Desulfovibrio alaskensis G20 (DA-G20) is utilized as a model for sulfate-reducing bacteria (SRB) that are associated with corrosion issues caused by microorganisms. SRB-based biofilms are thought to be responsible for the billion-dollar-per-year bio-corrosion of metal infrastructure. Understanding the extraction of the bacterial cells' shape and size properties in the SRB-biofilm at different growth stages will assist with the design of anti-corrosion techniques. However, numerous issues affect current approaches, including time-consuming geometric property extraction, low efficiency, and high error rates. This paper proposes BiofilScanner, a Yolact-based deep learning method integrated with invariant moments to address these problems. Our approach efficiently detects and segments bacterial cells in an SRB image while simultaneously invariant moments measure the geometric characteristics of the segmented cells with low errors. The numerical experiments of the proposed method demonstrate that the BiofilmScanner is 2.1x and 6.8x faster than our earlier Mask-RCNN and DLv3+ methods for detecting, segmenting, and measuring the geometric properties of the cell. Furthermore, the BiofilmScanner achieved an F1-score of 85.28% while Mask-RCNN and DLv3+ obtained F1-scores of 77.67% and 75.18%, respectively.

Bacterial cells are sensitive to a range of external signals used to learn the environment. These incoming external signals are then processed using a Gene Regulatory Network (GRN), exhibiting similarities to modern computing algorithms. An in-depth analysis of gene expression dynamics suggests an inherited Gene Regulatory Neural Network (GRNN) behavior within the GRN that enables the cellular decision-making based on received signals from the environment and neighbor cells. In this study, we extract a sub-network of \textit{Pseudomonas aeruginosa} GRN that is associated with one virulence factor: pyocyanin production as a use case to investigate the GRNN behaviors. Further, using Graph Neural Network (GNN) architecture, we model a single species biofilm to reveal the role of GRNN dynamics on ecosystem-wide decision-making. Varying environmental conditions, we prove that the extracted GRNN computes input signals similar to natural decision-making process of the cell. Identifying of neural network behaviors in GRNs may lead to more accurate bacterial cell activity predictive models for many applications, including human health-related problems and agricultural applications. Further, this model can produce data on causal relationships throughout the network, enabling the possibility of designing tailor-made infection-controlling mechanisms. More interestingly, these GRNNs can perform computational tasks for bio-hybrid computing systems.

Biofilms pose significant problems for engineers in diverse fields, such as marine science, bioenergy, and biomedicine, where effective biofilm control is a long-term goal. The adhesion and surface mechanics of biofilms play crucial roles in generating and removing biofilm. Designing customized nano-surfaces with different surface topologies can alter the adhesive properties to remove biofilms more easily and greatly improve long-term biofilm control. To rapidly design such topologies, we employ individual-based modeling and Bayesian optimization to automate the design process and generate different active surfaces for effective biofilm removal. Our framework successfully generated ideal nano-surfaces for biofilm removal through applied shear and vibration. Densely distributed short pillar topography is the optimal geometry to prevent biofilm formation. Under fluidic shearing, the optimal topography is to sparsely distribute tall, slim, pillar-like structures. When subjected to either vertical or lateral vibrations, thick trapezoidal cones are found to be optimal. Optimizing the vibrational loading indicates a small vibration magnitude with relatively low frequencies is more efficient in removing biofilm. Our results provide insights into various engineering fields that require surface-mediated biofilm control. Our framework can also be applied to more general materials design and optimization.

Born in 1881 on a farm in Pennsylvania, Alice C. Evans dedicated her life to studying bacteria in dairy products. Early in her career, Alice became convinced that most bacteria display multicellular behavior as part of their life cycles. At the time, the morphological changes observed in bacterial life cycles created confusion among scientists. In 1928, as the first female president of the American Society for Microbiology, Alice wrote to the scientific community: “When one-celled organisms grow in masses, … individual cells influence and protect one another.” She continued, “Bacteriologists need not feel chagrinned … to admit that… forms they have considered as different genera are but stages in the life cycle of one species” ([ 1 ][1]). Nearly 100 years later, on page 71 of this issue, Qin et al. ([ 2 ][2]) make a substantial leap forward in deciphering cell dynamics in biofilms—groups of microorganisms that adhere to a surface, and each other, by excreting matrix components. In the interim period, microbiologists have learned that many bacteria organize in groups. This allows bacterial cells to achieve collectively what individuals in isolation cannot, thus conferring a selective advantage on the individuals. Multicellular behaviors help cells to migrate ([ 3 ][3]), resist antibiotic treatments ([ 4 ][4]), and protect themselves from predators ([ 5 ][5]). In recent years, microbiologists have begun to unravel the mechanisms behind these multicellular behaviors, by studying single-cell gene expression, growth rate regulation, and cell-to-cell interactions ([ 6 ][6]–[ 9 ][7]), as well as by developing tools to investigate the morphology and growth of entire bacterial biofilms ([ 10 ][8], [ 11 ][9]). A multicellular aggregate starts with a single founder cell that grows into a mature biofilm. Despite substantial progress, scientists still lack a detailed understanding of how bacterial cells are programmed to build multicellular structures. Each cell makes individual decisions—whether to divide, move, excrete chemicals, exert forces, or express extracellular matrix components—in response to its local environment. In turn, the local environment is determined by the collective decisions of all of its cells, played out as a mosaic over time in a three-dimensional (3D) space. A primary challenge to unraveling the mystery of how cells are programmed to produce a mature functional biofilm is that researchers lack the experimental tools needed to study how the dynamics of individual cells drive biofilm formation and structure. ![Figure][10] The building of biofilms A fountain-like flow of bacterial cells drives biofilm expansion. CREDIT: V. ALTOUNIAN/ SCIENCE In their elegant study, Qin et al. developed dual-view light-sheet microscopy to reconstruct single-cell trajectories in 3D Vibrio cholerae biofilms initiated by a single founder cell. This method fluorescently labeled cellular puncta, giving isotropic single-cell resolution in the biofilm with much less photobleaching than that seen with previous methods. This advance allowed the authors to carry out simultaneous imaging of 10,000 V. cholerae cells for the 16 hours it takes for the biofilm to develop, with 3-min intervals between subsequent images. This frequent imaging made it possible to track the trajectories of micrometer-sized cells, giving an unprecedented view into the behaviors of individual cells as the biofilm developed (see the figure). The measurements revealed a qualitative transition in an individual cell's behavior, in which Brownian motion changes to ballistic motion as the biofilm develops. This transition corresponds to a new phase of collective growth, when the biofilm as a whole begins its vertical expansion away from the substrate. In this phase, cells displayed two types of trajectories. Some of the cells expanded ballistically outward, whereas others became trapped at the substrate. Overall, these trajectories gave rise to a collective fountain-like flow, which transported some cells to the biofilm front, while bypassing the cells trapped at the substrate. This fountain-like flow allowed for fast lateral expansion of the biofilm. Cell tracking allowed Qin et al. to precisely quantify the dynamics of various cells, while also assessing how these dynamics differ for mutant cells that overproduce matrix components. To interpret the results, the authors built a mathematical model for the mechanics of biofilm expansion, balancing growth with substrate friction. By modeling different surface frictions and comparing the predicted cell motion with the observed cell motion, Qin et al. were able to explain the observed behavior as long as friction between the cells and surface was a dominant effect. This study of V. cholerae offers an exciting insight into how collective behavior can arise from processes operating at the single-cell level. The mechanisms uncovered with a gram-negative bacterial species likely will be generalizable across other bacterial types. For example, the qualitative transitions in biofilm expansion observed in this study have analogs in other bacterial biofilms. With the gram-positive bacterium Bacillus subtilis , a qualitative change in colony expansion is triggered by a cellular bistable switch in which cells expressing flagella produce extracellular matrices ([ 12 ][11], [ 13 ][12]). Osmolarity associated with matrix production drives colony expansion ([ 14 ][13]). More broadly, this study demonstrates the great potential for advances in imaging technology and computer vision to help unravel how collective behavior arises from the activity of individual cells and their interactions. However, there is much more going on inside a biofilm that cannot yet be seen. More complete information would allow researchers to not only reconstruct the motion of cells but also uncover their phenotypic states. Previous work on B. subtilis with fluorescent labeling of genetic components shows detailed spatial arrangement of various cell types, with cells carrying out different biological functions in distinct parts of the biofilm ([ 3 ][3], [ 15 ][14]). One can only hypothesize about the diversity of cellular types and functions inside the beautiful fountain revealed in the present study. A deeper understanding of bacterial multicellular behavior will increase our ability to treat bacterial infections, control natural bacterial communities, and engineer synthetic ones for specific purposes. 1. [↵][15]1. A. C. Evans , J. Bacteriol. 17, 63 (1929). [OpenUrl][16][FREE Full Text][17] 2. [↵][18]1. B. Qin et al ., Science 369, 71 (2020). [OpenUrl][19][Abstract/FREE Full Text][20] 3. [↵][21]1. J. van Gestel et al ., PLOS Biol. 13, e1002141 (2015). [OpenUrl][22][CrossRef][23][PubMed][24] 4. [↵][25]1. C. W. Hall, 2. T.-F. Mah , FEMS Microbiol. Rev. 41, 276 (2017). [OpenUrl][26][CrossRef][27] 5. [↵][28]1. P. K. Raghupathi et al ., Front. Microbiol. 8, 2649 (2018). [OpenUrl][29] 6. [↵][30]1. A. Dal Co, 2. S. van Vliet, 3. M. Ackermann , Philos. Trans. R. Soc. London Ser. B 374, 20190080 (2019). [OpenUrl][31] 7. 1. A. Dal Co et al ., Nat. Ecol. Evol. 4, 366 (2020). [OpenUrl][32] 8. 1. S. van Vliet et al ., Cell Syst. 6, 496 (2018). [OpenUrl][33] 9. [↵][34]1. A. Dragoš et al ., Curr. Biol. 28, 1903 (2018). [OpenUrl][35][CrossRef][36] 10. [↵][37]1. K. Drescher et al ., Proc. Natl. Acad. Sci. U.S.A. 113, E2066 (2016). [OpenUrl][38][Abstract/FREE Full Text][39] 11. [↵][40]1. R. Hartmann et al ., Nat. Phys. 15, 251 (2019). [OpenUrl][41][CrossRef][42][PubMed][43] 12. [↵][44]1. H. Vlamakis et al ., Chemtracts 20, 427 (2007). [OpenUrl][45] 13. [↵][46]1. D. B. Kearns et al ., Mol. Microbiol. 55, 739 (2005). [OpenUrl][47][CrossRef][48][PubMed][49][Web of Science][50] 14. [↵][51]1. A. Seminara et al ., Proc. Natl. Acad. Sci. U.S.A. 109, 1116 (2012). [OpenUrl][52][Abstract/FREE Full Text][53] 15. [↵][54]1. H. Vlamakis et al ., Nat. Rev. Microbiol. 11, 157 (2013). [OpenUrl][55][CrossRef][56][PubMed][57] Acknowledgments: A.D.C. and M.P.B. are supported by the National Science Foundation (DMS-1715477), Materials Research Science and Engineering Center (DMR-1420570), the Office of Naval Research (N00014-17-1-3029), and the Simons Foundation. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-9 [8]: #ref-10 [9]: #ref-11 [10]: pending:yes [11]: #ref-12 [12]: #ref-13 [13]: #ref-14 [14]: #ref-15 [15]: #xref-ref-1-1 "View reference 1 in text" [16]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2BBacteriology%26rft.stitle%253DJ.%2BBacteriol.%26rft.aulast%253DEvans%26rft.auinit1%253DA.%2BC.%26rft.volume%253D17%26rft.issue%253D2%26rft.spage%253D63%26rft.epage%253D77%26rft.atitle%253DLIFE%2BCYCLES%2BIN%2BBACTERIA.%26rft_id%253Dinfo%253Apmid%252F16559356%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [17]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6MzoiUERGIjtzOjExOiJqb3VybmFsQ29kZSI7czoyOiJqYiI7czo1OiJyZXNpZCI7czo3OiIxNy8yLzYzIjtzOjQ6ImF0b20iO3M6MjE6Ii9zY2kvMzY5LzY0OTkvMzAuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [18]: #xref-ref-2-1 "View reference 2 in text" [19]: {openurl}?query=rft.jtitle%253DScience%26rft.stitle%253DScience%26rft.aulast%253DQin%26rft.auinit1%253DB.%26rft.volume%253D369%26rft.issue%253D6499%26rft.spage%253D71%26rft.epage%253D77%26rft.atitle%253DCell%2Bposition%2Bfates%2Band%2Bcollective%2Bfountain%2Bflow%2Bin%2Bbacterial%2Bbiofilms%2Brevealed%2Bby%2Blight-sheet%2Bmicroscopy%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fscience.abb8501%26rft_id%253Dinfo%253Apmid%252F32527924%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [20]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6Mzoic2NpIjtzOjU6InJlc2lkIjtzOjExOiIzNjkvNjQ5OS83MSI7czo0OiJhdG9tIjtzOjIxOiIvc2NpLzM2OS82NDk5LzMwLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [21]: #xref-ref-3-1 "View reference 3 in text" [22]: {openurl}?query=rft.jtitle%253DPLOS%2BBiol.%26rft.volume%253D13%26rft.spage%253De1002141%26rft_id%253Dinfo%253Adoi%252F10.1371%252Fjournal.pbio.1002141%26rft_id%253Dinfo%253Apmid%252F25894589%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [23]: /lookup/external-ref?access_num=10.1371/journal.pbio.1002141&link_type=DOI [24]: /lookup/external-ref?access_num=25894589&link_type=MED&atom=%2Fsci%2F369%2F6499%2F30.atom [25]: #xref-ref-4-1 "View reference 4 in text" [26]: {openurl}?query=rft.jtitle%253DFEMS%2BMicrobiol.%2BRev.%26rft.volume%253D41%26rft.spage%253D276%26rft_id%253Dinfo%253Adoi%252F10.1093%252Ffemsre%252Ffux010%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [27]: /lookup/external-ref?access_num=10.1093/femsre/fux010&link_type=DOI [28]: #xref-ref-5-1 "View reference 5 in text" [29]: {openurl}?query=rft.jtitle%253DFront.%2BMicrobiol.%26rft.volume%253D8%26rft.spage%253D2649%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [30]: #xref-ref-6-1 "View reference 6 in text" [31]: {openurl}?query=rft.jtitle%253DPhilos.%2BTrans.%2BR.%2BSoc.%2BLondon%2BSer.%2BB%26rft.volume%253D374%26rft.spage%253D20190080%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [32]: {openurl}?query=rft.jtitle%253DNat.%2BEcol.%2BEvol.%26rft.volume%253D4%26rft.spage%253D366%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [33]: {openurl}?query=rft.jtitle%253DCell%2BSyst.%26rft.volume%253D6%26rft.spage%253D496%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [34]: #xref-ref-9-1 "View reference 9 in text" [35]: {openurl}?query=rft.jtitle%253DCurr.%2BBiol.%26rft.volume%253D28%26rft.spage%253D1903%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.cub.2018.04.046%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [36]: /lookup/external-ref?access_num=10.1016/j.cub.2018.04.046&link_type=DOI [37]: #xref-ref-10-1 "View reference 10 in text" [38]: {openurl}?query=rft.jtitle%253DProc.%2BNatl.%2BAcad.%2BSci.%2BU.S.A.%26rft_id%253Dinfo%253Adoi%252F10.1073%252Fpnas.1601702113%26rft_id%253Dinfo%253Apmid%252F26933214%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [39]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NDoicG5hcyI7czo1OiJyZXNpZCI7czoxMjoiMTEzLzE0L0UyMDY2IjtzOjQ6ImF0b20iO3M6MjE6Ii9zY2kvMzY5LzY0OTkvMzAuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [40]: #xref-ref-11-1 "View reference 11 in text" [41]: {openurl}?query=rft.jtitle%253DNat.%2BPhys.%26rft.volume%253D15%26rft.spage%253D251%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fs41567-018-0356-9%26rft_id%253Dinfo%253Apmid%252F31156716%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [42]: /lookup/external-ref?access_num=10.1038/s41567-018-0356-9&link_type=DOI [43]: /lookup/external-ref?access_num=31156716&link_type=MED&atom=%2Fsci%2F369%2F6499%2F30.atom [44]: #xref-ref-12-1 "View reference 12 in text" [45]: {openurl}?query=rft.jtitle%253DChemtracts%26rft.volume%253D20%26rft.spage%253D427%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [46]: #xref-ref-13-1 "View reference 13 in text" [47]: {openurl}?query=rft.jtitle%253DMolecular%2Bmicrobiology%26rft.stitle%253DMol%2BMicrobiol%26rft.aulast%253DKearns%26rft.auinit1%253DD.%2BB.%26rft.volume%253D55%26rft.issue%253D3%26rft.spage%253D739%26rft.epage%253D749%26rft.atitle%253DA%2Bmaster%2Bregulator%2Bfor%2Bbiofilm%2Bformation%2Bby%2BBacillus%2Bsubtilis.%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fj.1365-2958.2004.04440.x%26rft_id%253Dinfo%253Apmid%252F15661000%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [48]: /lookup/external-ref?access_num=10.1111/j.1365-2958.2004.04440.x&link_type=DOI [49]: /lookup/external-ref?access_num=15661000&link_type=MED&atom=%2Fsci%2F369%2F6499%2F30.atom [50]: /lookup/external-ref?access_num=000226457800008&link_type=ISI [51]: #xref-ref-14-1 "View reference 14 in text" [52]: {openurl}?query=rft.jtitle%253DProc.%2BNatl.%2BAcad.%2BSci.%2BU.S.A.%26rft_id%253Dinfo%253Adoi%252F10.1073%252Fpnas.1109261108%26rft_id%253Dinfo%253Apmid%252F22232655%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [53]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NDoicG5hcyI7czo1OiJyZXNpZCI7czoxMDoiMTA5LzQvMTExNiI7czo0OiJhdG9tIjtzOjIxOiIvc2NpLzM2OS82NDk5LzMwLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [54]: #xref-ref-15-1 "View reference 15 in text" [55]: {openurl}?query=rft.jtitle%253DNat.%2BRev.%2BMicrobiol.%26rft.volume%253D11%26rft.spage%253D157%26rft_id%253Dinfo%253Adoi%252F10.1038%252Fnrmicro2960%26rft_id%253Dinfo%253Apmid%252F23353768%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [56]: /lookup/external-ref?access_num=10.1038/nrmicro2960&link_type=DOI [57]: /lookup/external-ref?access_num=23353768&link_type=MED&atom=%2Fsci%2F369%2F6499%2F30.atom

Intelligence is not a quality to attribute lightly to microbes. There is no reason to think that bacteria, slime molds and similar single-cell forms of life have awareness, understanding or other capacities implicit in real intellect. But particularly when these cells commune in great numbers, their startling collective talents for solving problems and controlling their environment emerge. Those behaviors may be genetically encoded into these cells by billions of years of evolution, but in that sense the cells are not so different from robots programmed to respond in sophisticated ways to their environment. If we can speak of artificial intelligence for the latter, perhaps it's not too outrageous to refer to the underappreciated cellular intelligence of the former.