title":"Mol Syst Biol"},{"key":"4382_CR23","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.mib.2017.09.002","volume":"39","author":"JH Yang","year":"2017","unstructured":"Yang JH, Bening SC, Collins JJ. Antibiotic efficacy\u2014context matters. Curr Opin Microbiol. 2017;39:73\u201380. https:\/\/doi.org\/10.1016\/j.mib.2017.09.002.","journal-title":"Curr Opin Microbiol"},{"key":"4382_CR24","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1038\/nbt.3418","volume":"34","author":"A Schmidt","year":"2016","unstructured":"Schmidt A, Kochanowski K, Vedelaar S, Ahrn\u00e9 E, Volkmer B, Callipo L, et al. The quantitative and condition-dependent Escherichia coli proteome. Nat Biotechnol. 2016;34:104. https:\/\/doi.org\/10.1038\/nbt.3418.","journal-title":"Nat Biotechnol"},{"key":"4382_CR25","doi-asserted-by":"publisher","first-page":"e1004998","DOI":"10.1371\/journal.pcbi.1004998","volume":"12","author":"EJ O\u2019Brien","year":"2016","unstructured":"O\u2019Brien EJ, Utrilla J, Palsson BO. Quantification and classification of E. coli proteome utilization and unused protein costs across environments. PLoS Comput Biol. 2016;12:e1004998. https:\/\/doi.org\/10.1371\/journal.pcbi.1004998.","journal-title":"PLoS Comput Biol"},{"key":"4382_CR26","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1038\/s41564-018-0199-2","volume":"3","author":"SH-J Li","year":"2018","unstructured":"Li SH-J, Li Z, Park JO, King CG, Rabinowitz JD, Wingreen NS, et al. Escherichia coli translation strategies differ across carbon, nitrogen and phosphorus limitation conditions. Nat Microbiol. 2018;3:939\u201347. https:\/\/doi.org\/10.1038\/s41564-018-0199-2.","journal-title":"Nat Microbiol"},{"key":"4382_CR27","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1038\/s41467-017-01242-8","volume":"8","author":"M Mori","year":"2017","unstructured":"Mori M, Schink S, Erickson DW, Gerland U, Hwa T. Quantifying the benefit of a proteome reserve in fluctuating environments. Nat Commun. 2017;8:1225. https:\/\/doi.org\/10.1038\/s41467-017-01242-8.","journal-title":"Nat Commun"},{"key":"4382_CR28","doi-asserted-by":"publisher","first-page":"2891","DOI":"10.1016\/j.celrep.2018.05.007","volume":"23","author":"Y Korem Kohanim","year":"2018","unstructured":"Korem Kohanim Y, Levi D, Jona G, Towbin BD, Bren A, Alon U. A bacterial growth law out of steady state. Cell Rep. 2018;23:2891\u2013900. https:\/\/doi.org\/10.1016\/j.celrep.2018.05.007.","journal-title":"Cell Rep"},{"key":"4382_CR29","doi-asserted-by":"publisher","first-page":"e1001764","DOI":"10.1371\/journal.pbio.1001764","volume":"12","author":"AM New","year":"2014","unstructured":"New AM, Cerulus B, Govers SK, Perez-Samper G, Zhu B, Boogmans S, et al. Different levels of catabolite repression optimize growth in stable and variable environments. PLoS Biol. 2014;12:e1001764. https:\/\/doi.org\/10.1371\/journal.pbio.1001764.","journal-title":"PLoS Biol"},{"key":"4382_CR30","volume-title":"Escherichia coli and Salmonella","author":"H Bremer","year":"1996","unstructured":"Bremer H, Dennis P. Escherichia coli and Salmonella. Washington, DC: ASM Press; 1996."},{"key":"4382_CR31","volume-title":"Biological regulation and development","author":"O Maal\u00f8e","year":"1979","unstructured":"Maal\u00f8e O. Biological regulation and development. New York: Plenum; 1979."},{"key":"4382_CR32","doi-asserted-by":"publisher","first-page":"15128","DOI":"10.1038\/ncomms15128","volume":"8","author":"OS Venturelli","year":"2017","unstructured":"Venturelli OS, Tei M, Bauer S, Chan LJG, Petzold CJ, Arkin AP. Programming mRNA decay to modulate synthetic circuit resource allocation. Nat Commun. 2017;8:15128. https:\/\/doi.org\/10.1038\/ncomms15128.","journal-title":"Nat Commun"},{"key":"4382_CR33","doi-asserted-by":"publisher","first-page":"742","DOI":"10.15252\/msb.20145299","volume":"10","author":"TH Segall-Shapiro","year":"2014","unstructured":"Segall-Shapiro TH, Meyer AJ, Ellington AD, Sontag ED, Voigt CA. A \u2018resource allocator\u2019 for transcription based on a highly fragmented T7 RNA polymerase. Mol Syst Biol. 2014;10:742. https:\/\/doi.org\/10.15252\/msb.20145299.","journal-title":"Mol Syst Biol"},{"key":"4382_CR34","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1186\/1752-0509-7-138","volume":"7","author":"Y Zhou","year":"2013","unstructured":"Zhou Y, Vazquez A, Wise A, Warita T, Warita K, Bar-Joseph Z, et al. Carbon catabolite repression correlates with the maintenance of near invariant molecular crowding in proliferating E. coli cells. BMC Syst Biol. 2013;7:138. https:\/\/doi.org\/10.1186\/1752-0509-7-138.","journal-title":"BMC Syst Biol"},{"key":"4382_CR35","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.cels.2017.06.005","volume":"5","author":"M Szenk","year":"2017","unstructured":"Szenk M, Dill KA, de Graff AMR. Why do fast-growing bacteria enter overflow metabolism? Testing the membrane real estate hypothesis. Cell Syst. 2017;5:95\u2013104. https:\/\/doi.org\/10.1016\/j.cels.2017.06.005.","journal-title":"Cell Syst"},{"key":"4382_CR36","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1038\/nchembio.975","volume":"8","author":"CM Agapakis","year":"2012","unstructured":"Agapakis CM, Boyle PM, Silver PA. Natural strategies for the spatial optimization of metabolism in synthetic biology. Nat Chem Biol. 2012;8:527\u201335. https:\/\/doi.org\/10.1038\/nchembio.975.","journal-title":"Nat Chem Biol"},{"key":"4382_CR37","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.1038\/s41467-019-09261-3","volume":"10","author":"X Wang","year":"2019","unstructured":"Wang X, Xia K, Yang X, Tang C. Growth strategy of microbes on mixed carbon sources. Nat Commun. 2019;10:1279. https:\/\/doi.org\/10.1038\/s41467-019-09261-3.","journal-title":"Nat Commun"},{"key":"4382_CR38","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1038\/nrmicro1932","volume":"6","author":"B G\u00f6rke","year":"2008","unstructured":"G\u00f6rke B, St\u00fclke J. Carbon catabolite repression in bacteria: many ways to make the most out of nutrients. Nat Rev Microbiol. 2008;6:613\u201324. https:\/\/doi.org\/10.1038\/nrmicro1932.","journal-title":"Nat Rev Microbiol"},{"key":"4382_CR39","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1099\/00221287-44-2-149","volume":"44","author":"RH De Deken","year":"1966","unstructured":"De Deken RH. The crabtree effect: a regulatory system in yeast. Microbiology. 1966;44:149\u201356. https:\/\/doi.org\/10.1099\/00221287-44-2-149.","journal-title":"Microbiology"},{"key":"4382_CR40","doi-asserted-by":"publisher","DOI":"10.1093\/femsyr\/foz070","author":"R Yu","year":"2019","unstructured":"Yu R, Nielsen J. Big data in yeast systems biology. FEMS Yeast Res. 2019. https:\/\/doi.org\/10.1093\/femsyr\/foz070.","journal-title":"FEMS Yeast Res"},{"key":"4382_CR41","doi-asserted-by":"publisher","first-page":"17592","DOI":"10.1073\/pnas.1906569116","volume":"116","author":"Y Chen","year":"2019","unstructured":"Chen Y, Nielsen J. Energy metabolism controls phenotypes by protein efficiency and allocation. Proc Natl Acad Sci. 2019;116:17592\u20137. https:\/\/doi.org\/10.1073\/pnas.1906569116.","journal-title":"Proc Natl Acad Sci"},{"key":"4382_CR42","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1126\/science.1160809","volume":"324","author":"MG Vander Heiden","year":"2009","unstructured":"Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324:1029\u201333. https:\/\/doi.org\/10.1126\/science.1160809.","journal-title":"Science"},{"key":"4382_CR43","doi-asserted-by":"publisher","first-page":"e1002018","DOI":"10.1371\/journal.pcbi.1002018","volume":"7","author":"T Shlomi","year":"2011","unstructured":"Shlomi T, Benyamini T, Gottlieb E, Sharan R, Ruppin E. Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warburg effect. PLoS Comput Biol. 2011;7:e1002018. https:\/\/doi.org\/10.1371\/journal.pcbi.1002018.","journal-title":"PLoS Comput Biol"},{"key":"4382_CR44","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1080\/15384101.2018.1442622","volume":"17","author":"E de Alteriis","year":"2018","unstructured":"de Alteriis E, Carten\u00ec F, Parascandola P, Serpa J, Mazzoleni S. Revisiting the Crabtree\/Warburg effect in a dynamic perspective: a fitness advantage against sugar-induced cell death. Cell Cycle. 2018;17:688\u2013701. https:\/\/doi.org\/10.1080\/15384101.2018.1442622.","journal-title":"Cell Cycle"},{"key":"4382_CR45","doi-asserted-by":"publisher","DOI":"10.1186\/1752-0509-2-7","author":"A Vazquez","year":"2008","unstructured":"Vazquez A, Beg QK, Demenezes MA, Ernst J, Bar-Joseph Z, Barabasi AL, et al. Impact of the solvent capacity constraint on E. coli metabolism. BMC Syst Biol. 2008. https:\/\/doi.org\/10.1186\/1752-0509-2-7.","journal-title":"BMC Syst Biol"},{"key":"4382_CR46","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0609845104","author":"QK Beg","year":"2007","unstructured":"Beg QK, Vazquez A, Ernst J, de Menezes MA, Bar-Joseph Z, Barabasi AL, et al. Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc Natl Acad Sci USA. 2007. https:\/\/doi.org\/10.1073\/pnas.0609845104.","journal-title":"Proc Natl Acad Sci USA"},{"key":"4382_CR47","doi-asserted-by":"publisher","DOI":"10.1038\/msb.2011.34","author":"K Zhuang","year":"2011","unstructured":"Zhuang K, Vemuri GN, Mahadevan R. Economics of membrane occupancy and respiro-fermentation. Mol Syst Biol. 2011. https:\/\/doi.org\/10.1038\/msb.2011.34.","journal-title":"Mol Syst Biol"},{"key":"4382_CR48","doi-asserted-by":"publisher","first-page":"31007","DOI":"10.1038\/srep31007","volume":"6","author":"A Vazquez","year":"2016","unstructured":"Vazquez A, Oltvai ZN. Macromolecular crowding explains overflow metabolism in cells. Sci Rep. 2016;6:31007. https:\/\/doi.org\/10.1038\/srep31007.","journal-title":"Sci Rep"},{"key":"4382_CR49","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1128\/jb.148.1.58-63.1981","volume":"148","author":"CL Woldringh","year":"1981","unstructured":"Woldringh CL, Binnerts JS, Mans A. Variation in Escherichia coli buoyant density measured in Percoll gradients. J Bacteriol. 1981;148:58\u201363.","journal-title":"J Bacteriol"},{"key":"4382_CR50","doi-asserted-by":"publisher","first-page":"836","DOI":"10.15252\/msb.20156178","volume":"11","author":"M Basan","year":"2015","unstructured":"Basan M, Zhu M, Dai X, Warren M, S\u00e9vin D, Wang Y-P, et al. Inflating bacterial cells by increased protein synthesis. Mol Syst Biol. 2015;11:836. https:\/\/doi.org\/10.15252\/msb.20156178.","journal-title":"Mol Syst Biol"},{"key":"4382_CR51","doi-asserted-by":"publisher","first-page":"1210","DOI":"10.1016\/j.automatica.2011.02.038","volume":"47","author":"A Goelzer","year":"2011","unstructured":"Goelzer A, Fromion V, Scorletti G. Cell design in bacteria as a convex optimization problem. Automatica. 2011;47:1210\u20138.","journal-title":"Automatica"},{"key":"4382_CR52","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.ymben.2015.10.003","volume":"32","author":"A Goelzer","year":"2015","unstructured":"Goelzer A, Muntel J, Chubukov V, Jules M, Prestel E, N\u00f6lker R, et al. Quantitative prediction of genome-wide resource allocation in bacteria. Metab Eng. 2015;32:232\u201343. https:\/\/doi.org\/10.1016\/j.ymben.2015.10.003.","journal-title":"Metab Eng"},{"key":"4382_CR53","doi-asserted-by":"publisher","first-page":"e45635","DOI":"10.1371\/journal.pone.0045635","volume":"7","author":"I Thiele","year":"2012","unstructured":"Thiele I, Fleming RMT, Que R, Bordbar A, Diep D, Palsson BO. Multiscale modeling of metabolism and macromolecular synthesis in E. coli and its application to the evolution of codon usage. PLoS ONE. 2012;7:e45635. https:\/\/doi.org\/10.1371\/journal.pone.0045635.","journal-title":"PLoS ONE"},{"key":"4382_CR54","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1038\/msb.2013.52","volume":"9","author":"EJ O\u2019Brien","year":"2013","unstructured":"O\u2019Brien EJ, Lerman JA, Chang RL, Hyduke DR, Palsson BO. Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol. 2013;9:693\u2013693. https:\/\/doi.org\/10.1038\/msb.2013.52.","journal-title":"Mol Syst Biol"},{"key":"4382_CR55","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1073\/pnas.1421138111","volume":"112","author":"A Maitra","year":"2015","unstructured":"Maitra A, Dill KA. Bacterial growth laws reflect the evolutionary importance of energy efficiency. Proc Natl Acad Sci. 2015;112:406\u201311.","journal-title":"Proc Natl Acad Sci"},{"key":"4382_CR56","doi-asserted-by":"publisher","unstructured":"Chen T, He HL, Church GM. Modeling gene expression with differential equations. In: Proceedings of pacific symposium on biocomputing (PSB\u201999). Singapore: World Scientific; 1998. p. 29\u201340. https:\/\/doi.org\/10.1142\/9789814447300_0004.","DOI":"10.1142\/9789814447300_0004"},{"key":"4382_CR57","doi-asserted-by":"publisher","first-page":"2850","DOI":"10.1039\/C4MB00358F","volume":"10","author":"K Tchourine","year":"2014","unstructured":"Tchourine K, Poultney CS, Wang L, Silva GM, Manohar S, Mueller CL, et al. One third of dynamic protein expression profiles can be predicted by a simple rate equation. Mol BioSyst. 2014;10:2850\u201362. https:\/\/doi.org\/10.1039\/C4MB00358F.","journal-title":"Mol BioSyst"},{"key":"4382_CR58","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/BF01923511","volume":"48","author":"MR Maurizi","year":"1992","unstructured":"Maurizi MR. Proteases and protein degradation in Escherichia coli. Experientia. 1992;48:178\u2013201. https:\/\/doi.org\/10.1007\/BF01923511.","journal-title":"Experientia"},{"key":"4382_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1000606","volume":"5","author":"C Dressaire","year":"2009","unstructured":"Dressaire C, Gitton C, Loubi\u00e8re P, Monnet V, Queinnec I, Cocaign-Bousquet M. Transcriptome and proteome exploration to model translation efficiency and protein stability in Lactococcus lactis. PLOS Comput Biol. 2009;5:1\u201312. https:\/\/doi.org\/10.1371\/journal.pcbi.1000606.","journal-title":"PLOS Comput Biol"},{"key":"4382_CR60","doi-asserted-by":"publisher","DOI":"10.1101\/2020.11.19.390583","author":"F N\u00f3bel","year":"2020","unstructured":"N\u00f3bel F, Pic\u00f3 J. Resources allocation explains the differential roles of RBS and promoter strengths in cell mass distribution and optimal protein expression productivity. bioRxiv. 2020. https:\/\/doi.org\/10.1101\/2020.11.19.390583.","journal-title":"bioRxiv"},{"key":"4382_CR61","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/nbt.1614","volume":"28","author":"JD Orth","year":"2010","unstructured":"Orth JD, Thiele I, Palsson B\u00d8. What is flux balance analysis? Nat Biotechnol. 2010;28:245\u20138. https:\/\/doi.org\/10.1038\/nbt.1614.","journal-title":"Nat Biotechnol"},{"key":"4382_CR62","doi-asserted-by":"publisher","first-page":"e1004913","DOI":"10.1371\/journal.pcbi.1004913","volume":"12","author":"M Mori","year":"2016","unstructured":"Mori M, Hwa T, Martin OC, De Martino A, Marinari E. Constrained allocation flux balance analysis. PLoS Comput Biol. 2016;12:e1004913.","journal-title":"PLoS Comput Biol"},{"key":"4382_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12918-018-0677-4","volume":"13","author":"H Zeng","year":"2019","unstructured":"Zeng H, Yang A. Modelling overflow metabolism in Escherichia coli with flux balance analysis incorporating differential proteomic efficiencies of energy pathways. BMC Syst Biol. 2019;13:1\u201318.","journal-title":"BMC Syst Biol"},{"key":"4382_CR64","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1002\/bit.26943","volume":"116","author":"H Zeng","year":"2019","unstructured":"Zeng H, Yang A. Quantification of proteomic and metabolic burdens predicts growth retardation and overflow metabolism in recombinant Escherichia coli. Biotechnol Bioeng. 2019;116:1484\u201395.","journal-title":"Biotechnol Bioeng"},{"key":"4382_CR65","doi-asserted-by":"publisher","first-page":"D764","DOI":"10.1093\/nar\/gks1049","volume":"41","author":"I Schomburg","year":"2012","unstructured":"Schomburg I, Chang A, Placzek S, S\u00f6hngen C, Rother M, Lang M, et al. BRENDA in 2013: integrated reactions, kinetic data, enzyme function data, improved disease classification\u2014new options and contents in BRENDA. Nucleic Acids Res. 2012;41:D764\u201372. https:\/\/doi.org\/10.1093\/nar\/gks1049.","journal-title":"Nucleic Acids Res"},{"key":"4382_CR66","doi-asserted-by":"publisher","first-page":"935","DOI":"10.15252\/msb.20167411","volume":"13","author":"BJ S\u00e1nchez","year":"2017","unstructured":"S\u00e1nchez BJ, Zhang C, Nilsson A, Lahtvee P-J, Kerkhoven EJ, Nielsen J. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol Syst Biol. 2017;13:935.","journal-title":"Mol Syst Biol"},{"key":"4382_CR67","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.cell.2012.05.044","volume":"150","author":"JR Karr","year":"2012","unstructured":"Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B, et al. A whole-cell computational model predicts phenotype from genotype. Cell. 2012;150:389\u2013401. https:\/\/doi.org\/10.1016\/j.cell.2012.05.044.","journal-title":"Cell"},{"key":"4382_CR68","doi-asserted-by":"publisher","first-page":"10810","DOI":"10.1073\/pnas.1501384112","volume":"112","author":"L Yang","year":"2015","unstructured":"Yang L, Tan J, O\u2019Brien EJ, Monk JM, Kim D, Li HJ, et al. Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data. Proc Natl Acad Sci. 2015;112:10810\u20135. https:\/\/doi.org\/10.1073\/pnas.1501384112.","journal-title":"Proc Natl Acad Sci"},{"key":"4382_CR69","doi-asserted-by":"publisher","first-page":"e1005939","DOI":"10.1371\/journal.ppat.1005939","volume":"12","author":"R Peyraud","year":"2016","unstructured":"Peyraud R, Cottret L, Marmiesse L, Gouzy J, Genin S. A resource allocation trade-off between virulence and proliferation drives metabolic versatility in the plant pathogen Ralstonia solanacearum. PLoS Pathog. 2016;12:e1005939. https:\/\/doi.org\/10.1371\/journal.ppat.1005939.","journal-title":"PLoS Pathog"},{"key":"4382_CR70","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.jtbi.2014.10.035","volume":"365","author":"S Waldherr","year":"2015","unstructured":"Waldherr S, Oyarz\u00fan DA, Bockmayr A. Dynamic optimization of metabolic networks coupled with gene expression. J Theor Biol. 2015;365:469\u201385. https:\/\/doi.org\/10.1016\/j.jtbi.2014.10.035.","journal-title":"J Theor Biol"},{"key":"4382_CR71","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1016\/S0006-3495(02)73903-9","volume":"83","author":"R Mahadevan","year":"2002","unstructured":"Mahadevan R, Edwards JS, Doyle FJ. Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J. 2002;83:1331\u201340. https:\/\/doi.org\/10.1016\/S0006-3495(02)73903-9.","journal-title":"Biophys J"},{"key":"4382_CR72","doi-asserted-by":"publisher","DOI":"10.1038\/73786","author":"S Schuster","year":"2000","unstructured":"Schuster S, Fell DA, Dandekar T. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nat Biotechnol. 2000. https:\/\/doi.org\/10.1038\/73786.","journal-title":"Nat Biotechnol"},{"key":"4382_CR73","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm082","author":"RP Carlson","year":"2007","unstructured":"Carlson RP. Metabolic systems cost-benefit analysis for interpreting network structure and regulation. Bioinformatics. 2007. https:\/\/doi.org\/10.1093\/bioinformatics\/btm082.","journal-title":"Bioinformatics"},{"key":"4382_CR74","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1006010","volume":"14","author":"MT Wortel","year":"2018","unstructured":"Wortel MT, Noor E, Ferris M, Bruggeman FJ, Liebermeister W. Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield. PLoS Comput Biol. 2018;14:1\u201321. https:\/\/doi.org\/10.1371\/journal.pcbi.1006010.","journal-title":"PLoS Comput Biol"},{"key":"4382_CR75","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006858","volume":"15","author":"DH de Groot","year":"2019","unstructured":"de Groot DH, van Boxtel C, Planqu\u00e9 R, Bruggeman FJ, Teusink B. The number of active metabolic pathways is bounded by the number of cellular constraints at maximal metabolic rates. PLoS Comput Biol. 2019;15: e1006858. https:\/\/doi.org\/10.1371\/journal.pcbi.1006858.","journal-title":"PLoS Comput Biol"},{"key":"4382_CR76","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1038\/ncomms1928","volume":"3","author":"JA Lerman","year":"2012","unstructured":"Lerman JA, Hyduke DR, Latif H, Portnoy VA, Lewis NE, Orth JD, et al. In silico method for modelling metabolism and gene product expression at genome scale. Nat Commun. 2012;3:929.","journal-title":"Nat Commun"},{"key":"4382_CR77","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1126\/science.1219083","volume":"336","author":"M Eames","year":"2012","unstructured":"Eames M, Kortemme T. Cost-benefit tradeoffs in engineered lac operons. Science (80- ). 2012;336:911\u20135. https:\/\/doi.org\/10.1126\/science.1219083.","journal-title":"Science (80- )"},{"key":"4382_CR78","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1038\/s41540-019-0093-4","volume":"5","author":"M Mori","year":"2019","unstructured":"Mori M, Marinari E, De Martino A. A yield-cost tradeoff governs Escherichia coli\u2019s decision between fermentation and respiration in carbon-limited growth. npj Syst Biol Appl. 2019;5:16. https:\/\/doi.org\/10.1038\/s41540-019-0093-4.","journal-title":"npj Syst Biol Appl"},{"key":"4382_CR79","doi-asserted-by":"publisher","first-page":"e1007066","DOI":"10.1371\/journal.pcbi.1007066","volume":"15","author":"C Cheng","year":"2019","unstructured":"Cheng C, O\u2019Brien EJ, McCloskey D, Utrilla J, Olson C, LaCroix RA, et al. Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism. PLoS Comput Biol. 2019;15:e1007066. https:\/\/doi.org\/10.1371\/journal.pcbi.1007066.","journal-title":"PLoS Comput Biol"},{"key":"4382_CR80","doi-asserted-by":"publisher","first-page":"E6457","DOI":"10.1073\/pnas.1617508114","volume":"114","author":"A-M Reimers","year":"2017","unstructured":"Reimers A-M, Knoop H, Bockmayr A, Steuer R. Cellular trade-offs and optimal resource allocation during cyanobacterial diurnal growth. Proc Natl Acad Sci. 2017;114:E6457\u201365. https:\/\/doi.org\/10.1073\/pnas.1617508114.","journal-title":"Proc Natl Acad Sci"},{"key":"4382_CR81","doi-asserted-by":"publisher","first-page":"3354","DOI":"10.1038\/s41467-019-11331-5","volume":"10","author":"M Zampieri","year":"2019","unstructured":"Zampieri M, H\u00f6rl M, Hotz F, M\u00fcller NF, Sauer U. Regulatory mechanisms underlying coordination of amino acid and glucose catabolism in Escherichia coli. Nat Commun. 2019;10:3354. https:\/\/doi.org\/10.1038\/s41467-019-11331-5.","journal-title":"Nat Commun"},{"key":"4382_CR82","doi-asserted-by":"publisher","DOI":"10.1128\/ecosalplus.10.2.1","author":"JD Orth","year":"2010","unstructured":"Orth JD, Palsson B\u00d8, Fleming RMT. Reconstruction and use of microbial metabolic networks: the core Escherichia coli metabolic model as an educational guide. EcoSal Plus. 2010. https:\/\/doi.org\/10.1128\/ecosalplus.10.2.1.","journal-title":"EcoSal Plus"},{"key":"4382_CR83","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1046\/j.1365-2958.1998.00941.x","volume":"29","author":"G Sawers","year":"1998","unstructured":"Sawers G, Watson G. A glycyl radical solution: oxygen-dependent interconversion of pyruvate formate-lyase. Mol Microbiol. 1998;29:945\u201354. https:\/\/doi.org\/10.1046\/j.1365-2958.1998.00941.x.","journal-title":"Mol Microbiol"},{"key":"4382_CR84","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.ymben.2016.12.004","volume":"39","author":"ZA King","year":"2017","unstructured":"King ZA, O\u2019Brien EJ, Feist AM, Palsson BO. Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion. Metab Eng. 2017;39:220\u20137. https:\/\/doi.org\/10.1016\/j.ymben.2016.12.004.","journal-title":"Metab Eng"},{"key":"4382_CR85","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.mib.2016.07.009","volume":"33","author":"O Borkowski","year":"2016","unstructured":"Borkowski O, Ceroni F, Stan G-B, Ellis T. Overloaded and stressed: whole-cell considerations for bacterial synthetic biology. Curr Opin Microbiol. 2016;33:123\u201330. https:\/\/doi.org\/10.1016\/j.mib.2016.07.009.","journal-title":"Curr Opin Microbiol"},{"key":"4382_CR86","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.coisb.2019.03.001","volume":"14","author":"A Boo","year":"2019","unstructured":"Boo A, Ellis T, Stan G-B. Host-aware synthetic biology. Curr Opin Syst Biol. 2019;14:66\u201372. https:\/\/doi.org\/10.1016\/j.coisb.2019.03.001.","journal-title":"Curr Opin Syst Biol"},{"key":"4382_CR87","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1002\/biot.201200085","volume":"7","author":"S Cardinale","year":"2012","unstructured":"Cardinale S, Arkin AP. Contextualizing context for synthetic biology: identifying causes of failure of synthetic biological systems. Biotechnol J. 2012;7:856\u201366. https:\/\/doi.org\/10.1002\/biot.201200085.","journal-title":"Biotechnol J"},{"key":"4382_CR88","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1021\/acssynbio.8b00531","volume":"8","author":"E-M Nikolados","year":"2019","unstructured":"Nikolados E-M, Wei\u00dfe AY, Ceroni F, Oyarz\u00fan DA. Growth defects and loss-of-function in synthetic gene circuits. ACS Synth Biol. 2019;8:1231\u201340. https:\/\/doi.org\/10.1021\/acssynbio.8b00531.","journal-title":"ACS Synth Biol"},{"key":"4382_CR89","doi-asserted-by":"publisher","first-page":"2503","DOI":"10.1038\/ncomms3503","volume":"4","author":"CNS Santos","year":"2013","unstructured":"Santos CNS, Regitsky DD, Yoshikuni Y. Implementation of stable and complex biological systems through recombinase-assisted genome engineering. Nat Commun. 2013;4:2503. https:\/\/doi.org\/10.1038\/ncomms3503.","journal-title":"Nat Commun"},{"key":"4382_CR90","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1038\/nmeth.2926","volume":"11","author":"JAN Brophy","year":"2014","unstructured":"Brophy JAN, Voigt CA. Principles of genetic circuit design. Nat Methods. 2014;11:508\u201320. https:\/\/doi.org\/10.1038\/nmeth.2926.","journal-title":"Nat Methods"},{"key":"4382_CR91","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1038\/nmeth.3339","volume":"12","author":"F Ceroni","year":"2015","unstructured":"Ceroni F, Algar R, Stan G-B, Ellis T. Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat Methods. 2015;12:415\u20138. https:\/\/doi.org\/10.1038\/nmeth.3339.","journal-title":"Nat Methods"},{"key":"4382_CR92","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1038\/ng1555","volume":"37","author":"E Fischer","year":"2005","unstructured":"Fischer E, Sauer U. Large-scale in vivo flux analysis shows rigidity and suboptimal performance of Bacillus subtilis metabolism. Nat Genet. 2005;37:636\u201340. https:\/\/doi.org\/10.1038\/ng1555.","journal-title":"Nat Genet"},{"key":"4382_CR93","doi-asserted-by":"publisher","first-page":"1008","DOI":"10.1074\/mcp.M113.032631","volume":"13","author":"J Muntel","year":"2014","unstructured":"Muntel J, Fromion V, Goelzer A, Maa\u03b2 S, M\u00e4der U, B\u00fcttner K, et al. Comprehensive absolute quantification of the cytosolic proteome of bacillus subtilis by data independent, parallel fragmentation in liquid chromatography\/mass spectrometry (LC\/MSE). Mol Cell Proteom. 2014;13:1008\u201319. https:\/\/doi.org\/10.1074\/mcp.M113.032631.","journal-title":"Mol Cell Proteom"},{"key":"4382_CR94","doi-asserted-by":"publisher","first-page":"2559","DOI":"10.1111\/evo.12468","volume":"68","author":"G D\u2019Souza","year":"2014","unstructured":"D\u2019Souza G, Waschina S, Pande S, Bohl K, Kaleta C, Kost C. Less is more: selective advantages can explain the prevalent loss of biosynthetic genes in bacteria. Evolution (N Y). 2014;68:2559\u201370. https:\/\/doi.org\/10.1111\/evo.12468.","journal-title":"Evolution (N Y)"},{"key":"4382_CR95","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1038\/s41589-020-0593-y","volume":"16","author":"G Lastiri-Pancardo","year":"2020","unstructured":"Lastiri-Pancardo G, Mercado-Hern\u00e1ndez JS, Kim J, Jim\u00e9nez JI, Utrilla J. A quantitative method for proteome reallocation using minimal regulatory interventions. Nat Chem Biol. 2020;16:1026\u201333. https:\/\/doi.org\/10.1038\/s41589-020-0593-y.","journal-title":"Nat Chem Biol"},{"key":"4382_CR96","doi-asserted-by":"crossref","unstructured":"Nikolados E-M, Wei\u00dfe AY, Oyarz\u00fan DA. Prediction of cellular burden with host-circuit models. arXiv e-prints. 2020. arXiv:2004.00995.","DOI":"10.1007\/978-1-0716-1032-9_13"},{"key":"4382_CR97","doi-asserted-by":"publisher","unstructured":"Wei\u00dfe AY, Oyarz\u00fan DA, Danos V, Swain PS. Mechanistic links between cellular trade-offs, gene expression, and growth. Proc Natl Acad Sci. 2015;112:E1038 LP-E1047. doi:https:\/\/doi.org\/10.1073\/pnas.1416533112.","DOI":"10.1073\/pnas.1416533112"},{"key":"4382_CR98","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1038\/s41564-017-0022-5","volume":"2","author":"C Liao","year":"2017","unstructured":"Liao C, Blanchard AE, Lu T. An integrative circuit\u2013host modelling framework for predicting synthetic gene network behaviours. Nat Microbiol. 2017;2:1658\u201366. https:\/\/doi.org\/10.1038\/s41564-017-0022-5.","journal-title":"Nat Microbiol"},{"key":"4382_CR99","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1038\/nchembio.2554","volume":"14","author":"CC Liu","year":"2018","unstructured":"Liu CC, Jewett MC, Chin JW, Voigt CA. Toward an orthogonal central dogma. Nat Chem Biol. 2018;14:103\u20136. https:\/\/doi.org\/10.1038\/nchembio.2554.","journal-title":"Nat Chem Biol"},{"key":"4382_CR100","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1021\/sb500299c","volume":"4","author":"AJ Meyer","year":"2015","unstructured":"Meyer AJ, Ellefson JW, Ellington AD. Directed evolution of a panel of orthogonal T7 RNA polymerase variants for in vivo or in vitro synthetic circuitry. ACS Synth Biol. 2015;4:1070\u20136. https:\/\/doi.org\/10.1021\/sb500299c.","journal-title":"ACS Synth Biol"},{"key":"4382_CR101","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1038\/nbt.3053","volume":"32","author":"DE Cameron","year":"2014","unstructured":"Cameron DE, Collins JJ. Tunable protein degradation in bacteria. Nat Biotechnol. 2014;32:1276\u201381. https:\/\/doi.org\/10.1038\/nbt.3053.","journal-title":"Nat Biotechnol"},{"key":"4382_CR102","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1038\/s41467-018-02898-6","volume":"9","author":"APS Darlington","year":"2018","unstructured":"Darlington APS, Kim J, Jim\u00e9nez JI, Bates DG. Dynamic allocation of orthogonal ribosomes facilitates uncoupling of co-expressed genes. Nat Commun. 2018;9:695. https:\/\/doi.org\/10.1038\/s41467-018-02898-6.","journal-title":"Nat Commun"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04382-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-021-04382-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04382-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T19:08:27Z","timestamp":1632856107000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-021-04382-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,28]]},"references-count":102,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["4382"],"URL":"https:\/\/doi.org\/10.1186\/s12859-021-04382-3","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,28]]},"assertion":[{"value":"2 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"467"}}