Advertisement

Resource Allocation Principles and Minimal Cell Design

  • David Hidalgo
  • José UtrillaEmail author
Chapter

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.

Keywords

Resource allocation Proteome Efficiency Trade-off Minimal cell Bacteria Design 

Notes

Acknowledgment

Support from grants UNAM-PAPIIT-IA201518 and Newton Advanced Fellowship Project NA 160328 is acknowledged.

References

  1. Aiyar SE, Gaal T, Gourse RL (2002) rRNA promoter activity in the fast-growing bacterium Vibrio natriegens. J Bacteriol 184:1349–1358CrossRefGoogle Scholar
  2. 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 CrossRefPubMedGoogle Scholar
  3. Bachmann BJ (1990) Linkage map of Escherichia coli K-12, edition 8. Microbiol Rev 54:130–197PubMedPubMedCentralGoogle Scholar
  4. Baracchini E, Bremer H (1988) Stringent and growth control of rRNA synthesis in Escherichia coli are both mediated by ppGpp. J Biol Chem 263:2597–2602PubMedGoogle Scholar
  5. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 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 CrossRefPubMedGoogle Scholar
  9. 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 CrossRefPubMedGoogle Scholar
  10. 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 12:4305–4315CrossRefGoogle Scholar
  11. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  13. de Jong H, Geiselmann J, Ropers D (2017) Resource reallocation in bacteria by reengineering the gene expression machinery. Trends Microbiol 25:480–493CrossRefGoogle Scholar
  14. 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
  15. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 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 CrossRefGoogle Scholar
  17. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Edwards J, Ibarra R, Palsson B (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol:125–130CrossRefGoogle Scholar
  19. Elowitz M, Leibler S (2000) A synthetic oscillatory netwrok of transcriptional regulators. Nature 403:335–338CrossRefGoogle Scholar
  20. 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 CrossRefPubMedGoogle Scholar
  21. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  24. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 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 CrossRefPubMedGoogle Scholar
  28. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Kjelgaard N, Gausing K (1974) Regulation of biosynthesis of ribosomes. Cold Spring Harb Monogr Arch 4:369–392Google Scholar
  31. Klappenbach JA, Dunbar JM, Schmidt TM (2000) rRNA operon copy number reflects ecological strategies of bacteria. Appl Environ Microbiol 66:1328–1333CrossRefGoogle Scholar
  32. 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 CrossRefGoogle Scholar
  33. 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 164:505–534CrossRefGoogle Scholar
  34. 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 CrossRefPubMedGoogle Scholar
  35. 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 CrossRefGoogle Scholar
  36. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  37. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  38. 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 CrossRefPubMedGoogle Scholar
  39. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  41. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  42. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  43. Mori M, Hwa T, Martin OC, De Martino A, Marinari E (2016) Constrained allocation flux balance analysis. PLoS Comput Biol 12:e1004913CrossRefGoogle Scholar
  44. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  45. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  46. 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 CrossRefPubMedGoogle Scholar
  47. 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
  48. 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 CrossRefGoogle Scholar
  49. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  50. 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 CrossRefPubMedGoogle Scholar
  51. Pirt SJ (1965) The maintenance energy of bacteria in growing cultures. Proc R Soc Lond B Biol Sci 163(991):224–231CrossRefGoogle Scholar
  52. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  53. Schmidt A, Kochanowski K, Vedelaar S et al (2015) The quantitative and condition-dependent Escherichia coli proteome. Nat Biotechnol 34:104–110CrossRefGoogle Scholar
  54. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  55. 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 CrossRefPubMedGoogle Scholar
  56. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  57. 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 CrossRefPubMedGoogle Scholar
  58. 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 143:1332–1344PubMedPubMedCentralGoogle Scholar
  59. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  60. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  61. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  62. 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 CrossRefPubMedGoogle Scholar
  63. 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
  64. 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 CrossRefGoogle Scholar
  65. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  66. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  67. 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 CrossRefPubMedGoogle Scholar
  68. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  69. Yang L et al (2016) Principles of proteome allocation are revealed using proteomic data and genome-scale models. Sci Rep 6:36734CrossRefGoogle Scholar
  70. 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 CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Programa de Biología de Sistemas y Biología Sintetica, Cengro de Ciencias Genómicas, Universidad Nacional Autónoma de MéxicoCuernavacaMéxico

Personalised recommendations