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

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.[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. ^ a b c 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. ^ a b c d e 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. 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. ^ a b 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