Microbial intelligence explained

Microbial intelligence (known as bacterial intelligence) is the intelligence shown by microorganisms. The concept encompasses complex adaptive behavior shown by single cells, and altruistic or cooperative behavior in populations of like or unlike cells mediated by chemical signalling that induces physiological or behavioral changes in cells and influences colony structures.[1]

Complex cells, like protozoa or algae, show remarkable abilities to organize themselves in changing circumstances.[2] Shell-building by amoebae reveals complex discrimination and manipulative skills that are ordinarily thought to occur only in multicellular organisms.

Even bacteria can display more behavior as a population. These behaviors occur in single species populations, or mixed species populations. Examples are colonies or swarms of myxobacteria, quorum sensing, and biofilms.[3]

It has been suggested that a bacterial colony loosely mimics a biological neural network. The bacteria can take inputs in form of chemical signals, process them and then produce output chemicals to signal other bacteria in the colony.

Bacteria communication and self-organization in the context of network theory has been investigated by Eshel Ben-Jacob research group at Tel Aviv University which developed a fractal model of bacterial colony and identified linguistic and social patterns in colony lifecycle.[4]

Examples of microbial intelligence

Bacterial

Protists

Applications

Bacterial colony optimisation

Bacterial colony optimization is an algorithm used in evolutionary computing. The algorithm is based on a lifecycle model that simulates some typical behaviors of E. coli bacteria during their whole lifecycle, including chemotaxis, communication, elimination, reproduction, and migration.

Slime mold computing

Logical circuits can be built with slime moulds.[17] Distributed systems experiments have used them to approximate motorway graphs.[18] The slime mould Physarum polycephalum is able to solve the Traveling Salesman Problem, a combinatorial test with exponentially increasing complexity, in linear time.[19]

Soil ecology

Microbial community intelligence is found in soil ecosystems in the form of interacting adaptive behaviors and metabolisms.[20] According to Ferreira et al., "Soil microbiota has its own unique capacity to recover from change and to adapt to the present state[...] [This] capacity to recover from change and to adapt to the present state by altruistic, cooperative and co-occurring behavior is considered a key attribute of microbial community intelligence."[21]

Many bacteria that exhibit complex behaviors or coordination are heavily present in soil in the form of biofilms. Micropredators that inhabit soil, including social predatory bacteria, have significant implications for its ecology. Soil biodiversity, managed in part by these micropredators, is of significant importance for carbon cycling and ecosystem functioning.[22]

The complicated interaction of microbes in the soil has been proposed as a potential carbon sink. Bioaugmentation has been suggested as a method to increase the 'intelligence' of microbial communities, that is, adding the genomes of autotrophic, carbon-fixing or nitrogen-fixing bacteria to their metagenome.

See also

Further reading

External links

Notes and References

  1. Web site: The Beautiful Intelligence of Bacteria and Other Microbes. Rennie J . Quanta Magazine. 13 November 2017.
  2. Ford, Brian J.. Are Cells Ingenious?. Microscope. 52. 3/4. 135–144. 2004.
  3. Book: Life at the Edge of Sight: A Photographic Exploration of the Microbial World. Chimileski S, Kolter R . Harvard University Press. 2017. 9780674975910. Cambridge, Massachusetts.
  4. Cohen, Inon . 1999 . Continuous and discrete models of cooperation in complex bacterial colonies . Fractals . 7.03 (1999) . 3 . 235–247 . etal . 10.1142/S0218348X99000244 . cond-mat/9807121 . 15489293 . 2014-12-25 . https://web.archive.org/web/20140808000513/http://tamar.tau.ac.il/~eshel/papers/fractals.pdf . 2014-08-08 . dead .
  5. Beagle SD, Lockless SW . Microbiology: Electrical signalling goes bacterial . Nature . 527 . 7576 . 44–5 . November 2015 . 26503058 . 10.1038/nature15641 . free . 2015Natur.527...44B .
  6. Muñoz-Dorado J, Marcos-Torres FJ, García-Bravo E, Moraleda-Muñoz A, Pérez J . Myxobacteria: Moving, Killing, Feeding, and Surviving Together . Frontiers in Microbiology . 7 . 781 . 2016-05-26 . 27303375 . 4880591 . 10.3389/fmicb.2016.00781 . free .
  7. Kaiser D . Are Myxobacteria intelligent? . Frontiers in Microbiology . 4 . 335 . 2013-11-12 . 24273536 . 3824092 . 10.3389/fmicb.2013.00335 . free .
  8. Islam ST, Vergara Alvarez I, Saïdi F, Guiseppi A, Vinogradov E, Sharma G, Espinosa L, Morrone C, Brasseur G, Guillemot JF, Benarouche A, Bridot JL, Ravicoularamin G, Cagna A, Gauthier C, Singer M, Fierobe HP, Mignot T, Mauriello EM . 6 . Modulation of bacterial multicellularity via spatio-specific polysaccharide secretion . PLOS Biology . 18 . 6 . e3000728 . June 2020 . 32516311 . 7310880 . 10.1371/journal.pbio.3000728 . free .
  9. News: Escalante A . Scientists Just Brought Us One Step Closer To A Living Computer. . 18 May 2020 . Forbes . en.
  10. News: They remember: Communities of microbes found to have working memory . 18 May 2020 . phys.org . en.
  11. Yang CY, Bialecka-Fornal M, Weatherwax C, Larkin JW, Prindle A, Liu J, Garcia-Ojalvo J, Süel GM . 6 . Encoding Membrane-Potential-Based Memory within a Microbial Community . Cell Systems . 10 . 5 . 417–423.e3 . May 2020 . 32343961 . 10.1016/j.cels.2020.04.002 . 7286314 .
  12. Web site: The 'sultan of slime': Biologist continues to be fascinated by organisms after nearly 70 years of study. Princeton University. en. 2019-12-06.
  13. Web site: Can a single-celled organism 'change its mind'? New study says yes. phys.org. en-us. 2019-12-06.
  14. Cell learning. Current Biology. 22 October 2018. 28. 20. R1180–R1184. 10.1016/j.cub.2018.09.015. 30352182. en-us. Tang SKY. Marshall. W. F.. 9673188 . 53031600. free. 2018CBio...28R1180T .
  15. Alipour A, Dorvash M, Yeganeh Y, Hatam G . 2017-11-29. Paramecium Learning: New Insights and Modifications. bioRxiv. en. 225250. 10.1101/225250. free.
  16. Kunita I, Yamaguchi T, Tero A, Akiyama M, Kuroda S, Nakagaki T . A ciliate memorizes the geometry of a swimming arena . Journal of the Royal Society, Interface . 13 . 118 . 20160155 . May 2016 . 27226383 . 4892268 . 10.1098/rsif.2016.0155 .
  17. Web site: Computing with slime: Logical circuits built using living slime molds. ScienceDaily. en. 2019-12-06.
  18. Adamatzky A, Akl S, Alonso-Sanz R, Van Dessel W, Ibrahim Z, Ilachinski A, Jones J, Kayem AV, Martínez GJ, De Oliveira P, Prokopenko M . 6 . 2013-06-01. Are motorways rational from slime mould's point of view?. International Journal of Parallel, Emergent and Distributed Systems. 28. 3. 230–248. 10.1080/17445760.2012.685884. 1744-5760. 1203.2851. 15534238 .
  19. Web site: Slime Mold Can Solve Exponentially Complicated Problems in Linear Time Biology, Computer Science Sci-News.com. Breaking Science News Sci-News.com. en-US. 2019-12-06.
  20. Agarwal L, Qureshi A, Kalia VC, Kapley A, Purohit HJ, Singh RN . 2014-05-25. Arid ecosystem: Future option for carbon sinks using microbial community intelligence . 24102481 . Current Science. 106. 10. 1357–1363.
  21. Book: Ferreira C, Kalantari Z, Salvati L, Canfora L, Zambon I, Walsh R . Chapter 6: Urban Areas . Soil Degradation, Restoration and Management in a Global Change Context.. 2019-01-01. https://www.researchgate.net/publication/336816466. Advances in Chemical Pollution Environmental Management and Protection. 4. 232. 978-0-12-816415-0. 2020-01-05.
  22. Zhang L, Lueders T . Micropredator niche differentiation between bulk soil and rhizosphere of an agricultural soil depends on bacterial prey . FEMS Microbiology Ecology . 93 . 9 . September 2017 . 28922803 . 10.1093/femsec/fix103 . free .