Skip to main content

Advertisement

Log in

Directed evolution of biocircuits using conjugative plasmids and CRISPR-Cas9: design and in silico experiments

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

Recent links between computer science and synthetic biology allow for construction of many kinds of algorithmic processes within cells, obtained either by a direct engineered design or by an evolutionary search. In the latter case, horizontal gene transfer and especially transfer of plasmids by conjugation is generally respected as a crucial source of genetic diversity in bacteria. While some previous studies focused on mutations as the crucial principle to obtain diversity for engineered evolution, here we consider conjugation itself as a tool to generate diversity from a pre-determined library of biocircuits basic components. The recent development of CRISPR-Cas9 and its programmable DNA cutting ability makes it a powerful selection tool able to remove nonfunctional biocircuits from a cell population. In this paper, we describe a framework for controlled bacterial evolution of biocircuits based on conjugation and on CRISPR-Cas9, resulting in a direct biological implementation of an evolutionary algorithm. In silico experiments provide data to estimate the computational/search capability of plasmid-based engineered evolution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Amos M et al (2015) Bacterial computing with engineered populations. Philos Trans R Soc A. doi:10.1098/rsta.2014.0218

    Google Scholar 

  • Barrick JE et al (2009) Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature 461:1243–1247

    Article  Google Scholar 

  • Beneš D, Sosík P, Rodríguez-Patón A (2015) An autonomous in vivo dual selection protocol for Boolean genetic circuits. Artif Life 21:247–260

    Article  Google Scholar 

  • Cameron DE, Bashor CJ, Collins JJ (2014) A brief history of synthetic biology. Nat Rev Microbiol 12(5):381–390

    Article  Google Scholar 

  • del Campo I et al (2012) Determination of conjugation rates on solid surfaces. Plasmid 67(2):174–182

    Article  Google Scholar 

  • Elowitz MB, Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403(6767):335–338

    Article  Google Scholar 

  • Esvelt KM, Wang HH (2012) Genome-scale engineering for systems and synthetic biology. Mol Syst Biol 9:641

    Article  Google Scholar 

  • Esvelt KM, Carlson JC, Liu DR (2011) A system for the continuous directed evolution of biomolecules. Nature 472(7344):499–503

    Article  Google Scholar 

  • Frost LS, Leplae R, Summers AO, Toussaint A, Edmonton A (2005) Mobile genetic elements: the agents of open source evolution. Nat Rev Microbiol 3:722–732

    Article  Google Scholar 

  • Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403(6767):339–342

    Article  Google Scholar 

  • Goñi-Moreno A, Amos M, de la Cruz F (2013) Multicellular computing using conjugation for wiring. PLoS ONE 8(6):e65986. doi:10.1371/journal.pone.0065986

    Article  Google Scholar 

  • Guet CC, Elowitz MB, Hsing W, Leibler S (2002) Combinatorial synthesis of genetic networks. Science 296:1466–1470

    Article  Google Scholar 

  • Harvey I, Tomko N (2010) Binomics—where metagenomics meets the binary world. In: Proceedings of the Alife XII conference, 370–377

  • Kreft J et al (2001) Individual-based modelling of biofilms. Microbiology 147:2897–2912

    Article  Google Scholar 

  • Krone SM, Lu R, Fox R, Suzuki H, Top EM (2007) Modeling the spatial dynamics of plasmid transfer and persistence. Microbiology 153:2803–2816

    Article  Google Scholar 

  • Levin BR, Stewart FM (1980) The population biology of bacterial plasmids: a priori conditions for the existence of mobilizable nonconjugative factors. Genetics 94:425–443

    MathSciNet  Google Scholar 

  • Llosa M, Gomis-Rüth FX, Coll M, De La Cruz F (2002) Bacterial conjugation: a two-step mechanism for DNA transport. Mol Microbiol 45:1–8

    Article  Google Scholar 

  • Mojica FJM, Díez-Villaseñor C, García-Martínez J, Almendros C (2009) Short motif sequences determine the targets of the prokaryotic CRISPR defence system. Microbiology 155:733–740

    Article  Google Scholar 

  • Mozhayskiy V, Tagkopoulos I (2013) Microbial evolution in vivo and in silico: methods and applications. Integr Biol 5:262–277

    Article  Google Scholar 

  • Norman A, Hansen LH, Sørensen SJ (2009) Conjugative plasmids: vessels of the communal gene pool. Philos Trans R Soc B 364:2275–2289

    Article  Google Scholar 

  • Packer MS, Liu DR (2015) Methods for the directed evolution of proteins. Nat Rev Genet 16(7):379–394

    Article  Google Scholar 

  • Prestes García A, Rodríguez-Patón A (2015a) BactoSim—an individual-based simulation environment for bacterial conjugation. PAAMS 2015:275–279

    Google Scholar 

  • Prestes García A, Rodríguez-Patón A (2015b) A preliminary assessment of three strategies for the agent-based modeling of bacterial conjugation. PACBB 2015:1–9

    Google Scholar 

  • Rodrigo G, Carrera J, Jaramillo A (2007a) Genetdes: automatic design of transcriptional networks. Bioinformatics 23:1857–1858

    Article  Google Scholar 

  • Rodrigo G, Carrera J, Jaramillo A (2007b) Asmparts: assembly of biological model parts. Syst Synth Biol 1:167–170

    Article  Google Scholar 

  • Seoane J et al (2011) An individual-based approach to explain plasmid invasion in bacterial populations. FEMS Microbiol Ecol 75:17–27

    Article  Google Scholar 

  • Simonsen L, Gordon DM, Stewart FM, Levin BR (1990) Estimating the rate of plasmid transfer: an end-point method. J Gen Microbiol 136:2319–2325

    Article  Google Scholar 

  • Sørensen SJ, Bailey M, Hansen LH, Kroer N, Wuertz S (2005) Studying plasmid horizontal transfer in situ: a critical review. Nat Rev Microbiol 3(9):700–710

    Article  Google Scholar 

  • Sternberg SH, Doudna JA (2015) Expanding the biologist’s toolkit with CRISPR-Cas9. Mol Cell 58(4):568–574

    Article  Google Scholar 

  • Tomko N, Harvey I, Philippides A (2013) Unconstrain the population: the benefits of horizontal gene transfer in genetic algorithms. SmartData, privacy meets evolutionary robotics. Springer, Berlin, pp 117–127

    Google Scholar 

  • The BACTOCOM project web page. http://bactocom.eu/

  • The PLASWIRES project web page. http://www.plaswires.eu/

  • Yokobayashi Y, Arnold FH (2005) A dual selection module for directed evolution of genetic circuits. Nat Comput 4(3):245–254

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the European Union projects BACTOCOM (248919/FP7-ICT-2009-4), PLASWIRES (612146/FP7-ICT-FET-Proactive) and EVOPROG (610730/FP7-ICT-FET-Proactive), by the National Programme of Sustainability (NPU II) of the Czech Republic, project IT4Innovations Excellence in Science—LQ1602, by the Silesian University in Opava under the Student Funding Scheme, project SGS/13/2016, and by Spanish projects TIN2012-36992 and TIN2016-81079-R.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Petr Sosík.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Beneš, D., Rodríguez-Patón, A. & Sosík, P. Directed evolution of biocircuits using conjugative plasmids and CRISPR-Cas9: design and in silico experiments. Nat Comput 16, 497–505 (2017). https://doi.org/10.1007/s11047-016-9595-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-016-9595-9

Keywords

Navigation