Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations

English version

Autoři: Jessica A. Lee ^aff001; Siavash Riazi ^aff002; Shahla Nemati ^aff002; Jannell V. Bazurto ^aff001; Andreas E. Vasdekis ^aff002; Benjamin J. Ridenhour ^aff002; Christopher H. Remien ^aff002; Christopher J. Marx ^aff001
Působiště autorů: Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America ^aff001; Center for Modeling Complex Interactions, University of Idaho, Moscow, Idaho, United States of America ^aff002; Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho, United States of America ^aff003; Global Viral, San Francisco, California, United States of America ^aff004; Bioinformatics and Computational Biology Graduate Program, University of Idaho, Moscow, Idaho, United States of America ^aff005; Department of Physics, University of Idaho, Moscow, Idaho, United States of America ^aff006; Department of Plant and Microbial Biology, University of Minnesota, Twin Cities, Minnesota, United States of America ^aff007; Microbial and Plant Genomics Institute, University of Minnesota, Twin Cities, Minnesota, United States of America ^aff008; Department of Mathematics, University of Idaho, Moscow, Idaho, United States of America ^aff009
Vyšlo v časopise: Microbial phenotypic heterogeneity in response to a metabolic toxin: Continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations. PLoS Genet 15(11): e32767. doi:10.1371/journal.pgen.1008458
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1008458

Souhrn

While microbiologists often make the simplifying assumption that genotype determines phenotype in a given environment, it is becoming increasingly apparent that phenotypic heterogeneity (in which one genotype generates multiple phenotypes simultaneously even in a uniform environment) is common in many microbial populations. The importance of phenotypic heterogeneity has been demonstrated in a number of model systems involving binary phenotypic states (e.g., growth/non-growth); however, less is known about systems involving phenotype distributions that are continuous across an environmental gradient, and how those distributions change when the environment changes. Here, we describe a novel instance of phenotypic diversity in tolerance to a metabolic toxin within wild-type populations of Methylobacterium extorquens, a ubiquitous phyllosphere methylotroph capable of growing on the methanol periodically released from plant leaves. The first intermediate in methanol metabolism is formaldehyde, a potent cellular toxin that is lethal in high concentrations. We have found that at moderate concentrations, formaldehyde tolerance in M. extorquens is heterogeneous, with a cell's minimum tolerance level ranging between 0 mM and 8 mM. Tolerant cells have a distinct gene expression profile from non-tolerant cells. This form of heterogeneity is continuous in terms of threshold (the formaldehyde concentration where growth ceases), yet binary in outcome (at a given formaldehyde concentration, cells either grow normally or die, with no intermediate phenotype), and it is not associated with any detectable genetic mutations. Moreover, tolerance distributions within the population are dynamic, changing over time in response to growth conditions. We characterized this phenomenon using bulk liquid culture experiments, colony growth tracking, flow cytometry, single-cell time-lapse microscopy, transcriptomics, and genome resequencing. Finally, we used mathematical modeling to better understand the processes by which cells change phenotype, and found evidence for both stochastic, bidirectional phenotypic diversification and responsive, directed phenotypic shifts, depending on the growth substrate and the presence of toxin.

Klíčová slova:

Gene expression – Phenotypes – Population dynamics – Population genetics – Genomic libraries – Protein domains – Cell death – Formaldehyde

Zdroje

1. Harms A, Fino C, Sørensen MA, Semsey S, Gerdes K. Prophages and growth dynamics confound experimental results with antibiotic-tolerant persister cells. mBio. 2017;8: e01964–17. doi: 10.1128/mBio.01964-17 29233898

2. Shaffer SM, Dunagin MC, Torborg SR, Torre EA, Emert B, Krepler C, et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature. 2017;546: 431–435. doi: 10.1038/nature22794 28607484

3. Lorenzi T, Chisholm RH, Desvillettes L, Hughes BD. Dissecting the dynamics of epigenetic changes in phenotype-structured populations exposed to fluctuating environments. J Theor Biol. 2015;386: 166–176. doi: 10.1016/j.jtbi.2015.08.031 26375370

4. Vasdekis AE, Silverman AM, Stephanopoulos G. Origins of cell-to-cell bioprocessing diversity and implications of the extracellular environment revealed at the single-cell level. Sci Rep. 2015;5: 17689. doi: 10.1038/srep17689 26657999

5. Elowitz MB, Levine AJ, Siggia ED, Swain PS. Stochastic gene expression in a single cell. Science. 2002;297: 1183–1186. doi: 10.1126/science.1070919 12183631

6. Kiviet DJ, Nghe P, Walker N, Boulineau S, Sunderlikova V, Tans SJ. Stochasticity of metabolism and growth at the single-cell level. Nature. 2014;514: 376–379. doi: 10.1038/nature13582 25186725

7. Newman JRS, Ghaemmaghami S, Ihmels J, Breslow DK, Noble M, DeRisi JL, et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature. 2006;441: 840. doi: 10.1038/nature04785 16699522

8. Bergmiller T, Ackermann M. Pole age affects cell size and the timing of cell division in Methylobacterium extorquens AM1. J Bacteriol. 2011;193: 5216–5221. doi: 10.1128/JB.00329-11 21784923

9. Levy SF, Ziv N, Siegal ML. Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant. PLOS Biol. 2012;10: e1001325. doi: 10.1371/journal.pbio.1001325 22589700

10. Huh D, Paulsson J. Random partitioning of molecules at cell division. Proc Natl Acad Sci U S A. 2011;108: 15004–15009. doi: 10.1073/pnas.1013171108 21873252

11. Vasdekis AE, Alanazi H, Silverman AM, Williams CJ, Canul AJ, Cliff JB, et al. Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging. Nat Commun. 2019;10: 1–11.

12. Ackermann M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat Rev Microbiol. 2015;13: 497–508. doi: 10.1038/nrmicro3491 26145732

13. Balaban NQ, Merrin J, Chait R, Kowalik L, Leibler S. Bacterial persistence as a phenotypic switch. Science. 2004;305: 1622–1625. doi: 10.1126/science.1099390 15308767

14. Veening J-W, Stewart EJ, Berngruber TW, Taddei F, Kuipers OP, Hamoen LW. Bet-hedging and epigenetic inheritance in bacterial cell development. Proc Natl Acad Sci. 2008;105: 4393–4398. doi: 10.1073/pnas.0700463105 18326026

15. Choi PJ, Cai L, Frieda K, Xie XS. A stochastic single-molecule event triggers phenotype switching of a bacterial cell. Science. 2008;322: 442. doi: 10.1126/science.1161427 18927393

16. Venturelli OS, Zuleta I, Murray RM, El-Samad H. Population diversification in a yeast metabolic program promotes anticipation of environmental shifts. PLOS Biol. 2015;13: e1002042. doi: 10.1371/journal.pbio.1002042 25626086

17. Wang J, Atolia E, Hua B, Savir Y, Escalante-Chong R, Springer M. Natural variation in preparation for nutrient depletion reveals a cost–benefit tradeoff. PLOS Biol. 2015;13: e1002041. doi: 10.1371/journal.pbio.1002041 25626068

18. Eldar A, Elowitz MB. Functional roles for noise in genetic circuits. Nature. 2010;467: 167–173. doi: 10.1038/nature09326 20829787

19. Fridman O, Goldberg A, Ronin I, Shoresh N, Balaban NQ. Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations. Nature. 2014;513: 418–421. doi: 10.1038/nature13469 25043002

20. Rainey PB, Beaumont HJ, Ferguson GC, Gallie J, Kost C, Libby E, et al. The evolutionary emergence of stochastic phenotype switching in bacteria. Microb Cell Factories. 2011;10: S14. doi: 10.1186/1475-2859-10-S1-S14 21995592

21. Chisholm RH, Lorenzi T, Desvillettes L, Hughes BD. Evolutionary dynamics of phenotype-structured populations: from individual-level mechanisms to population-level consequences. Z Für Angew Math Phys. 2016;67: 100. doi: 10.1007/s00033-016-0690-7

22. Klironomos FD, Berg J, Collins S. How epigenetic mutations can affect genetic evolution: Model and mechanism. BioEssays. 2013;35: 571–578. doi: 10.1002/bies.201200169 23580343

23. Levin-Reisman I, Ronin I, Gefen O, Braniss I, Shoresh N, Balaban NQ. Antibiotic tolerance facilitates the evolution of resistance. Science. 2017;355: 826–830. doi: 10.1126/science.aaj2191 28183996

24. Deris JB, Kim M, Zhang Z, Okano H, Hermsen R, Groisman A, et al. The innate growth bistability and fitness landscapes of antibiotic-resistant bacteria. Science. 2013;342: 1237435. doi: 10.1126/science.1237435 24288338

25. Lorenzi T, Chisholm RH, Clairambault J. Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations. Biol Direct. 2016;11: 43. doi: 10.1186/s13062-016-0143-4 27550042

26. Fall R, Benson AA. Leaf methanol—the simplest natural product from plants. Trends Plant Sci. 1996;1: 296–301. doi: 10.1016/S1360-1385(96)88175-0

27. Skovran E, Crowther GJ, Guo X, Yang S, Lidstrom ME. A systems biology approach uncovers cellular strategies used by Methylobacterium extorquens AM1 during the switch from multi- to single-carbon growth. PLoS ONE. 2010;5: e14091. doi: 10.1371/journal.pone.0014091 21124828

28. Nayak DD, Marx CJ. Genetic and phenotypic comparison of facultative methylotrophy between Methylobacterium extorquens strains PA1 and AM1. PLoS ONE. 2014;9: e107887. doi: 10.1371/journal.pone.0107887 25232997

29. Chen NH, Djoko KY, Veyrier FJ, McEwan AG. Formaldehyde stress responses in bacterial pathogens. Front Microbiol. 2016;7. doi: 10.3389/fmicb.2016.00257 26973631

30. Marx CJ, Chistoserdova L, Lidstrom ME. Formaldehyde-detoxifying role of the tetrahydromethanopterin-linked pathway in Methylobacterium extorquens AM1. J Bacteriol. 2003;185: 7160–7168. doi: 10.1128/JB.185.23.7160-7168.2003 14645276

31. Atwood KC, Norman A. On the interpretation of multi-hit survival curves. Proc Natl Acad Sci U S A. 1949;35: 696–709. doi: 10.1073/pnas.35.12.696 16578327

32. Peleg M. Microbial survival curves—the reality of flat “shoulders” and absolute thermal death times. Food Res Int. 2000;33: 531–538. doi: 10.1016/S0963-9969(00)00088-0

33. Regoes RR, Wiuff C, Zappala RM, Garner KN, Baquero F, Levin BR. Pharmacodynamic functions: a multiparameter approach to the design of antibiotic treatment regimens. Antimicrob Agents Chemother. 2004;48: 3670–3676. doi: 10.1128/AAC.48.10.3670-3676.2004 15388418

34. Levin BR, Udekwu KI. Population dynamics of antibiotic treatment: a mathematical model and hypotheses for time-kill and continuous-culture experiments. Antimicrob Agents Chemother. 2010;54: 3414–3426. doi: 10.1128/AAC.00381-10 20516272

35. Yurtsev EA, Chao HX, Datta MS, Artemova T, Gore J. Bacterial cheating drives the population dynamics of cooperative antibiotic resistance plasmids. Mol Syst Biol. 2013;9: 683. doi: 10.1038/msb.2013.39 23917989

36. Lennon JT, Jones SE. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat Rev Microbiol. 2011;9: 119–130. doi: 10.1038/nrmicro2504 21233850

37. Kotte O, Volkmer B, Radzikowski JL, Heinemann M. Phenotypic bistability in Escherichia coli’s central carbon metabolism. Mol Syst Biol. 2014;10: 736–736. doi: 10.15252/msb.20135022 24987115

38. van Boxtel C, van Heerden JH, Nordholt N, Schmidt P, Bruggeman FJ. Taking chances and making mistakes: non-genetic phenotypic heterogeneity and its consequences for surviving in dynamic environments. J R Soc Interface. 2017;14: 20170141. doi: 10.1098/rsif.2017.0141 28701503

39. Levin-Reisman I, Fridman O, Balaban NQ. ScanLag: High-throughput quantification of colony growth and lag time. J Vis Exp JoVE. 2014; doi: 10.3791/51456 25077667

40. Guillier L, Pardon P, Augustin J-C. Automated image analysis of bacterial colony growth as a tool to study individual lag time distributions of immobilized cells. J Microbiol Methods. 2006;65: 324–334. doi: 10.1016/j.mimet.2005.08.007 16185781

41. Ernebjerg M, Kishony R. Distinct growth strategies of soil bacteria as revealed by large-scale colony tracking. Appl Environ Microbiol. 2012;78: 1345–1352. doi: 10.1128/AEM.06585-11 22194284

42. Roca A, Rodríguez-Herva J-J, Duque E, Ramos JL. Physiological responses of Pseudomonas putida to formaldehyde during detoxification. Microb Biotechnol. 2008;1: 158–169. doi: 10.1111/j.1751-7915.2007.00014.x 21261833

43. Giuffrè A, Borisov VB, Arese M, Sarti P, Forte E. Cytochrome bd oxidase and bacterial tolerance to oxidative and nitrosative stress. Biochim Biophys Acta. 2014;1837: 1178–1187. doi: 10.1016/j.bbabio.2014.01.016 24486503

44. Borisov VB, Gennis RB, Hemp J, Verkhovsky MI. The cytochrome bd respiratory oxygen reductases. Biochim Biophys Acta BBA—Bioenerg. 2011;1807: 1398–1413. doi: 10.1016/j.bbabio.2011.06.016 21756872

45. de Graaf B, Clore A, McCullough AK. Cellular pathways for DNA repair and damage tolerance of formaldehyde-induced DNA-protein crosslinks. DNA Repair. 2009;8: 1207–1214. doi: 10.1016/j.dnarep.2009.06.007 19625222

46. Mitsui R, Kusano Y, Yurimoto H, Sakai Y, Kato N, Tanaka M. Formaldehyde fixation contributes to detoxification for growth of a nonmethylotroph, Burkholderia cepacia TM1, on vanillic acid. Appl Environ Microbiol. 2003;69: 6128–6132. doi: 10.1128/AEM.69.10.6128-6132.2003 14532071

47. Sudtachat N, Ito N, Itakura M, Masuda S, Eda S, Mitsui H, et al. Aerobic vanillate degradation and C1 compound metabolism in Bradyrhizobium japonicum. Appl Environ Microbiol. 2009;75: 5012–5017. doi: 10.1128/AEM.00755-09 19502448

48. Lee JA, Stolyar S, Marx CJ. An aerobic link between lignin degradation and C1 metabolism: growth on methoxylated aromatic compounds by members of the genus Methylobacterium. bioRxiv. 2019; 712836. doi: 10.1101/712836

49. Peredo EL, Simmons SL. Leaf-FISH: microscale imaging of bacterial taxa on phyllosphere. Front Microbiol. 2018;8. doi: 10.3389/fmicb.2017.02669 29375531

50. Voordeckers K, Kominek J, Das A, Espinosa-Cantú A, Maeyer DD, Arslan A, et al. Adaptation to high ethanol reveals complex evolutionary pathways. PLOS Genet. 2015;11: e1005635. doi: 10.1371/journal.pgen.1005635 26545090

51. Costa E, Pérez J, Kreft J-U. Why is metabolic labour divided in nitrification? Trends Microbiol. 2006;14: 213–219. doi: 10.1016/j.tim.2006.03.006 16621570

52. Strovas TJ, Sauter LM, Guo X, Lidstrom ME. Cell-to-Cell heterogeneity in growth rate and gene expression in Methylobacterium extorquens AM1. J Bacteriol. 2007;189: 7127–7133. doi: 10.1128/JB.00746-07 17644598

53. Strovas TJ, Lidstrom ME. Population heterogeneity in Methylobacterium extorquens AM1. Microbiology. 2009;155: 2040–2048. doi: 10.1099/mic.0.025890-0 19383691

54. Gallie J, Libby E, Bertels F, Remigi P, Jendresen CB, Ferguson GC, et al. Bistability in a metabolic network underpins the de novo evolution of colony switching in Pseudomonas fluorescens. PLOS Biol. 2015;13: e1002109. doi: 10.1371/journal.pbio.1002109 25763575

55. Roff DA. The evolution of threshold traits in animals. Q Rev Biol. 1996;71: 3–35. doi: 10.1086/419266

56. Govers SK, Mortier J, Adam A, Aertsen A. Protein aggregates encode epigenetic memory of stressful encounters in individual Escherichia coli cells. PLOS Biol. 2018;16: e2003853. doi: 10.1371/journal.pbio.2003853 30153247

57. Bandyopadhyay A, Wang H, Ray JCJ. Lineage space and the propensity of bacterial cells to undergo growth transitions. PLOS Comput Biol. 2018;14: e1006380. doi: 10.1371/journal.pcbi.1006380 30133447

58. Kussell E, Leibler S. Phenotypic diversity, population growth, and information in fluctuating environments. Science. 2005;309: 2075–2078. doi: 10.1126/science.1114383 16123265

59. Arnoldini M, Mostowy R, Bonhoeffer S, Ackermann M. Evolution of stress response in the face of unreliable environmental signals. PLOS Comput Biol. 2012;8: e1002627. doi: 10.1371/journal.pcbi.1002627 22916000

60. Beaumont HJE, Gallie J, Kost C, Ferguson GC, Rainey PB. Experimental evolution of bet hedging. Nature. 2009;462: 90. doi: 10.1038/nature08504 19890329

61. Draghi J. Links between evolutionary processes and phenotypic robustness in microbes. Semin Cell Dev Biol. 2018; doi: 10.1016/j.semcdb.2018.05.017 29803630

62. Abanda-Nkpwatt D, Müsch M, Tschiersch J, Boettner M, Schwab W. Molecular interaction between Methylobacterium extorquens and seedlings: growth promotion, methanol consumption, and localization of the methanol emission site. J Exp Bot. 2006;57: 4025–4032. doi: 10.1093/jxb/erl173 17043084

63. Ryffel F, Helfrich EJN, Kiefer P, Peyriga L, Portais J-C, Piel J, et al. Metabolic footprint of epiphytic bacteria on Arabidopsis thaliana leaves. ISME J. 2016;10: 632–643. doi: 10.1038/ismej.2015.141 26305156

64. Nemecek-Marshall M, MacDonald RC, Franzen JJ, Wojciechowski CL, Fall R. Methanol emission from leaves (Enzymatic detection of gas-phase methanol and relation of methanol fluxes to stomatal conductance and leaf development). Plant Physiol. 1995;108: 1359–1368. doi: 10.1104/pp.108.4.1359 12228547

65. Ackermann M. Microbial individuality in the natural environment. ISME J. 2013;7: 465–467. doi: 10.1038/ismej.2012.131 23178672

66. Delaney NF, Kaczmarek ME, Ward LM, Swanson PK, Lee M-C, Marx CJ. Development of an optimized medium, strain and high-throughput culturing methods for Methylobacterium extorquens. PLOS ONE. 2013;8: e62957. doi: 10.1371/journal.pone.0062957 23646164

67. Nash T. The colorimetric estimation of formaldehyde by means of the Hantzsch reaction. Biochem J. 1953;55: 416–421. doi: 10.1042/bj0550416 13105648

68. Deatherage DE, Barrick JE. Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Engineering and Analyzing Multicellular Systems. Humana Press, New York, NY; 2014. pp. 165–188. doi: 10.1007/978-1-4939-0554-6_12 24838886

69. Marx CJ, Bringel F, Chistoserdova L, Moulin L, Farhan Ul Haque M, Fleischman DE, et al. Complete genome sequences of six strains of the genus Methylobacterium. J Bacteriol. 2012;194: 4746–4748. doi: 10.1128/JB.01009-12 22887658

70. Michener JK, Vuilleumier S, Bringel F, Marx CJ. Transfer of a catabolic pathway for chloromethane in Methylobacterium strains highlights different limitations for growth with chloromethane or with dichloromethane. Front Microbiol. 2016;7. doi: 10.3389/fmicb.2016.01116 27486448

71. R Core Team. R: A language and environment for statistical computing. [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2018. http://www.R-project.org

72. Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, Sarkar D, et al. flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics. 2009;10: 106. doi: 10.1186/1471-2105-10-106 19358741

73. Wickham, H. ggplot2: Elegant graphics for data analysis [Internet]. New York: Springer-Verlag; 2016. http://ggplot2.org

74. van der Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, et al. scikit-image: image processing in Python. PeerJ. 2014;2: e453. doi: 10.7717/peerj.453 25024921

75. Babraham Bioinformatics. FastQC: A Quality Control tool for High Throughput Sequence Data [Internet]. [cited 18 Aug 2019]. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

76. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30: 2114–2120. doi: 10.1093/bioinformatics/btu170 24695404

77. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12: 357–360. doi: 10.1038/nmeth.3317 25751142

78. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7: 562–578. doi: 10.1038/nprot.2012.016 22383036

79. Liao Y, Smyth GK, Shi W. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res. 2019;47: e47–e47. doi: 10.1093/nar/gkz114 30783653

80. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. bioRxiv. 2014; doi: 10.1101/002832

81. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9: 671–675. doi: 10.1038/nmeth.2089 22930834

82. Wilcoxon F. Individual comparisons by ranking methods. Biom Bull. 1945;1: 80–83. doi: 10.2307/3001968

83. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26: 32–46. doi: 10.1111/j.1442-9993.2001.01070.pp.x

84. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, et al. The vegan package. Community Ecol Package. 2007;10: 631–637.

85. McAllister CF, Lepo JE. Succinate transport by free-living forms of Rhizobium japonicum. J Bacteriol. 1983;153: 1155–1162. 6402487

86. Anthony C, Zatman LJ. The microbial oxidation of methanol. 2. The methanol-oxidizing enzyme of Pseudomonas sp. M27. Biochem J. 1964;92: 614–621. doi: 10.1042/bj0920614 4378696

87. Soetaert K, Meysman F. Reactive transport in aquatic ecosystems: Rapid model prototyping in the open source software R. Environ Model Softw. 2012;32: 49–60. doi: 10.1016/j.envsoft.2011.08.011

88. Stoetaert K, Petzoldt T, Setzer W. Solving differential equations in R: package deSolve. J Stat Softw. 2010;33: 1–25.

89. Hindmarsh AC. ODEPACK, A systematized collection of ODE solvers. In: Stepleman RS, editor. Scientific Computing. Amsterdam: North-Holland; 1983. pp. 55–64.