Review Article

Synthetic Animal: Trends in Animal Breeding and Genetics

Abolfazl Bahrami* and Ali Najafi

Published: 11 January, 2019 | Volume 3 - Issue 1 | Pages: 007-025

Synthetic biology is an interdisciplinary branch of biology and engineering. The subject combines various disciplines from within these domains, such as biotechnology, evolutionary biology, molecular biology, systems biology, biophysics, computer engineering, and genetic engineering. Synthetic biology aims to understand whole biological systems working as a unit, rather than investigating their individual components and design new genome. Significant advances have been made using systems biology and synthetic biology approaches, especially in the field of bacterial and eukaryotic cells. Similarly, progress is being made with ‘synthetic approaches’ in genetics and animal sciences, providing exciting opportunities to modulate, genome design and finally synthesis animal for favorite traits.

Read Full Article HTML DOI: 10.29328/journal.ibm.1001015 Cite this Article Read Full Article PDF


Synthetic biology; Systems biology; Synthetic approaches; Genetic engineering


  1. Charles D. On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. Nature. 1859; 5: 502. Ref.: https://goo.gl/Vd9Zda
  2. Wright S. Statistical genetics and evolution. Bull Amer Math Soc 1942; 48: 223–246. Ref.: https://goo.gl/kFP84Y
  3. Fisher RA. The Correlation between Relatives on the Supposition of Mendelian Inheritance. Philosophical Transactions of the Royal Society of Edinburgh. 1918; 52: 399–433. Ref.: https://goo.gl/1FTvrH
  4. Haldane JB. Linkage in poultry. Science. 1921; 54: 663. Ref.: https://goo.gl/ayCNS9
  5. Morgan TH. Sex-limited inheritance in Drosophila. Science. 1910; 32:120–122. Ref.: https://goo.gl/cpCDXv
  6. Lush JL. 1896 - 1982 Biographical Memoirs of the AAAS. Ref.: https://goo.gl/C133gz
  7. Van Vleck LD. Charles Roy Henderson, 1911-1989: A brief biography. J Anim Sci. 1998; 76: 2959–2961. Ref.: https://goo.gl/Gjhraa
  8. Bahrami A, Miraei-Ashtiani SR, Mehrabani-Yeganeh H. Associations of growth hormone secretagogue receptor (GHSR) genes polymorphisms and protein structure changes with carcass traits in sheep. Gene. 2012; 505: 379–383. Ref.: https://goo.gl/GZy8PK
  9. Bahrami A, Behzadi SH, Miraei-Ashtiani SR, Roh SG, Katoh K. Genetic polymorphisms and protein structures in growth hormone, growth hormone receptor, ghrelin, insulin-like growth factor 1 and leptin in Mehraban sheep. Gene. 2013; 527: 397–404. Ref.: https://goo.gl/sZu7RM
  10. Meuwissen TH, Goddard ME. Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics. 2010; 185: 623–631. Ref.: https://goo.gl/2jynnU
  11. Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001; 157: 1819–1829. Ref.: https://goo.gl/CdLnVe
  12. Cole JB, VanRaden PM, O'Connell JR, Van Tassell CP, et al. Distribution and location of genetic effects for dairy traits. J Dairy Sci. 2009; 92: 2931–2946. Ref.: https://goo.gl/Jucx4Q
  13. Daetwyler HD, Kemper KE, van der Werf JH, Hayes BJ. Components of the accuracy of genomic prediction in a multi-breed sheep population. J Anim Sci 2012; 90: 3375–3384. Ref.: https://goo.gl/mhxhnB
  14. Pryce JE, Daetwyler HD. Designing dairy cattle breeding schemes under genomic selection: a review of international research. Anim Prod Sci. 2011; 52: 107–114. Ref.: https://goo.gl/tfrgDu
  15. Schaeffer LR. Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet. 2006; 123: 218–223. Ref.: https://goo.gl/iCbuqF
  16. Erbe M, Hayes BJ, Matukumalli LK, Goswami S, Bowman PJ, et al., Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. J Dairy Sci. 2012; 95: 4114–4129. Ref.: https://goo.gl/XfWQiV
  17. Daetwyler HD, Villanueva B, Woolliams JA. Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE. 2008; 3: e3395. Ref.: https://goo.gl/JPHkUa
  18. Goddard M. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica. 2008; 136: 245-257. Ref.: https://goo.gl/SpuwtD
  19. Habier D, Tetens J, Seefried FR, Lichtner P, Thaller G. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet Sel Evol. 2010; 42: 5. Ref.: https://goo.gl/QUYg71
  20. Hayes BJ, Macleod I, Daetwyler MD, Goddard ME. Towards genomic prediction from genome sequence data and the 1000 bull genomes project, Proceedings 4th International Conference on Quantiative Genetics, Edinburgh. 2012; O–54. Ref.: https://goo.gl/rg4DZJ
  21. Sanford JC, Klein TM, Wolf ED, Allen N. Delivery of substances into cells and tissues using a particle bombardment process. Journal of Particulate Science and Technology. 1987; 5: 27–37. Ref.: https://goo.gl/FXPGpK
  22. Klein RM, Wolf ED, Wu R, Sanford JC. High-velocity microprojectiles for delivering nucleic acids into living cells. Nature. 1987; 327: 70–73. Ref.: https://goo.gl/dApfMA
  23. Park F. Lentiviral vectors: are they the future of animal transgenesis? Physiol. Genomics. 2007; 31: 159–173. Ref.: https://goo.gl/2aqAeY
  24. Lee LY, Gelvin SB. T-DNA binary vectors and systems. Plant Physiol. 2008; 146: 325–332. Ref.: https://goo.gl/qamdgR
  25. Jackson DA, Symons RH, Berg P. Biochemical Method for Inserting New Genetic Information into DNA of Simian Virus 40: Circular SV40 DNA Molecules Containing Lambda Phage Genes and the Galactose Operon of Escherichia coli. PNAS. 1972; 69: 2904–2909. Ref.: https://goo.gl/YctKZY
  26. Brophy B, Smolenski G, Wheeler T, Wells D, L'Huillier P, et al. Cloned transgenic cattle produce milk with higher levels of β-casein and κ-casein. Nat Biotechnol. 2003; 21; 157–162. Ref.: https://goo.gl/J24QzX
  27. Clark J. The Mammary Gland as a Bioreactor: Expression, Processing, and Production of Recombinant Proteins. Journal of Mammary Gland Biology and Neoplasia. 1998; 3: 337–350. Ref.: https://goo.gl/EJfyn2
  28. Gordon K, Lee E, Vitale JA, Smith AE, Westphal H, et al. Production of human tissue plasmnogen activator in transgenic mouse milk. Biotechnology. 1987; 5: 1183-1187. Ref.: https://goo.gl/iqhpp6
  29. Anastasia B. Risk Assessment and Mitigation of AquAdvantage Salmon. 2010; ISB News Report. Ref.: https://goo.gl/Jjxcyw
  30. Thomas MA, Roemer GW, Donlan CJ, Dickson BG, Matocq M, et al. Ecology: Gene tweaking for conservation. Nature. 2013; 501: 485–486. Ref.: https://goo.gl/GtDny1
  31. Jaenisch R, Mintz B. Simian virus 40 DNA sequences in DNA of healthy adult mice derived from preimplantation blastocysts injected with viral DNA. Proc Natl Acad. 1974; 71: 1250–1254. Ref.: https://goo.gl/j3DBBS
  32. Sathasivam K, Hobbs C, Mangiarini L, Mahal A, Turmaine M, et al. Transgenic models of Huntington's disease. Philos Trans R Soc Lond B Biol Sci. 1999; 354: 963–969. Ref.: https://goo.gl/7LR7Jo
  33. Spencer LT, Humphries JE, Brantly ML; Transgenic Human Alpha 1-Antitrypsin Study Group. Antibody Response to Aerosolized Transgenic Human Alpha1-Antitrypsin. N Engl J Med. 2005; 352: 2030. Ref.: https://goo.gl/zgqVM4
  34. Schatten G, Mitalipov S. Developmental biology: Transgenic primate offspring. Nature. 2009; 459: 515–516. Ref.: https://goo.gl/nfzPnV
  35. Richard G. Genetically modified cows produce 'human' milk. 2011; Ref.: https://goo.gl/QaBjjC
  36. Wagner JS, McCracken J, Wells DN, Laible G, Targeted microRNA expression in dairy cattle directs production of -lactoglobulin-free, high-casein milk. Proceedings of the National Academy of Sciences. 2012; 109: 16811–16816. Ref.: https://goo.gl/oaZ6hT
  37. Margawati ET. Transgenic Animals: Their Benefits To Human Welfare. Actionbioscience. Retrieved June 29, 2014; Ref.: https://goo.gl/yvMECq
  38. Capecchi MR. Gene targeting in mice: functional analysis of the mammalian genome for the twenty-first century. Nat Rev Genet. 2005; 6: 507–512. Ref.: https://goo.gl/xeXiqP
  39. Cong L, Ran FA, Cox D, Lin S, Barretto R, et al. Multiplex genome engineering using CRISPR/Cas systems. Science. 2013; 339: 819–823. Ref.: https://goo.gl/QkraAU
  40. DiCarlo JE, Norville JE, Mali P, Rios X, Aach J, et al. Genome engineering in Saccharomyces cerevisiae using CRISPR-Cas systems. Nucleic Acids Res. 2013; 41: 4336–4343. Ref.: https://goo.gl/rT4FKq
  41. Mali P, Yang L, Esvelt KM, Aach J, Guell M, et al. RNA-guided human genome engineering via Cas9. Science. 2013; 339: 823–826. Ref.: https://goo.gl/keJNi3
  42. Friedland AE, Tzur YB, Esvelt KM, Colaiácovo MP, Church GM, et al. Heritable genome editing in C. elegans via a CRISPR-Cas9 system. Nat Methods. 2013; 10: 741–743. Ref.: https://goo.gl/QV1akB
  43. Xue H, Wu J, Li S, Rao MS, Liu Y. Genetic Modification in Human Pluripotent Stem Cells by Homologous Recombination and CRISPR/Cas9 System. Methods Mol Biol. 2016; 1307:173-190. Ref.: https://goo.gl/TtwWqh
  44. Esvelt KM, Wang HH. Genome-scale engineering for systems and synthetic biology. Mol Syst Biol. 2013; 9: 641. Ref.: https://goo.gl/yFaS15
  45. Ling MM, Robinson BH. Approaches to DNA mutagenesis: an overview, Analytical Biochemistry. 1997; 254: 157–178. Ref.: https://goo.gl/ayHjC4
  46. Capecchi MR. Altering the genome by homologous recombination. Science. 1989; 244: 1288-1292. Ref.: https://goo.gl/vZhv6s
  47. de Souza N. Primer: genome editing with engineered nucleases. Nat Meth. 2011; 9: 27-27. Ref.: https://goo.gl/zT5kkz
  48. Chevalier BS, Kortemme T, Chadsey MS, Baker D, Monnat RJ, et al. Design, Activity, and Structure of a Highly Specific Artificial Endonuclease. Molecular Cell. 2002; 10: 895-905. Ref.: https://goo.gl/GuDWgo
  49. Smith J, Grizot S, Arnould S, Duclert A, Epinat JC, et al. A combinatorial approach to create artificial homing endonucleases cleaving chosen sequences. Nucleic Acids Research. 2006; 34: e149. Ref.: https://goo.gl/KMHBAH
  50. Baker M. Gene-editing nucleases. Nat Meth. 2012; 9: 23-26. Ref.: https://goo.gl/qoViMw
  51. Urnov FD, Rebar EJ, Holmes MC, Zhang HS, Gregory PD. Genome editing with engineered zinc finger nucleases. Nat Rev Genet. 2010; 11: 636-646. Ref.: https://goo.gl/gYL6WE
  52. Boissel S, Jarjour J, Astrakhan A, Adey A, Gouble A, et al. megaTALs: a rare-cleaving nuclease architecture for therapeutic genome engineering. Nucleic Acids Research. 2014; 42: 2591–2601. Ref.: https://goo.gl/hEuSdm
  53. Bahrami A, Miraie-Ashtiani SR, Sadeghi M, Najafi A. miRNA-mRNA network involved in folliculogenesis interactome: systems biology approach. Reproduction. 2017; 154: 51-65. Ref.: https://goo.gl/cVfrhx
  54. Bahrami A, Miraie-Ashtiani SR, Sadeghi M, Najafi A, Ranjbar R. Dynamic modeling of folliculogenesis signaling pathways in the presence of miRNAs expression. J Ovarian Res. 2017; 10: 76. Ref.: https://goo.gl/LrNcDQ
  55. Alberghina L, Westerhoff HV. Systems Biology: Definitions and Perspectives. Topics in Current Genetics. 2005; 13: 13–30. Ref.: https://goo.gl/zYUL73
  56. Kholodenko BN, Sauro HM, eds. Systems Biology: Definitions and Perspectives. Topics in Current Genetics. 2005; 13: 357–451.
  57. Chiara R, Gerolamo L. Statistical Tools for Gene Expression Analysis and Systems Biology and Related Web Resources. In Stephen Krawetz, Bioinformatics for Systems Biology. 2009; Humana Press:. 181–205. Ref.: https://goo.gl/Jyuatn
  58. von Bertalanffy L. General System theory: Foundations, Development, Applications. George Braziller. 1976; 295. Ref.: https://goo.gl/d3kjVH
  59. Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol. 1952; 117: 500–544. Ref.: https://goo.gl/KTq2ER
  60. Noble D. Cardiac action and pacemaker potentials based on the Hodgkin-Huxley equations. Nature.1960; 188: 495–497. Ref.: https://goo.gl/4w7HtK
  61. Rosen R. A Means toward a New Holism. Science. 1968; 161: 34–35. Ref.: https://goo.gl/USthvk
  62. Hunter P. Back down to Earth: Even if it has not yet lived up to its promises, systems biology has now matured and is about to deliver its first results. EMBO Reports. 2012; 13: 408–411. Ref.: https://goo.gl/7eoD7E
  63. Zeng BJ. On the concept of system biological engineering. Communication on Transgenic Animals. 1994a; 6.
  64. Zeng BJ. Transgenic animal expression system – transgenic egg plan (goldegg plan). Communication on Transgenic Animals. 1994b; 1:11
  65. Zeng BJ. From positive to synthetic science. Communication on Transgenic Animals. 1995; 11.
  66. Tomita M, Hashimoto K, Takahashi K, Shimizu TS, Matsuzaki Y, et al. E-CELL: Software Environment for Whole Cell Simulation,. Genome Inform Ser Workshop Genome Inform. 199; P 8: 147–155. Ref.: https://goo.gl/vRcbX5
  67. Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, et al. A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell. 2012; 150: 389–401. Ref.: https://goo.gl/H9dwgF
  68. Tavassoly I. Dynamics of Cell Fate Decision Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells. Springer International Publishing. ISBN. 2015; 978-3-319-14961-5. Ref.: https://goo.gl/T5GmRj
  69. Nakano T. Molecular Communication. Cambridge. ISBN. 2013, 978-1-107-02308-6. Ref.: https://goo.gl/EsqF63
  70. Elowitz MB, Leibler S. A synthetic oscillatory network of transcriptional regulators. Nature. 2000; 403: 335–338. Ref.: https://goo.gl/Nw8FLz
  71. Gardner TS, Cantor CR, Collins JJ. Construction of a genetic toggle switch in Escherichia coli. Nature. 2000; 403: 339–342. Ref.: https://goo.gl/Bmkvyg
  72. Channon K, Bromley EH, Woolfson DN. Synthetic Biology through Biomolecular Design and Engineering. Curr Opin Struct Biol. 2008; 18: 491–498. Ref.: https://goo.gl/MpkfdD
  73. Stone M. Life Redesigned to Suit the Engineering Crowd. Microbe. 2006; 1: 566–570. Ref.: https://goo.gl/HkEBp2
  74. Zhang R, Lin Y. DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Res. 2009; 37: D455–D458. Ref.: https://goo.gl/dmRreS
  75. Juhas M, Eberl L, Glass JI. Essence of life: Essential genes of minimal genomes. Trends Cell Biol. 2011; 21: 562–568. Ref.: https://goo.gl/zt5r8q
  76. Hutchison CA, Peterson SN, Gill SR, Cline RT, White O, et al., Global transposon mutagenesis and a minimal Myco- plasma genome. Science. 1999; 286: 2165–2169. Ref.: https://goo.gl/tU1oag
  77. Goodman AL, Wu M, Gordon JI. Identifying microbial fitness determinants by insertion sequencing using genome-wide transposon mutant libraries. Nat Protoc. 2011; 6: 1969 –1980. Ref.: https://goo.gl/oh2yj1
  78. van Opijnen T, Bodi KL, Camilli A. Tn-seq: High-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms. Nat Methods. 2009; 6: 767–772. Ref.: https://goo.gl/p1bJZf
  79. Christen B, Abeliuk E, Collier JM, Kalogeraki VS, Passarelli B, et al. The essential genome of a bacterium. Mol Syst Biol. 2011; 7: 528. Ref.: https://goo.gl/PRdo5h
  80. Luo H, Lin Y, Gao F, Zhang CT, Zhang R. DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements. Nucleic Acids Res. 2014; 42: D574–D580. Ref.: https://goo.gl/15WnQ7
  81. Wetmore KM, Price MN, Waters RJ, Lamson JS, He J, et al. Rapid quantification of mutant fitness in diverse bacteria by sequencing randomly bar-coded transposons. MBio. 2015; 6: e00306-15. Ref.: https://goo.gl/cDDxcR
  82. Zhang R, Patena W, Armbruster U, Gang SS, Blum SR, et al. High-throughput genotyping of green algal mutants reveals random distribution of mutagenic insertion sites and endonucleolytic cleavage of transforming DNA. Plant Cell. 2014; 26: 1398–1409. Ref.: https://goo.gl/NwYcc2
  83. Angermayr SA, Gorchs Rovira A, Hellingwerf KJ. Metabolic engineering of cyanobacteria for the synthesis of commodity products. Trends Biotechnol. 2015; 33: 352–361. Ref.: https://goo.gl/QsVikZ
  84. Basulto D. Everything you need to know about why CRISPR is such a hot technology. Washington Post. 2015 Retrieved 5 December.
  85. Rollié S, Mangold M, Sundmacher K. Designing biological systems: Systems Engineering meets Synthetic Biology. Chemical Engineering Science. 2012; 69: 1–29. Ref.: https://goo.gl/AiVUFu
  86. Kaznessis YN. Models for synthetic biology. BMC Systems Biology. 2007; 1: 47. Ref.: https://goo.gl/ze2Sdr
  87. Masoudi-Nejad A, Bidkhori G2, Hosseini Ashtiani S2, Najafi A2, Bozorgmehr JH, et al. Cancer systems biology and modeling: microscopic scale and multiscale approaches. Semin. Cancer Biol. 2015; 30: 60–69. Ref.: https://goo.gl/LZwrco
  88. Najafi A, Bidkhori G, Bozorgmehr JH, Koch I, Masoudi-Nejad A. Genome scale modeling in systems biology: algorithms and resources. Curr. Genomics. 2014; 15: 130–159. Ref.: https://goo.gl/KXqKHi
  89. Kosuri S, Church GM. Large-scale de novo DNA synthesis: technologies and applications. Nature Methods. 2014; 11: 499–507. Ref.: https://goo.gl/SHRaZY
  90. Blight KJ, Kolykhalov AA, Rice CM. Efficient initiation of HCV RNA replication in cell culture. Science. 2000; 290: 1972–1974. Ref.: https://goo.gl/j344wQ
  91. Smith HO, Hutchison CA 3rd, Pfannkoch C, Venter JC. Generating a synthetic genome by whole genome assembly: {phi} X174 bacteriophage from synthetic oligonucleotides. Proc Natl Acad Sci USA. 2003; 100: 15440–15445. Ref.: https://goo.gl/ggr443
  92. Gibson DG, Benders GA, Andrews-Pfannkoch C, Denisova EA, Baden-Tillson H, et al. Complete chemical synthesis, assembly, and cloning of a Mycoplasma genitalium genome. Science.2008; 319: 1215–1220. Ref.: https://goo.gl/h3bj48
  93. Kramer BP, Fischer C, Fussenegger M. Biologic gates enable logical transcription control in mammalian cells. Biotechnol. Bioeng. 2004; 87: 478–484. Ref.: https://goo.gl/6NcbQL
  94. Nissim L, Bar-Ziv RH. A tunable dual-promoter integrator for targeting of cancer cells. Mol Syst Biol. 2010; 6: 444. Ref.: https://goo.gl/9KgZ27
  95. Lohmueller JJ, Armel TZ, Silver PA. A tunable zinc finger-based framework for Boolean logic computation in mammalian cells. Nucleic Acids Res. 2012; 40: 5180–5187. Ref.: https://goo.gl/aaS2eL
  96. Qi LS, Larson MH, Gilbert LA, Doudna JA, Weissman JS, et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell. 2013, 152: 1173–1183. Ref.: https://goo.gl/xE5Pzc
  97. Maeder ML, Linder SJ, Cascio VM, Fu Y, Ho QH, et al. CRISPR RNA-guided activation of endogenous human genes. Nat Methods. 2013; 10: 977–979. Ref.: https://goo.gl/zHZdkq
  98. Kiani S, Beal J, Ebrahimkhani MR, Huh J, Hall RN, et al. CRISPR transcriptional repression devices and layered circuits in mammalian cells. Nat Methods. 2014; 11: 723–726. Ref.: https://goo.gl/EE86T4
  99. Nissim L, Perli SD, Fridkin A, Perez-Pinera P, Lu TK. Multiplexed and programmable regulation of gene networks with an integrated RNA and CRISPR/ CAS toolkit in human cells. Mol Cell. 2014; 54: 698–710. Ref.: https://goo.gl/LVgoyy
  100. Fussenegger M, Morris RP, Fux C, Rimann M, von Stockar B, et al. Streptogramin-based gene regulation systems for mammalian cells. Nat Biotechnol. 2000; 18: 1203–1208. Ref.: https://goo.gl/kGbh64
  101. Gillette MU, Sejnowski TJ. Physiology: biological clocks coordinately keep life on time. Science. 2005; 309: 1196–1198. Ref.: https://goo.gl/Bs48Gc
  102. Kaasik K, Lee CC. Reciprocal regulation of haem biosynthesis and the circadian clock in mammals. Nature. 2004; 430: 467–471. Ref.: https://goo.gl/YF6FNt
  103. Covert MW, Leung TH, Gaston JE, Baltimore D. Achieving stability of lipopolysaccharide-induced NF-kappa B activation. Science. 2005; 309: 1854–1857. Ref.: https://goo.gl/wCS2Qk
  104. Lahav G. The strength of indecisiveness: oscillatory behavior for better cell fate determination. Sci STKE. 2004; 55. Ref.: https://goo.gl/ufX2Y8
  105. Tigges M, Marquez-Lago TT, Stelling J, Fussenegger M. A tunable synthetic mammalian oscillator. Nature. 2009; 457: 309–312. Ref.: https://goo.gl/GseoBW
  106. Tigges M, Dénervaud N, Greber D, Stelling J, Fussenegger M. A synthetic low-frequency mammalian oscillator. Nucleic Acids Res. 2010; 38: 2702–2711. Ref.: https://goo.gl/SZy5fZ
  107. Stricker J, Cookson S, Bennett MR, Mather WH, Tsimring LS, et al. A fast, robust and tunable synthetic gene oscillator. Nature. 2008; 456: 516–519. Ref.: https://goo.gl/LPSYtK
  108. Ausländer S, Ausländer D, Müller M, Wieland M, Fussenegger M. Programmable single-cell mammalian biocomputers. Nature. 2012; 487: 123–127. Ref.: https://goo.gl/uU1EVR
  109. Montague MG, Lartigue C, Vashee S. Synthetic genomics: potential and limitations. Current Opinion in Biotechnology. 2012; 23: 659-665. Ref.: https://goo.gl/qa5AYx
  110. Deamer A. giant step towards artificial life? Trends Biotechnol. 2005; 23: 336–338. Ref.: https://goo.gl/4RkuQ6
  111. Malyshev DA, Dhami K, Lavergne T, Chen T, Dai N, et al. A semi-synthetic organism with an expanded genetic alphabet. Nature. 2014; 509: 385–388. Ref.: https://goo.gl/98PDPH
  112. Gibson DG, Glass JI, Lartigue C, Noskov VN, Chuang RY, et al. Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome. Science. 2010; 329: 52–56. Ref.: https://goo.gl/RTEw3p
  113. Rogers-Hayden T, Pidgeon N. Reflecting upon the UK’s Citizens’ Jury on Nanotechnologies: Nano Jury UK. Nanotechnology Law & Business. 2006; 167-178. Ref.: https://goo.gl/6dmCpM
  114. Wynne B. Creating Public Alienation: Expert Cultures of Risk and Ethics on GMOs. Sci Cult (Lond). 2001; 10: 445-481. Ref.: https://goo.gl/JWk4vh
  115. Gregory R, Fischhoff B, McDaniels T. Acceptable Input: Using Decision Analysis to Guide Public Policy Deliberations. Decision Analysis. 2005; 2: 4-16. Ref.: https://goo.gl/5hhYx2


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