Abstract
Most natural organisms are generalists, as they deploy cellular resources for growth and survival under changing environments. Minimal cells are thought to be specialists; therefore, they should display specialized behaviors for very specific functions. Depending on the required function to display, the cellular resources should be differentially allocated, generating an optimal resource use that maximizes its designed function. Recently, many studies have focused on the economy of cellular resource allocation in different environments. With several tools and approaches, resource allocation has been extensively studied in natural and engineered cellular systems. These approaches have generated genome-scale models, coarse-grained models, and growth laws that may be used in minimal cell design. In this chapter, we will review the recent advances in econometric approaches to study and engineer resource allocation. We will propose design principles for cell minimization focusing on the cellular resource allocation framework to maximize the functions that they are designed to display.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aiyar SE, Gaal T, Gourse RL (2002) rRNA promoter activity in the fast-growing bacterium Vibrio natriegens. J Bacteriol 184:1349–1358
Artsimovitch I, Patlan V, Sekine S et al (2004) Structural basis for transcription regulation by alarmone ppGpp. Cell 117:299–310. https://doi.org/10.1016/S0092-8674(04)00401-5
Bachmann BJ (1990) Linkage map of Escherichia coli K-12, edition 8. Microbiol Rev 54:130–197
Baracchini E, Bremer H (1988) Stringent and growth control of rRNA synthesis in Escherichia coli are both mediated by ppGpp. J Biol Chem 263:2597–2602
Barenholz U, Keren L, Segal E, Milo R (2016) A minimalistic resource allocation model to explain ubiquitous increase in protein expression with growth rate. PLoS One 11:e0153344. https://doi.org/10.1371/journal.pone.0153344
Basan M, Hui S, Okano H et al (2015) Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528:99–104. https://doi.org/10.1038/nature15765
Bienick MS, Young KW, Klesmith JR et al (2014) The interrelationship between promoter strength, gene expression, and growth rate. PLoS One 9:e109105. https://doi.org/10.1371/journal.pone.0109105
Ceroni F, Algar R, Stan G-B, Ellis T (2015) Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat Methods 12:415–418. https://doi.org/10.1038/nmeth.3339
Ceroni F, Boo A, Furini S et al (2018) Burden-driven feedback control of gene expression. Nat Methods 15:387–393. https://doi.org/10.1038/nmeth.4635
Condon C, French S, Squires C, Squires CL (1993) Depletion of functional ribosomal RNA operons in Escherichia coli causes increased expression of the remaining intact copies. EMBO J 12:4305–4315
Condon C, Liveris D, Squires C et al (1995) rRNA operon multiplicity in Escherichia coli and the physiological implications of rrn inactivation. J Bacteriol 177:4152–4156. https://doi.org/10.1128/JB.177.14.4152-4156.1995
Dai X, Zhu M, Warren M et al (2016) Reduction of translating ribosomes enables Escherichia coli to maintain elongation rates during slow growth. Nat Microbiol 2:16231. https://doi.org/10.1038/nmicrobiol.2016.231
de Jong H, Geiselmann J, Ropers D (2017) Resource reallocation in bacteria by reengineering the gene expression machinery. Trends Microbiol 25:480–493
Dennis PP, Bremer H (2008) Modulation of chemical composition and other parameters of the cell at different exponential growth rates. EcoSal Plus. https://doi.org/10.1128/ecosal.5.2.3
Deutschbauer A, Price MN, Wetmore KM et al (2014) Towards an informative mutant phenotype for every bacterial gene. J Bacteriol 196:3643–3655. https://doi.org/10.1128/JB.01836-14
Dragosits M, Mattanovich D (2013) Adaptive laboratory evolution – principles and applications for biotechnology. Microb Cell Factories 12:64. https://doi.org/10.1186/1475-2859-12-64
Ebrahim A, Brunk E, Tan J et al (2016) Multi-omic data integration enables discovery of hidden biological regularities. Nat Commun 7:13091. https://doi.org/10.1038/ncomms13091
Edwards J, Ibarra R, Palsson B (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol:125–130
Elowitz M, Leibler S (2000) A synthetic oscillatory netwrok of transcriptional regulators. Nature 403:335–338
Frumkin I, Schirman D, Rotman A et al (2017) Gene architectures that minimize cost of gene expression. Mol Cell 65:142–153. https://doi.org/10.1016/j.molcel.2016.11.007
Glass JI, Merryman C, Wise KS et al (2017) Minimal cells-real and imagined. Cold Spring Harb Perspect Biol 9:a023861. https://doi.org/10.1101/cshperspect.a023861
Greenbaum D, Colangelo C, Williams K, Gerstein M (2003) Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol 4:117. https://doi.org/10.1186/gb-2003-4-9-117
Heckmann D, Lloyd CJ, Mih N et al (2018) Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models. Nat Commun 9:5252. https://doi.org/10.1038/s41467-018-07652-6
Hui S, Silverman JM, Chen SS et al (2015) Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Mol Syst Biol 11:784. https://doi.org/10.15252/msb.20145697
Hutchison CA, Chuang R-YR-Y, Noskov VN et al (2016) Design and synthesis of a minimal bacterial genome. Science 351:aad6253. https://doi.org/10.1126/science.aad6253
Ingolia NT, Ghaemmaghami S, Newman JRS, Weissman JS (2009) Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324:218–223. https://doi.org/10.1126/science.1168978
Kafri M, Metzl-Raz E, Jona G, Barkai N (2016) The cost of protein production. Cell Rep 14:22–31. https://doi.org/10.1016/J.CELREP.2015.12.015
Kallehauge TB, Li S, Pedersen LE et al (2017) Ribosome profiling-guided depletion of an mRNA increases cell growth rate and protein secretion. Sci Rep 7:40388. https://doi.org/10.1038/srep40388
Karr JR, Sanghvi JC, MacKlin DN et al (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150:389–401. https://doi.org/10.1016/j.cell.2012.05.044
Kjelgaard N, Gausing K (1974) Regulation of biosynthesis of ribosomes. Cold Spring Harb Monogr Arch 4:369–392
Klappenbach JA, Dunbar JM, Schmidt TM (2000) rRNA operon copy number reflects ecological strategies of bacteria. Appl Environ Microbiol 66:1328–1333
Klumpp S, Scott M, Pedersen S, Hwa T (2013) Molecular crowding limits translation and cell growth. Proc Natl Acad Sci U S A: 110(42):16754–16759. https://doi.org/10.1073/pnas.1310377110
Kudva R et al (2013) Protein translocation across the inner membrane of Gram-negative bacteria: the Sec and Tat dependent protein transport pathways. Res Microbiol 164:505–534
LaCroix RA, Sandberg TE, O’Brien EJ et al (2015) Use of adaptive laboratory evolution to discover key mutations enabling rapid growth of Escherichia coli K-12 MG1655 on glucose minimal medium. Appl Environ Microbiol 81:17–30. https://doi.org/10.1128/AEM.02246-14
Lee HH, Ostrov N, Wong BG et al (2019) Functional genomics of the rapidly replicating bacterium Vibrio natriegens by CRISPRi. Nat Microbiol 4(7):1105–1113. https://doi.org/10.1038/s41564-019-0423-8
Lewis NE, Hixson KK, Conrad TM et al (2010) Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol 6:390. https://doi.org/10.1038/msb.2010.47
Li G-W, Burkhardt D, Gross C, Weissman JS (2014) Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157:624–635. https://doi.org/10.1016/j.cell.2014.02.033
Liao C, Blanchard AE, Lu T (2017) An integrative circuit–host modelling framework for predicting synthetic gene network behaviours. Nat Microbiol 2:1658–1666. https://doi.org/10.1038/s41564-017-0022-5
Lloyd CJ, Ebrahim A, Yang L et al (2018) COBRAme: a computational framework for genome-scale models of metabolism and gene expression. PLoS Comput Biol 14(7):e1006302. https://doi.org/10.1371/journal.pcbi.1006302
Long CP, Gonzalez JE, Cipolla RM, Antoniewicz MR (2017) Metabolism of the fast-growing bacterium Vibrio natriegens elucidated by 13C metabolic flux analysis. Metab Eng 44:191–197. https://doi.org/10.1016/J.YMBEN.2017.10.008
Maida I, Bosi E, Perrin E et al (2013) Draft genome sequence of the fast-growing bacterium Vibrio natriegens strain DSMZ 759. Genome Announc 1:e00648–e00613. https://doi.org/10.1128/genomeA.00648-13
Monk JM, Lloyd CJ, Brunk E et al (2017) iML1515, a knowledge base that computes Escherichia coli traits. Nat Biotechnol 35:904–908. https://doi.org/10.1038/nbt.3956
Mori M, Hwa T, Martin OC, De Martino A, Marinari E (2016) Constrained allocation flux balance analysis. PLoS Comput Biol 12:e1004913
Mori M, Schink S, Erickson DW et al (2017) Quantifying the benefit of a proteome reserve in fluctuating environments. Nat Commun 8:1225. https://doi.org/10.1038/s41467-017-01242-8
Murray HD, Appleman JA, Gourse RL (2003) Regulation of the Escherichia coli rrnB P2 promoter. J Bacteriol 185:28. https://doi.org/10.1128/JB.185.1.28-34.2003
Neidhardt FC, Magasanik B (1960) Studies on the role of ribonucleic acid in the growth of bacteria. Biochim Biophys Acta 42:99–116. https://doi.org/10.1016/0006-3002(60)90757-5
Nikolados E-M, Weisse AY, Ceroni F, Oyarzun DA (2019) Growth defects and loss-of-function in synthetic gene circuits. bioRxiv:623421. https://doi.org/10.1101/623421
O’Brien EJ, Lerman JA, Chang RL et al (2014) Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol 9:693–693. https://doi.org/10.1038/msb.2013.52
O’Brien EJ, Utrilla J, Palsson BO (2016) Quantification and classification of E. coli proteome utilization and unused protein costs across environments. PLoS Comput Biol 12:e1004998. https://doi.org/10.1371/journal.pcbi.1004998
Peebo K, Valgepea K, Maser A et al (2015) Proteome reallocation in Escherichia coli with increasing specific growth rate. Mol BioSyst 11:1184–1193. https://doi.org/10.1039/C4MB00721B
Pirt SJ (1965) The maintenance energy of bacteria in growing cultures. Proc R Soc Lond B Biol Sci 163(991):224–231
Price MN, Wetmore KM, Deutschbauer AM, Arkin AP (2016) A comparison of the costs and benefits of bacterial gene expression. PLoS One 11:e0164314. https://doi.org/10.1371/journal.pone.0164314
Schmidt A, Kochanowski K, Vedelaar S et al (2015) The quantitative and condition-dependent Escherichia coli proteome. Nat Biotechnol 34:104–110
Schmidt A, Kochanowski K, Vedelaar S et al (2016) The quantitative and condition-dependent Escherichia coli proteome. Nat Biotechnol 34:104–110. https://doi.org/10.1038/nbt.3418
Scott M, Gunderson CW, Mateescu EM et al (2010) Interdependence of cell growth and gene expression: origins and consequences. Science 330:1099–1102. https://doi.org/10.1126/science.1192588
Scott M, Klumpp S, Mateescu EM, Hwa T (2014) Emergence of robust growth laws from optimal regulation of ribosome synthesis. Mol Syst Biol 10:747–747. https://doi.org/10.15252/msb.20145379
Selvarasu S, Ow DS-W, Lee SY et al (2009) Characterizing Escherichia coli DH5α growth and metabolism in a complex medium using genome-scale flux analysis. Biotechnol Bioeng 102:923–934. https://doi.org/10.1002/bit.22119
Shepherd N, Churchward G, Bremer H (1980) Synthesis and function of ribonucleic acid polymerase and ribosomes in Escherichia coli B/r after a nutritional shift-up. J Bacteriol 143:1332–1344
Tan C, Marguet P, You L (2009) Emergent bistability by a growth-modulating positive feedback circuit. Nat Chem Biol 5:842–848. https://doi.org/10.1038/nchembio.218
Thiele I, Jamshidi N, Fleming RMT, Palsson BØ (2009) Genome-scale reconstruction of Escherichia coli’s transcriptional and translational machinery: a knowledge base, its mathematical formulation, and its functional characterization. PLoS Comput Biol 5:e1000312. https://doi.org/10.1371/journal.pcbi.1000312
Utrilla J, O’Brien EJ, Chen K et al (2016) Global rebalancing of cellular resources by pleiotropic point mutations illustrates a multi-scale mechanism of adaptive evolution. Cell Syst 2:260–271. https://doi.org/10.1016/j.cels.2016.04.003
Valgepea K, Adamberg K, Seiman A, Vilu R (2013) Escherichia coli achieves faster growth by increasing catalytic and translation rates of proteins. Mol BioSyst 9:2344–2358. https://doi.org/10.1039/c3mb70119k
Wang Z, Lin B, Hervey WJ et al (2013) Draft genome sequence of the fast-growing marine bacterium Vibrio natriegens strain ATCC 14048. Genome Announc 1(4):e00589–13. https://doi.org/10.1128/genomeA.00589-13
Wehrs M, Tanjore D, Eng T et al (2019) Engineering robust production microbes for large-scale cultivation. Trends Microbiol:1–14. https://doi.org/10.1016/j.tim.2019.01.006
Weinstock MT, Hesek ED, Wilson CM, Gibson DG (2016) Vibrio natriegens as a fast-growing host for molecular biology. Nat Methods 13:849–851. https://doi.org/10.1038/nmeth.3970
Weiße AY, Oyarzún DA, Danos V, Swain PS (2015) Mechanistic links between cellular trade-offs, gene expression, and growth. Proc Natl Acad Sci U S A 112:E1038–E1047. https://doi.org/10.1073/pnas.1416533112
Wilson DN, Nierhaus KH (2007) The weird and wonderful world of bacterial ribosome regulation. Crit Rev Biochem Mol Biol 42:187–219. https://doi.org/10.1080/10409230701360843
Yang L, Tan J, O’Brien EJ et al (2015) Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data. Proc Natl Acad Sci U S A 112(34):10810–10815. https://doi.org/10.1073/pnas.1501384112
Yang L et al (2016) Principles of proteome allocation are revealed using proteomic data and genome-scale models. Sci Rep 6:36734
You C, Okano H, Hui S et al (2013) Coordination of bacterial proteome with metabolism by cyclic AMP signalling. Nature:1–6. https://doi.org/10.1038/nature12446
Acknowledgment
Support from grants UNAM-PAPIIT-IA201518 and Newton Advanced Fellowship Project NA 160328 is acknowledged.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Hidalgo, D., Utrilla, J. (2020). Resource Allocation Principles and Minimal Cell Design. In: Lara, A., Gosset, G. (eds) Minimal Cells: Design, Construction, Biotechnological Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-31897-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-31897-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31896-3
Online ISBN: 978-3-030-31897-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)