Abstract
Synthetic biology offers a powerful method to design and construct biological devices for human purposes. Two prominent design methodologies are currently used. Rational design adapts the design methodology of traditional engineering sciences, such as mechanical engineering. Directed evolution, in contrast, models its design principles after natural evolution, as it attempts to design and improve systems by guiding them to evolve in a certain direction. Previous work has argued that the primary difference between these two is the way they treat variation: rational design attempts to suppress it, whilst direct evolution utilizes variation. I argue that this contrast is too simplistic, as it fails to distinguish different types of variation and different phases of design in synthetic biology. I outline three types of variation and show how they influence the construction of synthetic biological systems during the design process. Viewing the two design approaches with these more fine-grained distinctions provides a better understanding of the methodological differences and respective benefits of rational design and directed evolution, and clarifies the constraints and choices that the different design approaches must deal with.
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Notes
Though Giese et al. (2013) approach the question from the perspective of different groups of synthetic biologists, their categorization of rational and evolutionary groups is equivalent to the distinction between rational design and directed evolution design approaches.
Directed evolution is framed as a more biologically inspired design method than rational design. However, one should not consider directed evolution as an exclusively biological design approach that is incompatible with engineering principles. Many engineering projects also rely on similar trial-and-error design methods (see Calcott et al. 2015), or evolutionarily based design, such as the use of genetic algorithms.
A synchronic/diachronic distinction can also be discussed in relation to design goals. In his analysis of software engineering, Calcott (2014, p. 298) defines a synchronic goal as the attempt to “make the software do something useful now,” and a diachronic goal means to “make the software easy to modify” in the future. As Calcott notes, analysis of engineering tends to focus on synchronic goals, whereas diachronic aspects are overlooked.
References
Agapakis CM, Silver PA (2009) Synthetic biology: exploring and exploiting genetic modularity through the design of novel biological networks. Mol Biosyst 5:704–713. https://doi.org/10.1039/B901484E
Andrianantoandro E, Basu S, Karig DK, Weiss R (2006) Synthetic biology: new engineering rules for an emerging discipline. Mol Syst Biol 2:1–14. https://doi.org/10.1038/msb4100073
Angov E, Hillier CJ, Kincaid RL, Lyon JA (2008) Heterologous protein expression is enhanced by harmonizing the codon usage frequencies of the target gene with those of the expression host. PLoS ONE 3:e2189. https://doi.org/10.1371/journal.pone.0002189
Arkin AP, Fletcher DA (2006) Fast, cheap and somewhat in control. Genome Biol 7:114. https://doi.org/10.1186/gb-2006-7-8-114
Blake WJ, Isaacs FJ (2004) Synthetic biology evolves. Trends Biotechnol 22:321–324. https://doi.org/10.1016/j.tibtech.2004.04.008
Bujara M, Panke S (2010) Engineering in complex systems. Curr Opin Biotechnol 21:586–591. https://doi.org/10.1016/j.copbio.2010.07.007
Calcott B (2014) Engineering and evolvability. Biol Philos 29:293–313. https://doi.org/10.1007/s10539-014-9425-3
Calcott B, Levy A, Siegal ML et al (2015) Engineering and biology: counsel for a continued relationship. Biol Theor 10:50–59. https://doi.org/10.1007/s13752-014-0198-3
Cambray G, Mutalik VK, Arkin AP (2011) Toward rational design of bacterial genomes. Curr Opin Microbiol 14:624–630. https://doi.org/10.1016/j.mib.2011.08.001
Cameron DE, Bashor CJ, Collins JJ (2014) A brief history of synthetic biology. Nat Rev Microbiol 12:381–390. https://doi.org/10.1038/nrmicro3239
Dougherty MJ, Arnold FH (2009) Directed evolution: new parts and optimized function. Curr Opin Biotech 20:486–491. https://doi.org/10.1016/j.copbio.2009.08.005
Eldar A, Elowitz MB (2010) Functional roles for noise in genetic circuits. Nature 467:167–173. https://doi.org/10.1038/nature09326
Endy D (2005) Foundations for engineering biology. Nature 438:449–453. https://doi.org/10.1038/nature04342
Gerhart J, Kirschner M (2007) The theory of facilitated variation. Proc Natl Acad Sci USA 104:8582–8589. https://doi.org/10.1073/pnas.0701035104
Gibson DG, Glass JI, Lartigue C et al (2010) Creation of a bacterial cell controlled by a chemically synthesized genome. Science 329:52–56. https://doi.org/10.1126/science.1190719
Giese B, Koenigstein S, Wigger H et al (2013) Rational engineering principles in synthetic biology: a framework for quantitative analysis and an initial assessment. Biol Theor 8:324–333. https://doi.org/10.1007/s13752-013-0130-2
Guimaraes JC, Liu CC, Arkin AP (2013) From biological parts to circuit design. In: Zhao H (ed) Synthetic biology: tools and applications. Elsevier, Amsterdam, pp 63–78
Güttinger S (2013) Creating parts that allow for rational design: synthetic biology and the problem of context-sensitivity. Stud Hist Philos Biol Biomed Sci 44:199–207. https://doi.org/10.1016/j.shpsc.2013.03.015
Haseltine EL, Arnold FH (2007) Synthetic gene circuits: design with directed evolution. Annu Rev Biophys Biom 36:1–19. https://doi.org/10.1146/annurev.biophys.36.040306.132600
Heinemann M, Panke S (2006) Synthetic biology: putting engineering into biology. Bioinformatics 22:2790–2799. https://doi.org/10.1093/bioinformatics/btl469
Heinemann M, Panke S (2009) Synthetic biology: putting engineering into bioengineering. In: Fu P, Panke S (eds) Systems biology and synthetic biology. Wiley, New York, pp 387–409
Houkes W, Vermaas PE (2010) Technical functions: on the use and design of artefacts. Springer, Dordrecht
Hu S, Wang M, Cai G, He M (2013) Genetic code-guided protein synthesis and folding in Escherichia coli. J Biol Chem 288:30855–30861. https://doi.org/10.1074/jbc.M113.467977
Kitano H (2004) Biological robustness. Nat Rev Genet 5:826–837. https://doi.org/10.1038/nrg1471
Knuuttila T, Loettgers A (2013) Basic science through engineering? Synthetic modeling and the idea of biology-inspired engineering. Stud Hist Philos Biol Biomed Sci 44:158–169. https://doi.org/10.1016/j.shpsc.2013.03.011
Knuuttila T, Loettgers A (2014) Varieties of noise: analogical reasoning in synthetic biology. Stud Hist Philos Sci 76–88. https://doi.org/10.1016/j.shpsa.2014.05.006
Kroes P (2012) Technical artefacts: creations of mind and matter—a philosophy of engineering design. Springer, Heidelberg
Krohs U, Bedau MA (2013) Interdisciplinary interconnections in synthetic biology. Biol Theor 8:313–317. https://doi.org/10.1007/s13752-013-0141-z
Kuorikoski J, Pöyhönen S (2013) Understanding nonmodular functionality: lessons from genetic algorithms. Philos Sci 80:637–649. https://doi.org/10.1086/673866
Lehner B (2008) Selection to minimise noise in living systems and its implications for the evolution of gene expression. Mol Syst Biol 4:170. https://doi.org/10.1038/msb.2008.11
Lewens T (2013) From bricolage to BioBricks™: synthetic biology and rational design. Stud Hist Philos Biol Biomed Sci 44:641–648. https://doi.org/10.1016/j.shpsc.2013.05.011
Marguet P, Balagadde F, Tan C, You L (2007) Biology by design: reduction and synthesis of cellular components and behaviour. J R Soc Interface 4:607–623. https://doi.org/10.1098/rsif.2006.0206
Marliere P (2009) The farther, the safer: a manifesto for securely navigating synthetic species away from the old living world. Syst Synth Biol 3:77. https://doi.org/10.1007/s11693-009-9040-9
Morange M (2013) Comparison between the work of synthetic biologists and the action of evolution: engineering versus tinkering. Biol Theor 8:318–323. https://doi.org/10.1007/s13752-013-0134-y
O’Malley M (2011) Exploration, iterativity and kludging in synthetic biology. C R Chim 14:406–412. https://doi.org/10.1016/j.crci.2010.06.021
Packer MS, Liu DR (2015) Methods for the directed evolution of proteins. Nat Rev Genet 16:379–394. https://doi.org/10.1038/nrg3927
Purnick PEM, Weiss R (2009) The second wave of synthetic biology: from modules to systems. Nat Rev Mol Cell Biol 10:410–422. https://doi.org/10.1038/nrm2698
Renda BA, Hammerling MJ, Barrick JE (2014) Engineering reduced evolutionary potential for synthetic biology. Mol Biosyst 10:1668–1678. https://doi.org/10.1039/C3MB70606K
Rollié S, Mangold M, Sundmacher K (2012) Designing biological systems: systems engineering meets synthetic biology. Chem Eng Sci 69:1–29. https://doi.org/10.1016/j.ces.2011.10.068
Romero PA, Arnold FH (2009) Exploring protein fitness landscapes by directed evolution. Nat Rev Mol Cell Biol 10:866–876. https://doi.org/10.1038/nrm2805
Rutherford SL (2003) Between genotype and phenotype: protein chaperones and evolvability. Nat Rev Genet 4:263–274. https://doi.org/10.1038/nrg1041
Silver PA, Way JC, Arnold FH, Meyerowitz JT (2014) Synthetic biology: engineering explored. Nature 509:166–167. https://doi.org/10.1038/509166a
Simon HA (1996) The sciences of the artificial, 3rd edn. MIT Press, Cambridge
Sleight SC, Bartley BA, Lieviant JA, Sauro HM (2010) Designing and engineering evolutionary robust genetic circuits. J Biol Eng 4:12. https://doi.org/10.1186/1754-1611-4-12
Tawfik DS (2010) Messy biology and the origins of evolutionary innovations. Nat Chem Biol 6:692. https://doi.org/10.1038/nchembio.441
Torres L, Krüger A, Csibra E et al (2016) Synthetic biology approaches to biological containment: pre-emptively tackling potential risks. Essays Biochem 60:393–410. https://doi.org/10.1042/EBC20160013
Wagner A (2005) Robustness and evolvability in living systems. Princeton University Press, Princeton
Wagner A (2008) Robustness and evolvability: a paradox resolved. Proc R Soc B 275:91–100. https://doi.org/10.1098/rspb.2007.1137
Yokobayashi Y, Weiss R, Arnold FH (2002) Directed evolution of a genetic circuit. Proc Natl Acad Sci USA 99:16587–16591. https://doi.org/10.1073/pnas.252535999
Zakeri B, Carr PA (2015) The limits of synthetic biology. Trends Biotechnol 33:57–58. https://doi.org/10.1016/j.tibtech.2014.10.008
Acknowledgements
This research has been supported by the Academy of Finland research project “Biological Knowledge through Modeling and Engineering: Epistemological and Social Aspects of Synthetic Biology” (PI: Prof. Tarja Knuuttila, grant number 272604), Finnish Cultural Foundation and Finnish Centre of Excellence in the Philosophy of the Social Sciences. I am grateful to Alkistis Elliott-Graves, Rami Koskinen, Tarja Knuuttila, Jaakko Kuorikoski, Uskali Mäki, Jani Raerinne, Anita Välikangas, and referees for this journal who provided helpful comments on previous drafts of this paper.
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Ijäs, T. Design Under Randomness: How Variation Affects the Engineering of Biological Systems. Biol Theory 13, 153–163 (2018). https://doi.org/10.1007/s13752-018-0294-x
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DOI: https://doi.org/10.1007/s13752-018-0294-x