DiSCUS: A Simulation Platform for Conjugation Computing

  • Angel Goñi-Moreno
  • Martyn Amos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9252)


In bacterial populations, cells are able to cooperate in order to yield complex collective functionalities. Interest in population-level cellular behaviour is increasing, due to both our expanding knowledge of the underlying biological principles, and the growing range of possible applications for engineered microbial consortia. The ability of cells to interact through small signalling molecules (a mechanism known as quorum sensing) is the basis for the majority of existing engineered systems. However, horizontal gene transfer (or conjugation) offers the possibility of cells exchanging messages (using DNA) that are much more information-rich. The potential of engineering this conjugation mechanism to suit specific goals will guide future developments in this area. Motivated by a lack of computational models for examining the specific dynamics of conjugation, we present a simulation framework for its further study.

(This paper was first presented at the Spatial Computing Workshop of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Paris, France, May 5–9 2014. There were no published proceedings).


Graphic Processing Unit Horizontal Gene Transfer Microbial Consortium Simulation Platform Modular Fashion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the European Commission FP7 Future and Emerging Technologies Proactive initiative: Bio-chemistry-based Information Technology (CHEM-IT, ICT-2009.8.3), project reference 248919 (BACTOCOM).


  1. 1.
    Abelson, H., Allen, D., Coore, D., Hanson, C., Homsy, G., Knight Jr, T.F., Nagpal, R., Rauch, E., Sussman, G.J., Weiss, R.: Amorphous computing. Commun. ACM 43(5), 74–82 (2000)CrossRefGoogle Scholar
  2. 2.
    Amos, M.: Population-based microbial computing: a third wave of synthetic biology? Int. J. Gen. Syst. 43(7), 770–782 (2014)CrossRefGoogle Scholar
  3. 3.
    Andrianantoandro, E., Basu, S., Karig, D.K., Weiss, R.: Synthetic biology: new engineering rules for an emerging discipline. Mol. Syst. Biol. 2, 0028 (2006)CrossRefGoogle Scholar
  4. 4.
    Atkinson, S., Williams, P.: Quorum sensing and social networking in the microbial world. J. R. Soc. Interface 6(40), 959–978 (2009)CrossRefGoogle Scholar
  5. 5.
    Ausländer, S., Ausländer, D., Müller, M., Wieland, M., Fussenegger, M.: Programmable single-cell mammalian biocomputers. Nature 487(7405), 123–127 (2012)Google Scholar
  6. 6.
    Bacchus, W., Fussenegger, M.: Engineering of synthetic intercellular communication systems. Metab. Eng. 16, 33–41 (2013)CrossRefGoogle Scholar
  7. 7.
    Balagaddé, F.K., Song, H., Ozaki, J., Collins, C.H., Barnet, M., Arnold, F.H., Quake, S.R., You, L.: A synthetic Escherichia coli predator-prey ecosystem. Mol. Syst. Biol. 4, 187 (2008)CrossRefGoogle Scholar
  8. 8.
    Basu, S., Gerchman, Y., Collins, C.H., Arnold, F.H., Weiss, R.: A synthetic multicellular system for programmed pattern formation. Nature 434(7037), 1130–1134 (2005)CrossRefGoogle Scholar
  9. 9.
    Beal, J.: Bridging biology and engineering together with spatial computing. In: Gheorghe, M., Păun, G., Rozenberg, G., Salomaa, A., Verlan, S. (eds.) CMC 2011. LNCS, vol. 7184, pp. 14–18. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  10. 10.
    Beal, J., Bachrach, J.: Cells are plausible targets for high-level spatial languages. In: 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops. SASOW 2008, pp. 284–291. IEEE (2008)Google Scholar
  11. 11.
    Brenner, K., You, L., Arnold, F.H.: Engineering microbial consortia: a new frontier in synthetic biology. Trends Biotechnol. 26(9), 483–489 (2008)CrossRefGoogle Scholar
  12. 12.
    Cheng, A.A., Timothy, K.L.: Synthetic biology: an emerging engineering discipline. Annu. Rev. Biomed. Eng. 14, 155–178 (2012)CrossRefGoogle Scholar
  13. 13.
    de la Cruz, F., Frost, L.S., Meyer, R.J., Zechner, E.L.: Conjugative DNA metabolism in gram-negative bacteria. FEMS Microbiol. Rev. 34(1), 18–40 (2010)CrossRefGoogle Scholar
  14. 14.
    del Campo, I., Ruiz, R., Cuevas, A., Revilla, C., Vielva, L., de la Cruz, F.: Determination of conjugation rates on solid surfaces. Plasmid 67(2), 174–182 (2012)CrossRefGoogle Scholar
  15. 15.
    Emonet, T., Macal, C.M., North, M.J., Wickersham, C.E., Cluzel, P.: Agentcell: a digital single-cell assay for bacterial chemotaxis. Bioinformatics 21(11), 2714–2721 (2005)CrossRefGoogle Scholar
  16. 16.
    Garcillán-Barcia, M.P., de la Cruz, F.: Why is entry exclusion an essential feature of conjugative plasmids? Plasmid 60(1), 1–18 (2008)CrossRefGoogle Scholar
  17. 17.
    Gardner, T.S., Cantor, C.R., Collins, J.J.: Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000)CrossRefGoogle Scholar
  18. 18.
    Moreno, A.G., Amos, M.: A reconfigurable NAND/NOR genetic logic gate. BMC Syst. Biol. 6(1), 126 (2012)CrossRefGoogle Scholar
  19. 19.
    Moreno, A.G., Amos, M., de la Cruz, F.: Multicellular computing using conjugation for wiring. PLoS ONE 8(6), e65986 (2013)CrossRefGoogle Scholar
  20. 20.
    Gorochowski, T.E., Matyjaszkiewicz, A., Todd, T., Oak, N., Kowalska, K., Reid, S., Tsaneva-Atanasova, K.T., Savery, N.J., Grierson, C.S., di Bernardo, M.: BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology. PLoS ONE 7(8), e42790 (2012)CrossRefGoogle Scholar
  21. 21.
    Guantes, R., Poyatos, J.F.: Dynamical principles of two-component genetic oscillators. PLoS Comput. Biol. 2(3), e30 (2005). preprint(2006)CrossRefGoogle Scholar
  22. 22.
    Heinemann, M., Panke, S.: Synthetic biology-putting engineering into biology. Bioinformatics 22(22), 2790–279 (2006)CrossRefGoogle Scholar
  23. 23.
    Holmes, A.B., Kalvala, S., Whitworth, D.E.: Spatial simulations of myxobacterial development. PLoS Comput. Biol. 6(2), e1000686 (2010)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Izaguirre, J.A., Chaturvedi, R., Huang, C., Cickovski, T., Coffland, J., Thomas, G., Forgacs, G., Alber, M., Hentschel, G., Newman, S.A., Glazier, J.A.: Compucell, a multi-model framework for simulation of morphogenesis. Bioinformatics 20(7), 1129–1137 (2004)CrossRefGoogle Scholar
  25. 25.
    Jass, J., Schedin, S., Fällman, E., Ohlsson, J., Nilsson, U.J., Uhlin, B.E., Axner, O.: Physical properties of Escherichia coli p pili measured by optical tweezers. Biophys. J. 87(6), 4271–4283 (2004)CrossRefGoogle Scholar
  26. 26.
    Kreft, J.U., Booth, G., Wimpenny, J.W.T.: Bacsim, a simulator for individual-based modelling of bacterial colony growth. Microbiology 144(12), 3275–3287 (1998)CrossRefGoogle Scholar
  27. 27.
    Krone, S.M., Lu, R., Fox, R., Suzuki, H., Top, E.M.: Modelling the spatial dynamics of plasmid transfer and persistence. Microbiology 153(Pt 8), 2803–2816 (2007)CrossRefGoogle Scholar
  28. 28.
    Lardon, L.A., Merkey, B.V., Martins, S., Dötsch, A., Picioreanu, C., Kreft, J.-U.U., Smets, B.F.: idynomics: next-generation individual-based modelling of biofilms. Environ. Microbiol. 13(9), 2416–2434 (2011)CrossRefGoogle Scholar
  29. 29.
    Macía, J., Posas, F., Solé, R.V.: Distributed computation: the new wave of synthetic biology devices. Trends Biotechnol. 30(6), 342–349 (2012)CrossRefGoogle Scholar
  30. 30.
    Melke, P., Sahlin, P., Levchenko, A., Jönsson, H.: A cell-based model for quorum sensing in heterogeneous bacterial colonies. PLoS Comput. Biol. 6(6), e1000819 (2010)CrossRefGoogle Scholar
  31. 31.
    Ortiz, M.E., Endy, D.: Engineered cell-cell communication via DNA messaging. J. Biol. Eng. 6(1), 16 (2012)CrossRefGoogle Scholar
  32. 32.
    Regot, S., Macia, J., Conde, N., Furukawa, K., Kjellén, J., Peeters, T., Hohmann, S., de Nadal, E., Posas, F., Solé, R.: Distributed biological computation with multicellular engineered networks. Nature 469(7329), 207–211 (2011)CrossRefGoogle Scholar
  33. 33.
    Rudge, T.J., Steiner, P.J., Phillips, A., Haseloff, J.: Computational modeling of synthetic microbial biofilms. ACS Synthetic Biol. 1, 345–352 (2012)CrossRefGoogle Scholar
  34. 34.
    Seoane, J., Yankelevich, T., Dechesne, A., Merkey, B., Sternberg, C., Smets, B.F.: An individual-based approach to explain plasmid invasion in bacterial populations. FEMS Microbiol. Ecol. 75(1), 17–27 (2011)CrossRefGoogle Scholar
  35. 35.
    Tabor, J.J., Salis, H.M., Simpson, Z.B., Chevalier, A.A., Levskaya, A., Marcotte, E.M., Voigt, C.A., Ellington, A.D.: A synthetic genetic edge detection program. Cell 137(7), 1272–1281 (2009)CrossRefGoogle Scholar
  36. 36.
    Tamsir, A., Tabor, J.J., Voigt, C.A.: Robust multicellular computing using genetically encoded NOR gates and chemical ’wires’. Nature 469(7329), 212–215 (2011)CrossRefGoogle Scholar
  37. 37.
    Volfson, D., Cookson, S., Hasty, J., Tsimring, L.S.: Biomechanical ordering of dense cell populations. Proc. National Acad. Sci. 105(40), 15346–15351 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Systems Biology Program, Centro Nacional de BiotecnologíaMadridSpain
  2. 2.Informatics Research Centre, School of Computing, Mathematics and Digital TechnologyManchester Metropolitan UniversityManchesterUK

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