Synthetic biology and experimental evolutionExpanding the structure–function space

Gregor Greslehnergregor.greslehner@gmail.com

University of Salzburg

The Generalized Theory of Evolution,Duesseldorf Center for Logic and Philosophy of Science

February 2, 2018

Philosophy & biology: “two cultures”

Figure 1: The lab.https://www.uni- salzburg.at/typo3temp/pics/phil_kgw_small_70cf61e0a2.jpg

Figure 2: The library.https://www.uni- salzburg.at/typo3temp/pics/phil_kgw_small_70cf61e0a2.jpg

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Introduction

Generalized evolution & experiments• Lewontin’s necessary and sufficient conditions for evolution by natural

selection (Lewontin, 1970):• phenotypic variation• differential fitness• heredity of fitness

• How to make experiments in evolution?• How to address evolutionary questions empirically?

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Outline

1. Snythetic biology

2. Experimental evolution

3. Lessons from and for a generalized theory of evolution

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Outline

1. Snythetic biology

2. Experimental evolution

3. Lessons from and for a generalized theory of evolution

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Synthetic biology

• origin of the term (in today’s meaning) around 2000 (Hartwell et al.,1999)• evolution in the lab; apply evolutionary principles to proteins:

• phenotypic variation• differential fitness• heredity of fitness

• SELEX (Systematic Evolution of Ligands by EXponential enrichment);artificial selection

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R. Feynman’s last blackboard

Figure 3: Feynman’s last blackboard (1988).http://archives- dc.library.caltech.edu/islandora/object/ct1%3A483/datastream/JPG/view 6 / 25

Synthetic biology: Craig Venter lab

Figure 4: (Hutchison et al. 2016), Design and synthesis of a minimal bacterialgenome, Science 351, aad6253-6.

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Synthetic biology: abiogenesis

Figure 5: Miller–Urey experiment.https://upload.wikimedia.org/wikipedia/commons/5/59/MUexperiment.png

Figure 6: Dan Brown’s latest novel.https://upload.wikimedia.org/wikipedia/en/6/67/Origin_%28Dan_Brown_novel_cover%29.jpg

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1. Snythetic biology

2. Experimental evolution

3. Lessons from and for a generalized theory of evolution

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Experimental evolution: William Dallinger, 1880–1886

Figure 7: Dallinger incubator.https://upload.wikimedia.org/wikipedia/commons/a/a6/Dallinger_Incubator_J.R.Microscop.Soc.1887p193.png

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R. Lenski: long-term evolution experiment

• starting with two E. coli strains in 1988, six populations each• currently approx. 70,000 generations

Figure 8: Lenski’s flasks from June 25, 2008.https://upload.wikimedia.org/wikipedia/commons/9/99/Lenski%27s_12_long- term_lines_of_E._coli_on_25_June_2008.jpg

addressed questions (Lenski, 2017, p. 6):1 dynamics of adaptation: slow, gradual vs. rapid change and stasis?

2 repeatable evolutionary “solutions”?

3 relation of genotype–phenotype changes?11 / 25

Lessons from Lenski’s long-term evolution experiment (1)

1) Dynamics of adaptation• initial fitness trajectories with step-like dynamics• afterwards, substantial slower development

2) Repeatability of adaptation• 70% fitness increase relative to ancestor• only a few percent difference at a time• “Half of the populations evolved hypermutable phenotypes [. . . ], which

led to slightly faster rates of fitness improvement” (Lenski, 2017, p. 6)• mutations required to metabolize citrate in the presence of oxygen

only evolved once (at generation 31,000);historical contingency of previous required mutations (Blount et al.,2008)

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Lessons from Lenski’s long-term evolution experiment (2)

“Rainey and Travisano showed that populations of Pseudomonasfluorescens rapidly diversified when cultured in static flasks but did not ifthe flasks were shaken. The diversification occurred because the staticflasks generated environmental gradients, which allowed ecotypes withdifferent environmental preferences to flourish” (Lenski, 2017, p. 8)

3) Relation of genotype–phenotype changes• “over 50% of nonsynonymous mutations that arose in

nonhypermutable lineages concentrated in just 2% of theprotein-coding genes” (Lenski, 2017, p. 7)• “The genes with beneficial mutations include ones that encode

proteins with core metabolic and regulatory functions” (Lenski, 2017,p. 7)

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Changes in genetic diversity over time

Figure 9: Mutational diversity in Lenski’s E. coli population. (Barrick and Lenski,2009, Figure 5, p. 9)

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Directed evolution (1)

“Directed evolution was originally developed to alter protein function, onceit became clear that rational protein engineering methods were limited byour incomplete knowledge of how protein sequences encode function.”(Peisajovich, 2012, p. 199)

The central dogma of molecular biology• DNA makes RNA makes protein

• “structure determines function”“central dogma” “structure–function dogma”︷ ︸︸ ︷ ︷ ︸︸ ︷

nucleotide sequencedetermines−−−−−−→ amino acid sequence

determines−−−−−−→ protein structure

determines−−−−−−→ function︸ ︷︷ ︸

“Anfinsen’s dogma”

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Directed evolution (2)

Figure 10: Directed evolution. (Packer and Liu, 2015, Figure 1, p. 380)

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Evolution and the structure–function space

• different notions of protein structure and function• divergent evolution: similar structures with different functions• convergent evolution: different structures with similar functions• source of variation: error-prone PCR or directed mutagenesis• artificial selection

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The evolution of molecular biology into systems biology?

• tendencies to become more quantitative and predictive: systemsbiology, synthetic biology• some methods that are needed in biology already exist

(⇒ interdisciplinarity)• in silico implementations• bioinformatics, data science, philosophy of science• simple laws like in 20th century physics is not the aim

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Towards systems and synthetic biology?

“is the buzz about systems and synthetic biology just that, a buzz, or arewe seeing substantial shifts in paradigms and research programmes”?(Potthast, 2009, p. S42)

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“Can a biologist fix a radio?” (Lazebnik, 2002)

Figure 11: Inside the radio.(Lazebnik 2002, Figure 2)

Figure 12: The biologist’s (A) &engineer’s (B) view of a radio. (Lazebnik 2002,Figure 3)

Src . . . Serendipitously Recovered ComponentMic . . . Most Important ComponentRic . . . Really Important ComponentU-Mic . . . Undoubtedly Most Important Component

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Typical “circuit diagrams” in biology

Figure 13: mTOR signaling.http://jcs.biologists.org/content/joces/122/20/3589/F1.large.jpg?width=800&height=600&carousel=1 21 / 25

1. Snythetic biology

2. Experimental evolution

3. Lessons from and for a generalized theory of evolution

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Lessons from and for a generalized theory of evolution (1)

Figure 14: The most probable evolutionary trajectories. (Weinreich et al., 2006,Figure 2, p. 113)

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Lessons from and for a generalized theory of evolution (2)

• “[H]istory includes too much contingency, or shaping of presentresults by long chains of unpredictable antecedent states, rather thanimmediate determination by timeless laws of nature” (Gould, 1994).• “replaying the protein tape of life”: only a limited number of pathways

lead to altered protein function;constraints, even predictability?• “Darwinian evolution can follow only very few mutational paths to fitter

proteins” (Weinreich et al., 2006)• “replaying the protein tape of life [. . . ] might be surprisingly repetitive.

It remains to be seen whether [. . . similar constraints exist for. . . ]Darwinian evolution at larger scales of biological organization”(Weinreich et al., 2006, p. 113)

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Conclusion

• Synthetic biology and experimental evolution can learn a lot from ageneralized theory of evolution, and vice versa.• A generalized theory of evolution is essential for understanding

evolutionary dynamics theoretically and conceptually.• Synthetic biology and experimental evolution allows us to observe

and manipulate evolutionary systems in the lab.• This allows us to address conceptual questions about evolutionary

processes and dynamics with empirical results.

Open questions• What are structural and functional entities in cultural evolution?• Do these also limit the structure–function space in a way that is

similar to the structure–function space of proteins?

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Thank you!

https://plato.stanford.edu/entries/systems- synthetic- biology/Figure3.png

Barrick, J. and Lenski, R. (2009). Genome-wide mutational diversity in an evolving population ofescherichia coli . Cold Spring Harbor Symposia on Quantitative Biology, Volume LXXIV.doi:10.1101/sqb.2009.74.018.

Blount, Z. D., Borland, C. Z., and Lenski, R. E. (2008). Historical contingency and the evolution of akey innovation in an experimental population of escherichia coli . Proceedings of the NationalAcademy of Sciences, 105(23):7899–7906. doi:10.1073/pnas.0803151105.

Hartwell, L. H., Hopfield, J. J., Leibler, S., and Murray, A. W. (1999). From molecular to modular cellbiology. Nature, 402(6761 Suppl.):C47–C52. doi:10.1038/35011540.

Lazebnik, Y. (2002). Can a biologist fix a radio?—Or, what I learned while studying apoptosis. CancerCell, 2:179–182. doi:10.1016/S1535-6108(02)00133-2.

Lenski, R. E. (2017). What is adaptation by natural selection? Perspectives of an experimentalmicrobiologist. PLOS Genetics, 13(4):e1006668. doi:10.1371/journal.pgen.1006668.

Lewontin, R. C. (1970). The units of selection. Annual Review of Ecology and Systematics, 1:1–18.doi:10.1146/annurev.es.01.110170.000245.

Packer, M. S. and Liu, D. R. (2015). Methods for the directed evolution of proteins. Nature ReviewGenetics, 16:379–394. doi:doi:10.1038/nrg3927.

Peisajovich, S. G. (2012). Evolutionary synthetic biology. ACS Synth Biol, 1(6):199–210.doi:10.1021/sb300012g.

Potthast, T. (2009). Paradigm shifts versus fashion shifts? EMBO reports, 10:S42–S45.doi:10.1038/embor.2009.130.

Weinreich, D. M., Delaney, N. F., DePristo, M. A., and Hartl, D. L. (2006). Darwinian evolution canfollow only very few mutational paths to fitter proteins. Science, 312:111–114.doi:10.1126/science.1123539.