Biological computation

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The concept of biological computation proposes that living organisms perform computations, and that as such, abstract ideas of information and computation may be key to understanding biology. [1] [2] As a field, biological computation can include the study of the systems biology computations performed by biota [3] [4] [5] [6] [7] the design of algorithms inspired by the computational methods of biota, [8] the design and engineering of manufactured computational devices using synthetic biology components [9] [10] and computer methods for the analysis of biological data, [11] elsewhere called computational biology or bioinformatics.

According to Dominique Chu, Mikhail Prokopenko, and J. Christian J. Ray, "the most important class of natural computers can be found in biological systems that perform computation on multiple levels. From molecular and cellular information processing networks to ecologies, economies and brains, life computes. Despite ubiquitous agreement on this fact going back as far as von Neumann automata and McCulloch–Pitts neural nets, we so far lack principles to understand rigorously how computation is done in living, or active, matter". [12]

Logical circuits can be built with slime moulds. [13] Distributed systems experiments have used them to approximate motorway graphs. [14] The slime mould Physarum polycephalum is able to compute high-quality approximate solutions to the Traveling Salesman Problem, a combinatorial test with exponentially increasing complexity, in linear time. [15] Fungi such as basidiomycetes can also be used to build logical circuits. In a proposed fungal computer, information is represented by spikes of electrical activity, a computation is implemented in a mycelium network, and an interface is realized via fruit bodies. [16]

See also

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References

  1. ↑ Mitchell M (2010-09-21). "Biological Computation". Computer Science Faculty Publications and Presentations.
  2. ↑ Didales, K. (2006) Living Computers - Intelligent Plastic Machines.
  3. ↑ Didales K (2007). "Being - Our New Understanding of the Meaning of Life".
  4. ↑ Bray D (2009). Wetware: a computer in every living cell . New Haven: Yale University Press. ISBN   978-0-300-14173-3.
  5. ↑ Mitchell M (2010). "Biological Computation" (PDF). Archived from the original (PDF) on 2013-10-23.
  6. ↑ "Information and entropy in biological systems". NIMBios Workshop. 2015.
  7. ↑ Dean C (2019). "How Plants Recognise Seasons Using Molecular Memory". The Royal Institution.
  8. ↑ Lamm E, Unger R (2011). Biological Computation. Chapman and Hall/CRC.
  9. ↑ Biological Computation Group at MIT - Psrg.csail.mit.edu "Biological Computation Group at MIT". Archived from the original on 2013-10-30. Retrieved 2013-10-23.
  10. ↑ Regot S, Macia J, Conde N, Furukawa K, Kjellén J, Peeters T, et al. (January 2011). "Distributed biological computation with multicellular engineered networks". Nature. 469 (7329): 207–11. Bibcode:2011Natur.469..207R. doi:10.1038/nature09679. PMID   21150900. S2CID   4389216.
  11. ↑ "Biological Computation". Microsoft Research.
  12. ↑ Chu D, Prokopenko M, Ray JC (2018-12-06). "Computation by natural systems". Interface Focus. 8 (6): 20180058. doi:10.1098/rsfs.2018.0058. PMC   6227810 .
  13. ↑ "Computing with slime: Logical circuits built using living slime molds". ScienceDaily. Retrieved 2019-12-06.
  14. ↑ 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.
  15. ↑ "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.
  16. ↑ Adamatzky A (December 2018). "Towards fungal computer". Interface Focus. 8 (6): 20180029. doi:10.1098/rsfs.2018.0029. PMC   6227805 . PMID   30443330.


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