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Programmable evolution of computing circuits in cellular populations

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Abstract

Evolution-based processes have been widely applied in bioengineering as well as in cellular computing. For example, different approaches for protein evolution have been proposed. Moreover, implementations of evolutionary heuristics to solve optimisation problems using cellular populations have been demonstrated. However, heuristics implemented with cellular populations to optimise their own response, have not yet been reported. Here we present a heuristic optimisation framework that integrates a programmable synthetic evolution into a cellular population. The proposed evolution is based on the automatic selection of computing parts to execute a given objective. These parts are implemented in the form of plasmids, which are randomly distributed among a cellular population. Further evolution of their distribution is guided by a fitness function integrated within each cell in the population. While high values of fitness functions stimulate the propagation of computing parts composing optimal solutions through the population, low fitness values trigger the apoptosis of a cell. We provide a theoretical implementation of the framework in which we demonstrate the programmable evolution of different functions with different levels of complexity. To the best of our knowledge, our approach describes the first synthetic evolution framework for programmable self-optimisation of cellular populations. It requires little human intervention without a requirement to specify the exact implementation of a biological function the population should perform. Namely, the designer only needs to define the response the population should obtain and does not need to know how this response will be implemented. The proposed computational framework is available at https://github.com/mmoskon/evolution.

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Data availability

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Code availability

The code that can be used to reproduce the results reported in the article is available at https://github.com/mmoskon/evolution.

References

  1. Packer MS, Liu DR (2015) Methods for the directed evolution of proteins. Nat Rev Genet 16(7):379–394. https://doi.org/10.1038/nrg3927

    Article  Google Scholar 

  2. Xie VC, Styles MJ, Dickinson BC (2022) Methods for the directed evolution of biomolecular interactions. Trends Biochem Sci 47(5):403–416. https://doi.org/10.1016/j.tibs.2022.01.001

    Article  Google Scholar 

  3. Esvelt KM, Carlson JC, Liu DR (2011) A system for the continuous directed evolution of biomolecules. Nature 472(7344):499–503. https://doi.org/10.1038/nature09929

    Article  Google Scholar 

  4. Miller SM, Wang T, Liu DR (2020) Phage-assisted continuous and non-continuous evolution. Nat Protoc 15(12):4101–4127. https://doi.org/10.1038/s41596-020-00410-3

    Article  Google Scholar 

  5. Eckdahl TT, Campbell AM, Heyer LJ, Poet JL, Blauch DN, Snyder NL, Atchley DT, Baker EJ, Brown M, Brunner EC et al (2015) Programmed evolution for optimization of orthogonal metabolic output in bacteria. PLoS ONE 10(2):e0118322. https://doi.org/10.1371/journal.pone.0118322

    Article  Google Scholar 

  6. Williams TC, Pretorius IS, Paulsen IT (2016) Synthetic evolution of metabolic productivity using biosensors. Trends Biotechnol 34(5):371–381. https://doi.org/10.1016/j.tibtech.2016.02.002

    Article  Google Scholar 

  7. Pennacchio A, Giampaolo F, Piccialli F, Cuomo S, Notomista E, Spinelli M, Amoresano A, Piscitelli A, Giardina P (2022) A machine learning-enhanced biosensor for mercury detection based on an hydrophobin chimera. Biosens Bioelectron 196:113696. https://doi.org/10.1016/j.bios.2021.113696

    Article  Google Scholar 

  8. Cubillos-Ruiz A, Guo T, Sokolovska A, Miller PF, Collins JJ, Lu TK, Lora JM (2021) Engineering living therapeutics with synthetic biology. Nat Rev Drug Discov 20(12):941–960. https://doi.org/10.1038/s41573-021-00285-3

    Article  Google Scholar 

  9. Scown CD, Keasling JD (2022) Sustainable manufacturing with synthetic biology. Nat Biotechnol 40(3):304–307. https://doi.org/10.1038/s41587-022-01248-8

    Article  Google Scholar 

  10. Grozinger L, Amos M, Gorochowski TE, Carbonell P, Oyarzún DA, Stoof R, Fellermann H, Zuliani P, Tas H, Goñi-Moreno A (2019) Pathways to cellular supremacy in biocomputing. Nat Commun 10(1):1–11. https://doi.org/10.1038/s41467-019-13232-z

    Article  Google Scholar 

  11. Goñi-Moreno A, Nikel PI (2019) High-performance biocomputing in synthetic biology-integrated transcriptional and metabolic circuits. Front Bioeng Biotechnol 7:40. https://doi.org/10.3389/fbioe.2019.00040

    Article  Google Scholar 

  12. Sarkar K, Bagh S (2022) Synthetic gene circuits for higher-order information processing. In: Singh V (ed) New frontiers and applications of synthetic biology. Academic Press, pp 373–395. https://doi.org/10.1016/B978-0-12-824469-2.00003-8

  13. Wang B, Kitney RI, Joly N, Buck M (2011) Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology. Nat Commun 2:508. https://doi.org/10.1038/ncomms1516

    Article  Google Scholar 

  14. Racovita A, Jaramillo A (2020) Reinforcement learning in synthetic gene circuits. Biochem Soc Trans 48(4):1637–1643. https://doi.org/10.1042/BST20200008

    Article  Google Scholar 

  15. Becerra AG, Gutiérrez M, Lahoz-Beltra R (2022) Computing within bacteria: programming of bacterial behavior by means of a plasmid encoding a perceptron neural network. BioSystems 213:104608. https://doi.org/10.1016/j.biosystems.2022.104608

    Article  Google Scholar 

  16. van der Linden AJ, Pieters PA, Bartelds MW, Nathalia BL, Yin P, Huck WT, Kim J, de Greef TF (2022) DNA input classification by a riboregulator-based cell-free perceptron. ACS Synth Biol 11(4):1510–1520. https://doi.org/10.1021/acssynbio.1c00596

    Article  Google Scholar 

  17. Moškon M, Komac R, Zimic N, Mraz M (2021) Distributed biological computation: from oscillators, logic gates and switches to a multicellular processor and neural computing applications. Neural Comput Appl 33:8923–8938. https://doi.org/10.1007/s00521-021-05711-6

    Article  Google Scholar 

  18. Karkaria BD, Treloar NJ, Barnes CP, Fedorec AJ (2020) From microbial communities to distributed computing systems. Front Bioeng Biotechnol 8:834. https://doi.org/10.3389/fbioe.2020.00834

    Article  Google Scholar 

  19. Ortiz Y, Carrión J, Lahoz-Beltrá R, Gutiérrez M (2021) A framework for implementing metaheuristic algorithms using intercellular communication. Front Bioeng Biotechnol 9:330. https://doi.org/10.3389/fbioe.2021.660148

    Article  Google Scholar 

  20. Gargantilla Becerra Á, Gutiérrez M, Lahoz-Beltra R (2021) A synthetic biology approach for the design of genetic algorithms with bacterial agents. Int J Parallel Emerg Distrib Syst 36(3):275–292. https://doi.org/10.1080/17445760.2021.1879072

    Article  Google Scholar 

  21. Wakabayashi K, Yamamura M (2005) A design for cellular evolutionary computation by using bacteria. Nat Comput 4(3):275–292. https://doi.org/10.1007/11493785_34

    Article  MathSciNet  Google Scholar 

  22. Frei T, Khammash M (2021) Adaptive circuits in synthetic biology. Curr Opin Syst Biol. https://doi.org/10.1016/j.coisb.2021.100399

  23. Moškon M, Pušnik Ž, Zimic N, Mraz M (2021) Field-programmable biological circuits and configurable (bio) logic blocks for distributed biological computing. Comput Biol Med 128:104109. https://doi.org/10.1016/j.compbiomed.2020.104109

    Article  Google Scholar 

  24. Racovita A, Prakash S, Varela C, Walsh M, Galizi R, Isalan M, Jaramillo A (2022) Engineered gene circuits with reinforcement learning allow bacteria to master gameplaying. bioRxiv. https://doi.org/10.1101/2022.04.22.489191

  25. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73. https://doi.org/10.1038/scientificamerican0792-66

    Article  Google Scholar 

  26. Gargantilla Becerra A, Lahoz-Beltra R (2020) A microbial screening in silico method for the fitness step evaluation in evolutionary algorithms. Appl Sci 10(11):3936. https://doi.org/10.3390/app10113936

    Article  Google Scholar 

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

  28. Atkinson S, Williams P (2009) Quorum sensing and social networking in the microbial world. J R Soc Interface 6(40):959–978. https://doi.org/10.1098/rsif.2009.0203

    Article  Google Scholar 

  29. Scott SR, Hasty J (2016) Quorum sensing communication modules for microbial consortia. ACS Synth Biol 5(9):969–977. https://doi.org/10.1021/acssynbio.5b00286

    Article  Google Scholar 

  30. Boo A, Amaro RL, Stan GB (2021) Quorum sensing in synthetic biology: a review. Curr Opin Syst Biol 28:100378. https://doi.org/10.1016/j.coisb.2021.100378

    Article  Google Scholar 

  31. Jung C, Bandilla P, von Reutern M, Schnepf M, Rieder S, Unnerstall U, Gaul U (2018) True equilibrium measurement of transcription factor-dna binding affinities using automated polarization microscopy. Nat Commun 9(1):1–11. https://doi.org/10.1038/s41467-018-03977-4

    Article  Google Scholar 

  32. Urrios A, Macía J, Manzoni R, Conde N, Bonforti A, De Nadal E, Posas F, Solé R (2016) A synthetic multicellular memory device. ACS Synth Biol 5(8):862–873. https://doi.org/10.1021/acssynbio.5b00252

    Article  Google Scholar 

  33. Teo JJ, Woo SS, Sarpeshkar R (2015) Synthetic biology: a unifying view and review using analog circuits. IEEE Trans Biomed Circuits Syst 9(4):453–474. https://doi.org/10.1109/TBCAS.2015.2461446

    Article  Google Scholar 

  34. Kahn D, Westerhoff HV (1991) Control theory of regulatory cascades. J Theor Biol 153(2):255–285. https://doi.org/10.1016/S0022-5193(05)80426-6

    Article  Google Scholar 

  35. He F, Murabito E, Westerhoff HV (2016) Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering. J R Soc Interface 13(117):20151046. https://doi.org/10.1098/rsif.2015.1046

    Article  Google Scholar 

  36. Ortiz ME, Endy D (2012) Engineered cell–cell communication via DNA messaging. J Biol Eng. https://doi.org/10.1186/1754-1611-6-16

    Article  Google Scholar 

  37. İnce M (2022) Automatic and intelligent content visualization system based on deep learning and genetic algorithm. Neural Comput Appl 34:2473–2493. https://doi.org/10.1007/s00521-022-06887-1

    Article  Google Scholar 

  38. Balaha HM, Saif M, Tamer A, Abdelhay EH (2022) Hybrid deep learning and genetic algorithms approach (HMB-DLGAHA) for the early ultrasound diagnoses of breast cancer. Neural Comput Appl 34:8671–8695. https://doi.org/10.1007/s00521-021-06851-5

    Article  Google Scholar 

  39. Sun J, Garibaldi JM, Hodgman C (2012) Parameter estimation using metaheuristics in systems biology: a comprehensive review. IEEE/ACM Trans Comput Biol Bioinform 9(1):185–202. https://doi.org/10.1109/TCBB.2011.63

    Article  Google Scholar 

  40. Novoa-del Toro EM, Mezura-Montes E, Vignes M, Térézol M, Magdinier F, Tichit L, Baudot A (2021) A multi-objective genetic algorithm to find active modules in multiplex biological networks. PLoS Comput Biol 17(8):e1009263. https://doi.org/10.1371/journal.pcbi.1009263

    Article  Google Scholar 

  41. Wu G, Yan Q, Jones JA, Tang YJ, Fong SS, Koffas MA (2016) Metabolic burden: cornerstones in synthetic biology and metabolic engineering applications. Trends Biotechnol 34(8):652–664. https://doi.org/10.1016/j.tibtech.2016.02.010

    Article  Google Scholar 

  42. Tsoi R, Wu F, Zhang C, Bewick S, Karig D, You L (2018) Metabolic division of labor in microbial systems. Proc Natl Acad Sci USA 115(10):2526–2531. https://doi.org/10.1073/pnas.1716888115

    Article  Google Scholar 

  43. Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. Springer. https://doi.org/10.1007/978-94-015-7744-1

  44. Chakraborty D, Rengaswamy R, Raman K (2022) Designing biological circuits: from principles to applications. ACS Synth Biol 11(4):1377–1388. https://doi.org/10.1021/acssynbio.1c00557

    Article  Google Scholar 

  45. Jones TS, Oliveira S, Myers CJ, Voigt CA, Densmore D (2022) Genetic circuit design automation with Cello 2.0. Nat Protoc 17:1097–1113. https://doi.org/10.1038/s41596-021-00675-2

    Article  Google Scholar 

  46. Stražar M, Mraz M, Zimic N, Moškon M (2013) An adaptive genetic algorithm for parameter estimation of biological oscillator models to achieve target quantitative system response. Nat Comput 13(1):119–127. https://doi.org/10.1007/s11047-013-9383-8

    Article  MathSciNet  Google Scholar 

  47. Pušnik Ž, Mraz M, Zimic N, Moškon M (2019) Computational analysis of viable parameter regions in models of synthetic biological systems. J Biol Eng 13(1):75

    Article  Google Scholar 

  48. Shen J, Liu F, Tu Y, Tang C (2021) Finding gene network topologies for given biological function with recurrent neural network. Nat Commun 12(1):1–10. https://doi.org/10.1038/s41467-021-23420-5

    Article  Google Scholar 

  49. Hiscock TW (2019) Adapting machine-learning algorithms to design gene circuits. BMC Bioinform 20(1):1–13. https://doi.org/10.1186/s12859-019-2788-3

    Article  Google Scholar 

  50. Siciliano V, DiAndreth B, Monel B, Beal J, Huh J, Clayton KL, Wroblewska L, McKeon A, Walker BD, Weiss R (2018) Engineering modular intracellular protein sensor-actuator devices. Nat Commun 9(1):1–7. https://doi.org/10.1038/s41467-018-03984-5

    Article  Google Scholar 

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Funding

The research was partially supported by the scientific research programme Pervasive Computing (P2-0359) financed by the Slovenian Research Agency. We would also like to acknowledge the support by the infrastructure program ELIXIR-SI RI-SI-2 financed by the European Regional Development Fund and by the Ministry of Education, Science and Sport of Republic of Slovenia. The funding sources had no role in the design of the study and collection, analysis, and interpretation of data nor in writing the manuscript.

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  1. Faculty of computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Miha Moškon & Miha Mraz

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  1. Miha Moškon
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Miha Moškon conceptualised the research, established the methodology, wrote the software, performed the analyses, and wrote the manuscript. Miha Mraz provided critical feedback and helped shape the research, analysis and manuscript. Both authors read and approved the final manuscript.

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Correspondence to Miha Moškon.

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Moškon, M., Mraz, M. Programmable evolution of computing circuits in cellular populations. Neural Comput & Applic 34, 19239–19251 (2022). https://doi.org/10.1007/s00521-022-07532-7

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