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.[1][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

  • Bacterial biofilms can emerge through the collective behavior of thousands or millions of cells[3]
  • Biofilms formed by Bacillus subtilis can use electric signals (ion transmission) to synchronize growth so that the innermost cells of the biofilm do not starve.[5]
  • Under nutritional stress bacterial colonies can organize themselves in such a way so as to maximize nutrient availability.
  • Bacteria reorganize themselves under antibiotic stress.
  • Bacteria can swap genes (such as genes coding antibiotic resistance) between members of mixed species colonies.
  • Individual cells of myxobacteria coordinate to produce complex structures or move as social entities.[3] Myxobacteria move and feed cooperatively in predatory groups, known as swarms or wolf packs, with multiple forms of signalling[6][7] and several polysaccharides play an important role.[8]
  • Populations of bacteria use quorum sensing to judge their own densities and change their behaviors accordingly. This occurs in the formation of biofilms, infectious disease processes, and the light organs of bobtail squid.[3]
  • For any bacterium to enter a host's cell, the cell must display receptors to which bacteria can adhere and be able to enter the cell. Some strains of E. coli are able to internalize themselves into a host's cell even without the presence of specific receptors as they bring their own receptor to which they then attach and enter the cell.
  • Under nutrient limitation, some bacteria transform into endospores to resist heat and dehydration.
  • A huge array of microorganisms have the ability to overcome being recognized by the immune system as they change their surface antigens so that any defense mechanisms directed against previously present antigens are now useless with the newly expressed ones.
  • In April 2020 it was reported that collectives of bacteria have a membrane potential-based form of working memory. When scientists shone light onto a biofilm of bacteria optical imprints lasted for hours after the initial stimulus as the light-exposed cells responded differently to oscillations in membrane potentials due to changes to their potassium channels.[9][10][11]

Protists

  • Individual cells of cellular slime moulds coordinate to produce complex structures or move as multicellular entities.[3] Biologist John Bonner pointed out that although slime molds are “no more than a bag of amoebae encased in a thin slime sheath, they manage to have various behaviors that are equal to those of animals who possess muscles and nerves with ganglia -- that is, simple brains.”[12]
  • The single-celled ciliate Stentor roeselii expresses a sort of "behavioral hierarchy" and can 'change its mind' if its response to an irritant does not relieve the irritant, implying a very speculative sense of 'cognition'.[13][14]
  • Paramecium, specifically P. caudatum, is capable of learning to associate intense light with stimulus such as electric shocks in its swimming medium; although it appears to be unable to associate darkness with electric shocks.[15]
  • Protozoan ciliate Tetrahymena has the capacity to 'memorize' the geometry of its swimming area. Cells that were separated and confined in a droplet of water, recapitulated circular swimming trajectories upon release. This may result mainly from a rise in intracellular calcium.[16]

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.[1] 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.[20]

See also

References

  1. 1 2 3 Rennie J (13 November 2017). "The Beautiful Intelligence of Bacteria and Other Microbes". Quanta Magazine.
  2. ↑ Ford, Brian J. (2004). "Are Cells Ingenious?" (PDF). Microscope. 52 (3/4): 135–144.
  3. 1 2 3 4 5 Chimileski S, Kolter R (2017). Life at the Edge of Sight: A Photographic Exploration of the Microbial World. Cambridge, Massachusetts: Harvard University Press. ISBN 9780674975910.
  4. ↑ Cohen, Inon, et al. (1999). "Continuous and discrete models of cooperation in complex bacterial colonies" (PDF). Fractals. 7.03 (1999) (3): 235–247. arXiv:cond-mat/9807121. doi:10.1142/S0218348X99000244. S2CID 15489293. Archived from the original (PDF) on 2014-08-08. Retrieved 2014-12-25.
  5. ↑ Beagle SD, Lockless SW (November 2015). "Microbiology: Electrical signalling goes bacterial". Nature. 527 (7576): 44–5. Bibcode:2015Natur.527...44B. doi:10.1038/nature15641. PMID 26503058.
  6. ↑ Muñoz-Dorado J, Marcos-Torres FJ, García-Bravo E, Moraleda-Muñoz A, Pérez J (2016-05-26). "Myxobacteria: Moving, Killing, Feeding, and Surviving Together". Frontiers in Microbiology. 7: 781. doi:10.3389/fmicb.2016.00781. PMC 4880591. PMID 27303375.
  7. ↑ Kaiser D (2013-11-12). "Are Myxobacteria intelligent?". Frontiers in Microbiology. 4: 335. doi:10.3389/fmicb.2013.00335. PMC 3824092. PMID 24273536.
  8. ↑ Islam ST, Vergara Alvarez I, Saïdi F, Guiseppi A, Vinogradov E, Sharma G, et al. (June 2020). "Modulation of bacterial multicellularity via spatio-specific polysaccharide secretion". PLOS Biology. 18 (6): e3000728. doi:10.1371/journal.pbio.3000728. PMC 7310880. PMID 32516311.
  9. ↑ Escalante A. "Scientists Just Brought Us One Step Closer To A Living Computer". Forbes. Retrieved 18 May 2020.
  10. ↑ "They remember: Communities of microbes found to have working memory". phys.org. Retrieved 18 May 2020.
  11. ↑ Yang CY, Bialecka-Fornal M, Weatherwax C, Larkin JW, Prindle A, Liu J, et al. (May 2020). "Encoding Membrane-Potential-Based Memory within a Microbial Community". Cell Systems. 10 (5): 417–423.e3. doi:10.1016/j.cels.2020.04.002. PMC 7286314. PMID 32343961.
  12. ↑ "The 'sultan of slime': Biologist continues to be fascinated by organisms after nearly 70 years of study". Princeton University. Retrieved 2019-12-06.
  13. ↑ "Can a single-celled organism 'change its mind'? New study says yes". phys.org. Retrieved 2019-12-06.
  14. ↑ Tang SKY; Marshall, W. F. (22 October 2018). "Cell learning". Current Biology. 28 (20): R1180–R1184. doi:10.1016/j.cub.2018.09.015. PMC 9673188. PMID 30352182. S2CID 53031600.
  15. ↑ Alipour A, Dorvash M, Yeganeh Y, Hatam G (2017-11-29). "Paramecium Learning: New Insights and Modifications". bioRxiv: 225250. doi:10.1101/225250.
  16. ↑ Kunita I, Yamaguchi T, Tero A, Akiyama M, Kuroda S, Nakagaki T (May 2016). "A ciliate memorizes the geometry of a swimming arena". Journal of the Royal Society, Interface. 13 (118): 20160155. doi:10.1098/rsif.2016.0155. PMC 4892268. PMID 27226383.
  17. ↑ "Computing with slime: Logical circuits built using living slime molds". ScienceDaily. Retrieved 2019-12-06.
  18. ↑ Adamatzky A, Akl S, Alonso-Sanz R, Van Dessel W, Ibrahim Z, Ilachinski A, et al. (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. arXiv:1203.2851. doi:10.1080/17445760.2012.685884. ISSN 1744-5760. S2CID 15534238.
  19. ↑ "Slime Mold Can Solve Exponentially Complicated Problems in Linear Time | Biology, Computer Science | Sci-News.com". Breaking Science News | Sci-News.com. Retrieved 2019-12-06.
  20. 1 2 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". Current Science. 106 (10): 1357–1363. JSTOR 24102481.
  21. ↑ Ferreira C, Kalantari Z, Salvati L, Canfora L, Zambon I, Walsh R (2019-01-01). "Chapter 6: Urban Areas". Soil Degradation, Restoration and Management in a Global Change Context. Advances in Chemical Pollution Environmental Management and Protection. Vol. 4. p. 232. ISBN 978-0-12-816415-0. Retrieved 2020-01-05.
  22. ↑ Zhang L, Lueders T (September 2017). "Micropredator niche differentiation between bulk soil and rhizosphere of an agricultural soil depends on bacterial prey". FEMS Microbiology Ecology. 93 (9). doi:10.1093/femsec/fix103. PMID 28922803.

Further reading

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