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Bacterial Stigmergy: An Organising Principle of Multicellular Collective Behaviours of Bacteria

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The self-organisation of collective behaviours often manifests as dramatic patterns of emergent large-scale order. This is true for relatively "simple" entities such as microbial communities and robot "swarms," through to more complex self-organised systems such as those displayed by social insects, migrating herds, and many human activities. The principle of stigmergy describes those self-organised phenomena that emerge as a consequence of indirect communication between individuals of the group through the generation of persistent cues in the environment. Interestingly, despite numerous examples of multicellular behaviours of bacteria, the principle of stigmergy has yet to become an accepted theoretical framework that describes how bacterial collectives self-organise. Here we review some examples of multicellular bacterial behaviours in the context of stigmergy with the aim of bringing this powerful and elegant self-organisation principle to the attention of the microbial research community.
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Review Article
Bacterial Stigmergy: An Organising Principle of
Multicellular Collective Behaviours of Bacteria
Erin S. Gloag, Lynne Turnbull, and Cynthia B. Whitchurch
e ithree Institute, University of Technology Sydney, P.O. Box 123, Broadway, Sydney, NSW 2007, Australia
Correspondence should be addressed to Cynthia B. Whitchurch; cynthia.whitchurch@uts.edu.au
Received  August ; Revised  December ; Accepted  December 
Academic Editor: Pascal Vallotton
Copyright ©  Erin S. Gloag et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
e self-organisation of collective behaviours oen manifests as dramatic patterns of emergent large-scale order. is is true for
relatively “simple” entities such as microbial communities and robot “swarms,” through to more complex self-organised systems
such as those displayed by social insects, migrating herds, and many human activities. e principle of stigmergy describes those
self-organised phenomena that emerge as a consequence of indirect communication between individuals of the group through the
generation of persistent cues in the environment. Interestingly, despite numerous examples of multicellular behaviours of bacteria,
the principle of stigmergy has yet to become an accepted theoretical framework that describes how bacterial collectives self-organise.
Here we review some examples of multicellular bacterial behaviours in the context of stigmergy with the aim of bringing this
powerful and elegant self-organisation principle to the attention of the microbial research community.
1. Introduction
e emergence of self-organised patterns in living and non-
living systems has fascinated scientists for centuries. In bio-
logical systems, the coordination of group behaviours and the
subsequent emergence of large-scale patterns are inherently
more complex than that which spontaneously emerges in
nonliving systems [], involving an interplay of physical,
chemical, and biological interactions, both physiological and
behavioural, that have been honed through natural selection
[]. Many self-organised phenomena in both biotic and
abiotic systems can be explained by the principle of stig-
mergy, a concept that describes self-organised systems that
arisethroughanindividualofthecollectiveinuencingthe
movement or behaviour of other individuals at a later point
in time through the generation of persistent cues within the
environment [,].
e concept of stigmergy was rst introduced by the
entomologist Grass´
e in  to explain the construction of
termite colonies []. is powerful concept, for the rst time,
explained how apparently random and independent move-
ments of an individual could result in the transfer of persis-
tent information locally, thereby manifesting as coordinated
behaviour at a global level [,]. e principle of stigmergy
has since been employed to describe a vast array of group
activities such as the laying-down of pheromone trails by
foraging ants, herd migration in animals, and various aspects
of human activities including the following of hiking trails
and pedestrian footpaths [] as well as articial systems
such as “swarm intelligence” within robotics and computing
[]. Interestingly, even the development of multicellular
tissues has been described as a stigmergic phenomenon in
which chemical cues are embedded in extracellular matrix
material []. As other scientic elds such as social sci-
ences, technology, and computer sciences began adopting the
concept of stigmergy to help describe and explain various
phenomena of emergent behaviour or properties, various
types or categories of stigmergy have been described in
an attempt to better understand the dierent stimuli and
responses which inuence the stigmergic interactions of the
agents in these systems.
Sematectonic and marker-based stigmergy dierentiate
between the forms of communication, that is, the types of
signals that initiate a response or behaviour change [,,
]. Sematectonic stigmergy was rst coined by Wilson and
describes communication through physical changes to the
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Scientifica
Volume 2015, Article ID 387342, 8 pages
http://dx.doi.org/10.1155/2015/387342
Scientica
environment [], for example, the following of trails by
herd animals, pedestrians, and hikers []andtheconstruc-
tion of wasp nests, where the development of the physical
structure acts as cues for the next steps or process in the
design [,]. In contrast, marker-based stigmergy refers to
communication through the deposition of chemical signals
within the environment [,,], for example, the following
of pheromone trails by ants aiding in their food foraging
behaviour []. A further distinction between these two vari-
ations is that for sematectonic stigmergy the communicative
information tends to provide a direct contribution to the
task/emergent property, whereas in marker-based stigmergy
the cues do not take direct action but rather inuence
subsequent behaviour, stimulating self-organisation for its
eective completion [].
Quantitative and qualitative stigmergy are other forms
that were introduced by eraulaz and Bonabeau to describe
the dierent stimulus signalling and response outcomes [].
Quantitative stigmergy describes a system where an individ-
ual’s response to a stimulus intensies the stimulus, with the
nature of these stigmergic systems oen leading to positive
feedback [,]. Here again the following of pheromone
trails by ants provides an example of quantitative stigmergy,
whereby continuous ant trac along specic pheromone
trails results in the deposition of more pheromone, thereby
amplifying the signal and attracting further ants to these
trails,whichinturnlaydownmorepheromone.Qualitative
stigmergy refers to self-organising systems that arise from
an individual responding to a stimulus that in turn creates a
qualitatively dierent stimulus, thereby triggering a separate
response [,]. e building of a wasp nest provides an exam-
ple of qualitative stigmergy as the growing structure provides
dierent signals and cues based on the stage of construction
and results in distinct building behaviours [,,]. It has
also been recognised that both sematectonic and marker-
based signals can initiate either quantitative or qualitative
responses []. Finally, passive and active stigmergy have
been described; however, as these two variations have for the
most part been applied only to collective swarm intelligence
in robotics [,], they will not be discussed here.
Whilst there are many examples of self-organised mul-
ticellular behaviours of bacteria, the concept of stigmergy
has rarely been used to describe these phenomena. Here we
review some examples of bacterial collective behaviours that
may be described in the context of the organising principle of
stigmergy.
2. Bacterial Swarms
Many species of bacteria are able to actively migrate across
surfaces via a number of dierent mechanisms including
twitching, gliding, and agella-mediated swarming motili-
ties. ese motilities can facilitate the surface translocation
of individual cells but can also manifest as highly organised
multicellular “swarms” that enable rapid expansion of the
bacterial communities. Here we show that stigmergy explains
many of these “swarming” behaviours of bacteria.
Twitching motility is a mechanism of surface transloca-
tion that is powered by the extension, surface binding, and
subsequent retraction of type IV pili (tfp) [,]. Under
appropriate conditions, twitching motility is as a complex,
highly coordinated multicellular behaviour that leads to the
active expansion of the bacterial community across solidied
nutrient media []. It has been found that when Pseu-
domonas aeruginosa is cultured at the interface of nutrient
media that has been solidied with .% gellan gum and
an abiotic surface such as plastic or glass, twitching motility
can lead to the formation of highly structured multicellular
communities at the interstitial space. ese have several char-
acteristic micromorphological features including large van-
guard ras of highly aligned cells at the leading edge behind
which there is an intricate, interconnected lattice-like net-
work of trails of cells (Figure (a);[]). Semmler et al. pro-
posed that as the vanguard ras migrated across the surface
of the semisolid nutrient media, they created some form of
trail along which ensuing cells preferentially followed [].
We have shown recently that the emergence of the inter-
connected network of trails is likely to occur due to the for-
mation of an interconnected furrow system in the underlying
semisolid media (Figure (b);[,]). We recently applied
the concept of stigmergy to describe the emergent self-
organisation of P. a e r u g i n o s a interstitial communities that
occurs as a consequence of cells creating and travelling within
the furrow network []. To our knowledge this was the rst
description of stigmergic behaviour driven by physical cues
within the environment. We hypothesised that as cellular
aggregates migrated across the media surface, they forged
a furrow along which ensuing cells migrated and in doing
so continued to remodel the substratum thereby rening
the furrow system [,]. We proposed that the furrows
physically conne cells thereby directing cell movement and
contributing to the emergence of the intricate interconnected
network of cellular trails that are a characteristic feature
of these biolms (Figure (a);[,]). is is an example
of sematectonic and quantitative stigmergy and is highly
reminiscent of the physical trail following displayed by
animals during herd migrations and by humans following
hiking trails and pedestrian footpaths [,].
Interestingly, some bacteria from diverse genera display
an “agar pitting” phenotype which can be used as an identi-
fying feature for these species []. One such organism,
Dichelobacter nodosus, also produces striking interconnected
pattern networks reminiscent of that of P. a e r u g i n o s a when
they are grown at the interstitial space between the petri
dish and media []. However, whether this emergent pattern
arises due to the corrosion of the agar during biolm
expansion, creating furrows that guide the movements of
the bacteria remains to be determined. e agar pitting
phenotypes of both D. nodosus and Moraxella bovis have been
correlated with the presence of tfp. It has been speculated that
the agar polysaccharides may act as ligands to which the tfp
bind and that the physical interaction of the tfp with the agar
may be responsible for the agar pitting phenotype []. It is
interesting to speculate that the formation of furrow networks
may constitute a more global mechanism for the stigmergic
organisation of bacterial communities.
We have recently also identied a role for extracellular
DNA (eDNA) in coordinating the collective behaviour of
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(a)
0 100 200
(nm)
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100.0
0.0
(nm)
60.0
45.0
30.0
15.0
0.0 0.0
0.0
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(𝜇m)
(b) (c)
(d) (e)
F : Stigmergic self-organisation of bacterial communities. (a) Pseudomonas aeruginosa interstitial biolm imaged using phase contrast
microscopy depicting the emergent pattern formation. At the advancing edge are ras of cells that initiate biolm expansion, behind which
there is an interconnected lattice-like network of cellular trails. Scale bar indicates  𝜇m. (b) D rendered image of the interconnected furrow
network underlying the P. a e r u g i n o s a interstitial biolms imaged using atomic force microscopy(AFM) within the lattice-like network. Height
scale is relative. (c) P. a e r u g i n o s a expressing cyan uorescent protein (CFP; blue) interstitial biolms were grown on media supplemented
with the cell impermeant nucleic acid dye TOTO- to visualize eDNA (yellow) and imaged using OMX BLAZE wide-eld microscopy. Scale
bar indicates  𝜇m. Swarming communities of (d) Pr. vulgaris and (e) M. xanthus grown on semisolid nutrient media and imaged using phase
contrast microscopy revealing the phase bright trails routinely observed at the leading edge. Scale bar is  𝜇m.
Scientica
P. a e r u g i n o s a cells undergoing twitching motility-mediated
biolm expansion []. We observed that P. a e r u g i n o s a
interstitial biolms contain eDNA distributed either as a
ne coating of the cells or as concentrated, punctate foci
from which thin tendrils radiated in the overall direction
of the motion of cells (Figure (c);[]). Removal of this
eDNA, through the incorporation of the eDNA-degrading
enzyme DNaseI into the solidied nutrient media, resulted
in the abrogation of the characteristic interconnected pattern
network of these biolms [].
To understand the role of eDNA within these biolms
we employed a computer vision and cell tracking analysis
pipeline [,,]toquantitatethebehaviourofthe
individual cells in the absence and presence of DNaseI.
Interrogation of the resulting image informatics database
revealed that eDNA facilitates twitching motility-mediated
biolm expansion by enabling more frequent movements of
individual cells, thereby resulting in more sustained motion
and greater distances traversed by individual cells over longer
time periods. ese analyses also revealed that eDNA is
required for maintaining coherent cell behaviour and cell
alignment over time []. Previous reports have identied
that P. a e r u g i n o s a tf p bind to DNA []andthatP. a e r u g i n o s a
cells spontaneously pneumatically orient with the direction
of extended DNA chains in a matrix of aligned, concentrated
DNA []. We proposed that the bed of aligned eDNA
molecules within P. a e r u g i n o s a interstitial biolms maintains
cell orientations by aligning cells to the thin strands of
eDNA and that eDNA provides a substrate for optimal tfp
binding, consequently enabling more frequent tfp-powered
translocations, ensuring smooth trac ow within the trail
network and a consistent supply of cells to the leading
edge of the expanding biolm []. We also propose that
eDNA serves as an intercellular “glue” that binds the cells
together within vanguard ra assemblages thereby facilitating
coherent cell movements to power migration of leading edge
ras into virgin territory [].
e ability of eDNA to promote cohesive group behaviour
during active biolm expansion is an example of sematec-
tonic stimergy. It could be further argued that the redistribu-
tion of eDNA through the biolm is also an example of quan-
titative stigmergy as continued cellular migration through the
concentrated regions of eDNA results in the production of
ne tendrils of eDNA aligned with the direction of bacterial
migration which then directs and maintains the alignment of
ensuing cells along these eDNA strands thereby maintaining
trac ow in the overall direction of travel of the preceding
cells [].
Flagella-mediated swarming motility of Proteus spp. leads
to the formation of rapidly expanding colonies grown on agar
that are characterised by a repeated concentric circle pattern
that extends across the swarm. is patterning is attributed to
continuous rounds of cell dierentiation, where the normal
rod cells, which are largely nonmotile, dierentiate into long,
hyperagellated swarmer cells. As a collective these swarmer
cells rapidly migrate across the surface until they dierentiate
back to the nonmotile normal cells resulting in consolidation
and the formation of the observed ring pattern [,]. e
agella of Proteus swarmer cells interweave with agella from
the same cell and with those of neighbouring cells, forming a
connected and highly synchronised swarming front that aids
in the rapid expansion by these colonies []. e secretion
of an extracellular slime has been found to facilitate the
collective swarming behaviour of Pr. mirabilis. At the leading
edge of Pr. mirabilis swarms, swarmer cells are encased in
a slime layer and appear to preferentially move along an
interconnected network of phase bright slime trails (Fig-
ure (d);[]). It has been hypothesised that the slime trails
aid in directing swarming motility and the slime encasement
facilitates the maintenance of a cohesive organisation of cells
[,]. erefore slime production and slime trail follow-
ing promote the self-organisation of collective behaviours
necessary for the expansion of the swarming colony.
Gliding motility of Myxococcus xanthus is mediated by
the combined eorts of two motility modes; social (S)
motility and adventurous (A) motility. Similar to twitching
motility, S-motility is driven by the extension, binding, and
retraction of tfp with this motility mode being typically
displayed by groups or clusters of cells [,]. A-motility
mediates single cell migration and in contrast to that of
S-motility, the machinery driving A-motility is yet to be
conrmed and is an area of controversy [,]. However
all current schools of thought predict the role of a secreted
slime in facilitating the A-motility of this organism [
], where phase bright trails are observed at the leading
edge of the M. xanthus swarms when grown on semisolid
media (Figure (e);[]), similar to that of Pr. mirabilis.
M. xanthus cells preferentially migrate along these slime
trails, with cells frequently observed to turn onto the trails
rather than migrating across virgin territory. Continued
cellular trac along the trails results in their thickening and
extension as a consequence of continued slime deposition
[,]. It is recognised that this trail following behaviour
coordinates the collective behaviour of M. xanthus cells,
specically those displaying A-motility, at the leading edge of
the surface swarms, and contributes to the emergence of the
interconnected pattern networks at these areas [].
e following of slime trails during Pr. mirabilis swarming
and M. xanthus gliding motilities are both sematectonic
and quantitative stigmergic systems, where the stimulus
(slime) is a physical manifestation within the environment
that directly contributes to the expansion of the community
as it is required for the motility of the organism. is is
particularly the case for the slime mediating the A-motility
of M. xanthus (sematectonic stigmergy). Continued trac
along the slime trails amplies the slime deposited resulting
in further recruitment of cells migrating along these regions
(quantitative stigmergy).
It has been shown recently that the formation of vor-
texes comprised of thousands of bacteria rotating in unison
that occur during active surface migration by Paenibacillus
vortex biolms occurs as a consequence of the actions of a
subpopulation of lamentous cells that direct the motion of
the other members of the collective [,]. is appears to
be another example of bacterial stigmergy, though it remains
to be determined if this collective behaviour occurs as a
consequence of physical alteration of the environment, slime,
or chemical cues.
Scientica
A number of computational models have been devel-
oped to describe collective behaviours displayed during
swarm activities, particularly for M. xanthus []. Due
to the inherent diculties in modelling biological systems,
a number of these models do not truly reect experimental
observations or contain artefacts as a consequence of the
rule parameters incorporated into the model []. Stigmergic
systems have long been the focus of extensive computational
modelling to understand the emergent properties within
these systems [] and to relate stigmergic principles from
one system to another in an attempt to draw comparisons
from well-studied and established systems []. It is our con-
tentionthatasimilarapproachcouldbetakenformodelling
bacterial swarming communities through the incorporation
of key ideas from other stigmergic models, such as those
ofHelbingetal.andGoldstoneetal.,whomodelledsema-
tectonic and quantitative stigmergic systems such as trail
following by humans and animals []. Incorporating such
an integrated approach could potentially yield further insight
into the self-organisation and emergent pattern networks of
bacterial swarms.
3. Bacterial Biofilms
Bacterial biolms are multicellular communities of bacteria
thatareattachedtoeachotherandoenabioticorabi-
otic surface via a self-produced extracellular matrix com-
prised of extracellular polymeric substances (EPS) including
exopolysaccharides, eDNA, proteins, and lipids []. e
production of this EPS matrix is essential for biolm develop-
ment as it provides intercellular connectivity that binds cells
to each other and, in the case of surface-attached biolms,
provides surface adherence [,]. e ability of the EPS
matrix produced by biolm cells to promote cohesion and
surface attachment of the biolm community is an example
of sematectonic stigmergy.
It has been observed that individual P. a e r u g i n o s a
cells undergo extensive twitching motility-mediated surface
exploration prior to subsequent microcolony formation dur-
ing the early stages of biolm formation on glass submerged
in liquid nutrient media []. Zhao and colleagues
showed recently that, during surface exploration, P. a e r u g i -
nosa cells deposited trails of the exopolysaccharide Psl, which
appeared to recruit additional cells along these trails leading
to a positive feedback loop of further Psl deposition and
subsequent cell attraction []. It was hypothesised that
this trail following behaviour was facilitated by twitching-
motility-mediated surface exploration, where the tfp were
thought to probe the surrounding areas for Psl networks,
promoting binding of the tfp and directing cellular migration
to these areas []. In areas of high Psl concentration, cells
were observed to adhere to the substratum and correlated to
the subsequent sites of microcolony formation [,]. is
mechanism of following exopolysaccharide trails to coor-
dinate the single cellular motilities of P. a e r u g i n o s a during
early biolm development is an example of sematectonic
and quantitative stigmergy. Zhao et al. used a “rich-getting-
richer” analogy comparable to that of capitalist economies to
describe this emergent behaviour [], which has itself been
described as a stigmergic system [,].
4. Quorum Sensing
In many bacterial communities quorum sensing regulates
and coordinates social behaviours, such as bioluminescence,
secretion of public goods, and the switch from planktonic
to the biolm mode of growth []. Quorum sensing
occurs through the release of small molecules by individual
bacteria into the environment by passive diusion. e
concentration of these small molecules increases within the
environment with increasing cell density, permitting cells
to gather information about their surrounding neighbours.
Once a sucient quantity of signal is present within the
environment, reecting a critical population density, a gene
regulation cascade is initiated culminating in the up- or
downregulation of the expression of various genes required
for social behaviours, virulence factor production, and so
forth [,]. In this manner it has been identied that
quorum sensing can regulate the expression of over  genes
within P. a e r u g i n o s a [].
Quorum sensing within bacterial communities bares a
striking resemblance to pheromone signalling that coordi-
nates the collective behaviours of social insects. It is there-
fore interesting to speculate whether quorum sensing oers
another example of stigmergic self-organisation within bacte-
rial communities. Under circumstances where quorum sens-
ing signalling molecules are able to persist and accumulate
withintheenvironment,thenquorumsensingcouldbe
considered an example of marker-based stigmergy whereby
the release of signalling molecules into the environment
stimulates collective behaviours of the growing bacterial
population, similar to the pheromones coordinating the
social behaviours of ants and termites. It could be suggested
that quorum sensing, in addition to marker-based stigmergy,
is also an example of qualitative stigmergy, where, depending
on their concentration, the quorum sensing signals trigger
dierent responses by the bacterial population.
5. Summary and Future Directions
We have presented a number of examples in which bacteria
employ stigmergic self-organisation to coordinate their col-
lective behaviours and found that sematectonic and quanti-
tative stigmergic systems in the form of trail following were
the most prevalent in the above examples. is highlights
the conserved nature of self-organising mechanisms within
nature regardless of the cognitive abilities of the individual
entities and suggests a common evolution of trail following
as a simple yet eective means of coordinating collective
behaviours.
e idea that self-organising systems utilised by bacterial
communities are similar to those utilised by higher organisms
is gaining interest within the scientic community. A recent
reviewhascalledfortheemploymentofamoreintegrative
approach across scientic elds in the study of self-organising
systems []. Stigmergy provides an excellent example of
this approach where, since its rst introduction within the
eld of entomology [], the importance of this concept has
been recognised across diverse areas ranging from biology
to social sciences, technology, and computer sciences [,
,]. e wide acceptance of stigmergy can, for the most
Scientica
part, be attributed to a special edition of Articial Life
dedicated to stigmergic systems [,], with the hopes of
bringing this concept to the forefront within the scientic
community. is concept, despite its obvious application to
the understanding of multicellular bacterial behaviours, has
been largely overlooked within the eld of microbiology.
Here we recognise the importance of the concept of stig-
mergic self-organisation and the implications it has on under-
standingthecollectivebehavioursofcomplexmulticellular
bacterial communities. We propose that bacterial stigmergy
should be included in the repertoire of systems that bacteria
employ to control multicellular activities. Furthermore, we
suggest that bacterial stigmergic systems may provide testable
models to explore stigmergic self-organisation at a molec-
ular level [], which is currently an unexplored concept
and will ultimately lead to greater understanding of other
biological stigmergic systems. Understanding the mecha-
nisms employed by bacteria to coordinate their multicellular
behaviours may lead to the development of novel strategies
to control infections and biofouling in industrial and marine
settings.
Conflict of Interests
e authors declare that there is no conict of interests
regarding the publication of this paper.
Acknowledgment
Cynthia B. Whitchurch was supported by a NHMRC Senior
Research Fellowship ().
References
[] V. Narayan, N. Menon, and S. Ramaswamy, “Nonequilibrium
steady states in a vibrated-rod monolayer: tetratic, nematic, and
smectic correlations,Journal of Statistical Mechanics: eory
and Experiment,no.,ArticleIDP,.
[] S. Camazine, J. L. Deneubourg, N. Franks, J. Sneyd, G. er-
aulaz, and E. Bonabeau, Self-Organization in Biological Systems,
Princeton University Press, Princeton, NJ, USA, .
[] B.Grammaticos,M.Badoual,andM.Aubert,“An(almost)solv-
able model for bacterial pattern formation,Physica D: Nonlin-
ear Phenomena,vol.,no.,pp.,.
[] H. Levine and E. Ben-Jacob, “Physical schemata underlying
biological pattern formation—examples, issues and strategies,
Physical Biology,vol.,no.,pp.PP,.
[] P.-P. Grass´
e, “La reconstruction du nid et les coordinations
interindividuelles chez Bellicositermes natalensis et Cubitermes
sp. la th´
eorie de la stigmergie: Essai d’interpr´
etation du com-
portement des termites constructeurs,Insec tes Soc iaux,vol.,
no. , pp. –, .
[] G. eraulaz and E. Bonabeau, “A brief history of stigmergy,
Articial Life,vol.,no.,pp.,.
[] R.L.GoldstoneandM.E.Roberts,“Self-organizedtrailsystems
in groups of humans,Complexity,vol.,no.,pp.,.
[] D.Helbing,J.Keltsch,andP.Moln
´
ar, “Modelling the evolution
of human trail systems,Nature,vol.,no.,pp.,
.
[]D.Helbing,F.Schweitzer,J.Keltsch,andP.Moln
´
ar, “Active
walker model for the formation of human and animal trail
systems,Physical Review E,vol.,no.,pp.,.
[] E.Boissard,P.Degond,andS.Motsch,“Trailformationbased
on directed pheromone deposition,Journal of Mathematical
Biology,vol.,no.,pp.,.
[] E. Bonabeau, G. eraulaz, J.-L. Deneubourg, S. Aron, and
S. Camazine, “Self-organization in social insects,Trend s in
Ecology and Evolution,vol.,no.,pp.,.
[] O. Holland and C. Melhuish, “Stigmergy, self-organization, and
sorting in collective robotics,Articial Life,vol.,no.,pp.
–, .
[] J.Stradner,R.enius,P.Zahadat,H.Hamann,K.Crailsheim,
and T. Schmickl, “Algorithmic requirements for swarm intelli-
gence in dierently coupled collective systems,Chaos, Solitons
&Fractals,vol.,pp.,.
[] L. R. Christensen, “Stigmergy in human practice: coordination
in construction work,Cognitive Systems Research,vol.,pp.
–, .
[] L. Marsh and C. Onof, “Stigmergic epistemology, stigmergic
cognition,Cognitive Systems Research,vol.,no.-,pp.
, .
[] H. V. D. Parunak, “A survey of environments and mechanisms
for human-human stigmergy,” in Environments for Multi-Agent
Systems II,vol.ofLecture Notes in Computer Science,pp.
–, Springer, Berlin, Germany, .
[] P. Peeters, H. van Brussel, P. Valckenaers et al., “Pheromone
based emergent shop oor control system for exible ow
shops,Articial Intelligence in Engineering,vol.,no.,pp.
–, .
[] S. Burbeck, “Complexity and the evolution of computing: bio-
logical principles for managing evolving systems,Computing
Systems, pp. –, .
[] E. O. Wilson, Sociobiology: e New Synthesis,HarvardUniver-
sity Press, Cambridge, Mass, USA, .
[] H. A. Downing and R. L. Jeanne, “Nest construction by
the paper wasp, Polistes: a test of stigmergy theory,Animal
Behaviour, vol. , no. , pp. –, .
[] S.Goss,R.Beckers,J.-L.Deneubourg,S.Aron,J.Pasteels,and
R. N. Hughes, “How trail laying and trail following can solve
foraging problems for ant colonies,” in Behavioural Mechanisms
of Food Selection, pp. –, .
[] O. Holland, “Multi-agent systems: lessons from social insects
and collective robotics,” in Proceedings of the AAAI Spring Sym-
posium on Adaptation, Coevolution and Learning in Multiagent
Systems, pp. –, AAAI Press, Menlo Park, Calif, USA, .
[] A. J. Merz, M. So, and M. P. Sheetz, “Pilus retraction powers
bacterial twitching motility,Nature, vol. , no. , pp. –
, .
[] J. M. Skerker and H. C. Berg, “Direct observation of extension
and retraction of type IV pili,Proceedings of the National
Academy of Sciences of the United States of America, vol. , no.
, pp. –, .
[] A. B. T. Semmler, C. B. Whitchurch, and J. S. Mattick, “A re-
examination of twitching motility in Pseudomonas aeruginosa,”
Microbiology,vol.,no.,pp.,.
[] K. Bovre and L. O. Froholm, “Variation of colony morphology
reecting mbriation in Moraxella bovis and two reference
strains of M. nonliquefaciens,” Acta Pathologica et Microbiolog-
ica Scandinavica—Section B: Microbiology and Immunology,vol.
,no.,pp.,.
Scientica
[] J. C. McMichael, “Bacterial dierentiation within Moraxella
bovis colonies growing at the interface of the agar medium with
the Petri dish,Journal of General Microbiology,vol.,no.,
pp. –, .
[] E. S. Gloag, L. Turnbull, A. Huang et al., “Self-organization of
bacterial biolms is facilitated by extracellular DNA,Proceed-
ings of the National Academy of Sciences of the United States of
America, vol. , no. , pp. –, .
[] E. S. Gloag, M. A. Javed, H. Wang et al., “Stigmergy: a key driver
of self-organization in bacterial biolms,Communicative and
Integrative Biology,vol.,no.,ArticleIDe,.
[] J. J. Alexander and J. F. Lew is, “Pitting of agar surfac e by
Pseudomonas stutzeri,” Journal of Clinical Microbiology,vol.,
article , .
[] S. D. Henriksen, ““Pitting” and “corrosion” of the surface of
agar cultures by colonies of some bacteria from the respiratory
tract,Acta Pathologica Microbiologica Scandinavica Section B
Microbiology and Immunology,vol.,no.,pp.,.
[] J. M. Tennent and J. S. Mattick, “Type  mbriae,” in Fimbriae
Adhesion, Genetics, Biogenesis, and Vaccines, P. Klemm, Ed., pp.
–, CRC Press, .
[]S.H.Zinner,A.K.Daly,andW.M.McCormack,“Isolation
of Eikenella corrodens in a general hospital,Journal of Applied
Microbiology,vol.,no.,pp.,.
[] D.N.Love,R.F.Jones,M.Bailey,andA.Calverley,“Comparison
of strains of gram-negative, anaerobic, agar-corroding rods
isolated from so tissue infections in cats and dogs with
type strains of Bacteroides gracilis,Wol i n e l l a recta,Wolinel l a
succinogenes,andCampylobacter concisus,” Journal of Clinical
Microbiology,vol.,no.,pp.,.
[] J.V.A.RobinsonandA.L.James,“Someobservationsonthe
colony morphology of “corroding bacilli”,” Journal of Applied
Bacteriology,vol.,no.,pp.,.
[] X. Han, R. M. Kennan, J. K. Davies et al., “Twitching motility
is essential for virulence in Dichelobacter nodosus,” Journal of
Bacteriology,vol.,no.,pp.,.
[] P. Vallotton, L. Mililli, L. Turnbull, and C. Whitchurch, “Seg-
mentation of dense D bacilli populations,” in Digital Image
Computing: Techniques and Applications,DICTA,Sydney,Aus-
tralia, .
[] P. Vallotton, C. Sun, D. Wang, L. Turnbull, C. Whitchurch,
and P. Ranganathan, “Segmentation and tracking individual
pseudomonas aeruginosa bacteria in dense populations of
motile cells,” in Proceedings of the 24th International Conference
Image and Vision Computing New Zealand (IVCNZ ’09),pp.
–, Wellington, New Zealand, November .
[] E. J. van Schaik, C. L. Giltner, G. F. Audette et al., “DNA
binding: a novel function of Pseudomonas aeruginosa type IV
pili,Journal of Bacteriolog y,vol.,no.,pp.,.
[] I.I.Smalyukh,J.Butler,J.D.Shrout,M.R.Parsek,andG.C.L.
Wong, “Elasticity-mediated nematiclike bacterial organization
in model extracellular DNA matrix,Physical Review E—
Statistical, Nonlinear, and So Matter Physics,vol.,no.,
Article ID , .
[] O. Rauprich, M. Matsushita, C. J. Weijer, F. Siegert, S. E. Esipov,
andJ.A.Shapiro,“PeriodicphenomenainProteus mirabilis
swarm colony development,Journal of Bacteriolog y,vol.,
no.,pp.,.
[] R. M. Harshey, “Bees aren’t the only ones: swarming in gram-
negative bacteria,Molecular Microbiology,vol.,no.,pp.
–, .
[] B. V. Jones, R. Young, E. Mahenthiralingam, and D. J. Stickler,
“Ultrastructure of Proteus mirabilis swarmer cell ras and role
of swarming in catheter-associated urinary tract infection,
Infection and Immunity,vol.,no.,pp.,.
[] S. J. Stahl, K. R. Stewart, and F. D. Williams, “Extracellular slime
associated with Proteus mirabilis during swarming,Journal of
Bacteriology,vol.,no.,pp.,.
[] J. Hodgkin and D. Kaiser, “Genetics of gliding motility in Myx-
ococcus xanthus (Myxobacterales): two gene systems control
movement,Molecular and General Genetics,vol.,no.,pp.
–, .
[] D. Kaiser, “Social gliding is correlated with the presence of pili
in Myxococcus xanthus,” Proceedings of the National Academy of
Sciences of the United States of America, vol. , no. , pp. –
, .
[] E. M. F. Mauriello, T. Mignot, Z. Yang, and D. R. Zusman,
“Gliding motility revisited: how do the myxobacteria move
without agella?” Microbiology and Molecular Biology Reviews,
vol.,no.,pp.,.
[] T.Mignot,“eelusiveengineinMyxococcus xanthus gliding
motility,Cellular and Molecular Life Sciences,vol.,no.,pp.
–, .
[] A. Ducret, M.-P. Valignat, F. Mouhamar, T. Mignot, and
O. eodoly, “Wet-surface-enhanced ellipsometric contrast
microscopy identies slime as a major adhesion factor during
bacterial surface motility,Proceedings of the National Academy
of Sciences of the United States of America,vol.,no.,pp.
–, .
[]B.Nan,J.Chen,J.C.Neu,R.M.Berry,G.Oster,andD.R.
Zusman, “Myxobacteria gliding motility requires cytoskeleton
rotation powered by proton motive force,” Proceedings of the
National Academy of Sciences of the United States of America,
vol. , no. , pp. –, .
[] C. Wolgemuth, E. Hoiczyk, D. Kaiser, and G. Oster, “How
myxobacteria glide,Current Biology,vol.,no.,pp.,
.
[] R. P. Burchard, “Trail following by gliding bacteria, Journal of
Bacteriology,vol.,no.,pp.,.
[] R. Yu and D. Kaiser, “Gliding motility and polarized slime
secretion,Molecular Microbiology,vol.,no.,pp.,
.
[] E. Ben-Jacob, I. Cohen, and D. L. Gutnick, “Cooperative orga-
nization of bacterial colonies: from genotype to morphotype,
Annual Review of Microbiology,vol.,pp.,.
[] P. Vallotton, “Size matters: lamentous bacteria drive interstitial
vortex formation and colony expansion in Paenibacillus vortex,”
Cytometry Part A, vol. , no. , pp. –, .
[] A. B. Holmes, S. Kalvala, and D. E. Whitworth, “Spatial sim-
ulations of myxobacterial development,PLoS Computational
Biology, vol. , no. , Article ID e, .
[] Y.Wu,Y.Jiang,D.Kaiser,andM.Alber,“Socialinteractionsin
myxobacterial swarming,PLoS Computational Biology,vol.,
no. , article e, .
[] Y. Wu, A. D. Kaiser, Y. Jiang, and M. S. Alber, “Periodic reversal
of direction allows Myxobacteria to swarm,Proceedings of the
National Academy of Sciences of the United States of America,
vol. , no. , pp. –, .
[] Y. Wu, N. Chen, M. Rissler, Y. Jiang, D. Kaiser, and M. Alber,
“CA models of myxobacteria swarming,” in Cellular Automata,
vol. , pp. –, Springer, Berlin, Germany, .
Scientica
[] S. S. Branda, ˚
A. Vik, L. Friedman, and R. Kolter, “Biolms: the
matrix revisited,Tr ends in Micr obi o logy,vol.,no.,pp.
, .
[] H.-C. Flemming and J. Wingender, “e biolm matrix,Nature
Reviews Microbiology,vol.,no.,pp.,.
[] C. B. Whitchurch, T. Tolker-Nielsen, P. C. Ragas, and J. S.
Mattick, “Extracellular DNA required for bacterial biolm
formation,Science,vol.,no.,article,.
[] J. C. Conrad, “Physics of bacterial near-surface motility using
agella and type IV pili: implications for biolm formation,
Research in Microbiology,vol.,no.-,pp.,.
[] J.C.Conrad,M.L.Gibiansky,F.Jinetal.,“Flagellaandpili-
mediated near-surface single-cell motility mechanisms in P.
aeruginosa,” Biophysical Journal,vol.,no.,pp.,
.
[] M. L. Gibiansky, J. C. Conrad, F. Jin et al., “Bacteria use type IV
pili to walk upright and detach from surfaces,Science,vol.,
no. , p. , .
[] F. Jin, J. C. Conrad, M. L. Gibiansky, and G. C. L. Wong, “Bacte-
ria use type-IV pili to slingshot on surfaces,Proceedings of the
National Academy of Sciences of the United States of America,
vol.,no.,pp.,.
[] K. Zhao, B. S. Tseng, B. Beckerman et al., “Psl trails guide explo-
ration and microcolony formation in Pseudomonas aeruginosa
biolms,” Nature, vol. , no. , pp. –, .
[] S. Wang, M. R. Parsek, D. J. Wozniak, and L. Z. Ma, “A spider
web strategy of type IV pili-mediated migration to build a
bre-like Psl polysaccharide matrix in Pseudomonas aeruginosa
biolms,” Environmental Microbiology,vol.,no.,pp.
, .
[] M. Schuster, D. Joseph Sexton, S. P. Diggle, and E. Peter
Greenberg, “Acyl-homoserine lactone quorum sensing: from
evolution to application,Annual Review of Microbiology,vol.
, pp. –, .
[] Y.-H. Li and X. Tian, “Quorum sensing and bacterial social
interactions in biolms,Sensors,vol.,no.,pp.,
.
[] A. Ross-Gillespie and R. K¨
ummerli, “Collective decision-mak-
ing in microbes,Frontiers in Microbiology,vol.,article,
.
[] M. J. Doyle and L. Marsh, “Stigmergy .: from ants to econo-
mies,Cognitive Systems Research,vol.,pp.,.
[] E. Bonabeau, “Editor’s introduction: stigmergy,Articial Life,
vol. , no. , pp. –, .
... Collective behavior refers to complex macroscopic dynamics of microbial communities exhibiting emergence and self-organization properties without a global controller (Balaban et al. 2018). The self-organization of collective behaviors often manifests as dramatic patterns of emergent large-scale order (Gloag et al. 2015). Bacterial stigmergy is a selforganization principle that explained how random and independent movements of individual cell (or trichomes) could result, by the transfer of local information (chemical, for example), coordinated behavior at a global level (Gloag et al. 2015). ...
... The self-organization of collective behaviors often manifests as dramatic patterns of emergent large-scale order (Gloag et al. 2015). Bacterial stigmergy is a selforganization principle that explained how random and independent movements of individual cell (or trichomes) could result, by the transfer of local information (chemical, for example), coordinated behavior at a global level (Gloag et al. 2015). Many other researchers of bacterial aggregations and collective rearrangement of the cells in colonies and metabolic dynamics also considered the coordinated social behavior in bacterial communities as self-organization (Caratozzolo et al. 2008;Brodsky 2009;Hengge and Sourjik 2013;Ebrahimi et al. 2019;de Astacio et al. 2020;You et al. 2021). ...
... Social behavior is found at every level of biological complexity, ranging from quorum sensing in bacteria to human altruism (Parrish et al. 2002;De Monte et al. 2007;Ballerini et al. 2008;Balázsi et al. 2011;Leu et al. 2013;Attanasi et al. 2014;Gloag et al. 2015). Sociobiology is an attractive interdisciplinary field. ...
Chapter
The chapter presents an analytic description of evolutionary and developmental morphogenetic events in Metazoa using concepts of self-organization, morphological and molecular–genetic data, and the topological approach to the analysis. Biological objects are complex systems capable of dynamic self-organization at all levels of biological complexity. Some examples of self-organization in cyanobacteria, metazoan cells in vitro (chick embryo myogenic cells, molluscan hemocytes, sea urchin embryo cells), and animal communities of some vertebrates are shown. Following René Thom, a topological interpretation of some evolutionary and developmental transformations is presented using well-known mathematical concepts. Toroidal forms are considered as examples of functionally optimized biological design and attractors in metazoan morphogenesis. Molecular–genetic evidence of genomic–phenomic correlations determining the body plan and evolutionary trajectories in Metazoa is discussed. Gene regulatory networks and whole metazoan genomes are interpreted as self-organizing network systems dynamically transforming in development and evolution. Symmetry breaking, topological discontinuities and catastrophes, and body plan transformations are fundamental phenomena in metazoan development and evolution.
... The spatial architecture of living systems emerges through self-organization without a central coordination [1][2][3], often, with information propagating via selfgenerated chemical signals [4][5][6][7]. These systems display large-scale patterns, from shell and neural structures [8][9][10][11] to ecological distributions [12,13]. ...
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Full-text available
Complex spatial patterns in biological systems often arise through self-organization without a central coordination, guided by local interactions and chemical signaling. In this study, we explore how motility-dependent chemical deposition and concentration-sensitive feedback can give rise to fractal-like networks, using a minimal agent-based model. Agents deposit chemicals only while moving, and their future motion is biased by local chemical gradients. This interaction generates a rich variety of self-organized structures resembling those seen in processes like early vasculogenesis and epithelial cell dispersal. We identify a diverse phase diagram governed by the rates of chemical deposition and decay, revealing transitions from uniform distributions to sparse and dense networks, and ultimately to full phase separation. At low chemical decay rates, agents form stable, system-spanning networks; further reduction leads to re-entry into a uniform state. A continuum model capturing the co-evolution of agent density and chemical fields confirms these transitions and reveals how linear stability criteria determine the observed phases. At low chemical concentrations, diffusion dominates and promotes fractal growth, while higher concentrations favor nucleation and compact clustering. These findings unify a range of biological phenomena - such as chemotaxis, tissue remodeling, and self-generated gradient navigation - within a simple, physically grounded framework. Our results also offer insights into designing artificial systems with emergent collective behavior, including robotic swarms or synthetic active matter.
... 3 This further suggests that an embodied world model, extending the system in space and time by its interactions with an environment, can be leveraged to maintain coherence. We hypothesise this explains why stigmergy [58][59][60][61] and other forms of extracellular signalling arise in biological systems. Throughout we have assumed that the free energy is minimised. ...
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All intelligence is collective intelligence, in the sense that it is made of parts which must align with respect to system-level goals. Understanding the dynamics which facilitate or limit navigation of problem spaces by aligned parts thus impacts many fields ranging across life sciences and engineering. To that end, consider a system on the vertices of a planar graph, with pairwise interactions prescribed by the edges of the graph. Such systems can sometimes exhibit long-range order, distinguishing one phase of macroscopic behaviour from another. In networks of interacting systems we may view spontaneous ordering as a form of self-organisation, modelling neural and basal forms of cognition. Here, we discuss necessary conditions on the topology of the graph for an ordered phase to exist, with an eye towards finding constraints on the ability of a system with local interactions to maintain an ordered target state. By studying the scaling of free energy under the formation of domain walls in three model systems -- the Potts model, autoregressive models, and hierarchical networks -- we show how the combinatorics of interactions on a graph prevent or allow spontaneous ordering. As an application we are able to analyse why multiscale systems like those prevalent in biology are capable of organising into complex patterns, whereas rudimentary language models are challenged by long sequences of outputs.
... A similar principle is also used by ants to follow trails [12], by paper wasps to construct nests [13], and by honeybees to locate their queens [14][15][16]. Recently, stigmergy has been extended beyond social insects to bacterial colonies [17,18], spatiotemporal patterns of animal territories [19], cognition [20,21] and swarm robots [22][23][24]. ...
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Collective behaviour defines the lives of many animal species on the Earth. Underwater swarms span several orders of magnitude in size, from coral larvae and krill to tunas and dolphins. Agent-based algorithms have modelled collective movements of animal groups by use of social forces , which approximate the behaviour of individual animals. But details of how swarming individuals interact with the fluid environment are often under-examined. How do fluid forces shape aquatic swarms? How do fish use their flow-sensing capabilities to coordinate with their schooling mates? We propose viewing underwater collective behaviour from the framework of fluid stigmergy , which considers both physical interactions and information transfer in fluid environments. Understanding the role of hydrodynamics in aquatic collectives requires multi-disciplinary efforts across fluid mechanics, biology and biomimetic robotics. To facilitate future collaborations, we synthesize key studies in these fields.
... The ability to exploit the traces left in the environment by the action of organisms is one of the simplest and oldest mechanisms used to coordinate collective behaviors in biological systems (34)(35)(36). In humans, over the past thirty years, the massive development of the Internet, together with applications that extensively use digital traces left voluntarily or not by their users, has reinforced the need to understand how these traces influence individual and collective behaviors (25,(37)(38)(39). ...
Article
Stigmergy is a generic coordination mechanism widely used by animal societies, in which traces left by individuals in a medium guide and stimulate their subsequent actions. In humans, new forms of stigmergic processes have emerged through the development of online services that extensively use the digital traces left by their users. Here, we combine interactive experiments with faithful data-based modeling to investigate how groups of individuals exploit a simple rating system and the resulting traces in an information search task in competitive or noncompetitive conditions. We find that stigmergic interactions can help groups to collectively find the cells with the highest values in a table of hidden numbers. We show that individuals can be classified into three behavioral profiles that differ in their degree of cooperation. Moreover, the competitive situation prompts individuals to give deceptive ratings and reinforces the weight of private information versus social information in their decisions.
... Moreover, while unicellular organisms have initially been thought of as individualistic and disorganized, high levels of self-organization have also been evidenced through inter and intra specie interactions. Indeed, multiple observations shows that a microbe collective can self-organize, thus making large scale patterns to emerge to the point their behavior have been compared to this of a multicellular organism (Velicer et al., 1998;Velicer, 2003;Jacob et al., 2004;Gloag et al., 2015). To summarise, those observations suggest that microbes can assemble into 85 complex communities harboring interactions already described in macrobial community ecology. ...
Thesis
Microbial communities play a key role in geochemical cycles and environmental bioprocesses. Despite their importance, the mechanisms involved in their structuration remain elusive and are poorly captured in current models. The modelling approach developed during this thesis stands as an alternative to the current empirical approaches. It relies on a novel theory of microbial growth (the MTS theory), which introduce a flux/force relationship between the microbial growth rate and the free energy gradients available in the biotope. The purpose of this thesis is to characterize the dynamic properties of the MTS model and to determine, through simulations, the part of the microbial communities’ spatio-temporal structuration that is intrinsically captured by the MTS theory and which does not pertain to parameters adjustment.Simulations firstly reveal that a characteristic of the MTS model is its ability to account for the simultaneous growth limitation by many resources of different kinds (electron acceptor/donor, but also nutrients), and to integrate them as stoichiometric limitations, giving rise to coherent populations dynamics.In a second stage, the MTS model has been used to predict the dynamics of microbial communities. Those studies revealed that the thermodynamics constraints on which the MTS kinetic theory is built intrinsically give rise to consistent ecological successions without the need to adjust specifically the parameters of each population. In the case of a simplified activated sludge ecosystem, after calibration using respirometric data, the model was able to reproduce ecosystem dynamics quantitatively with a reduced number of parameters compared to current Activated Sludge Models (ASM).In a third stage, a large database of experimental growth yield observations has been compiled from literature. The relationship between multiple physicochemical parameters characterizing the metabolisms (reduction degrees, catabolic energy...) and the growth yield has been investigated using statistical methods. This work confirms that microbial growth yields can be accurately predicted solely on thermodynamic properties of metabolic reactions. The growth yields predictor could be included in future developments of the MTS models.More generally, the work undertaken during this thesis evidenced that the MTS model proposes a formalization of the coupling between thermodynamic and dynamic variables of a microbial ecosystem. The simulated microbial populations and ecosystems display coherent dynamic behaviors. The model is able to account, by construction, for well-known ecological successions, without specific parameter adjustment. This model is peculiarly adapted to the prediction of the functional structure of communities in ecosystems dominated by selection by competition, rather than on species dispersion, diversification or genetic drift.Those results encourage the development of microbial ecosystems based on firmer theoretical grounds. Such models are necessary to the development of bioprocesses able to answer to the new technological and environmental challenges.
... Herein, all tested strains produced gliding motility-dependent furrows, indicating that the presence or absence of EPS or BPS does not qualitatively impact the formation of these substratum depressions. While it was not possible to distinguish between single and grouped cells via stereoscopy, additional cells were detected following the path of the various furrows ( Figure 1a); this supports sematectonic stigmergic coordination for the phenomenon of trail following on agar by M. xanthus cells (Gloag et al., 2015). ...
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Exopolysaccharide (EPS) layers on the bacterial cell surface are key determinants of biofilm establishment and maintenance, leading to the formation of higher‐order 3D structures that confer numerous survival benefits to a cell community. In addition to a specific cell‐associated EPS glycocalyx, we recently revealed that the social δ‐proteobacterium Myxococcus xanthus secretes a novel biosurfactant polysaccharide (BPS) to the extracellular milieu. Together, secretion of the two polymers (EPS and BPS) is required for type IV pilus (T4P)‐dependent swarm expansion via spatio‐specific biofilm expression profiles. Thus the synergy between EPS and BPS secretion somehow modulates the multicellular lifecycle of M. xanthus. Herein, we demonstrate that BPS secretion functionally alters the EPS glycocalyx via destabilization of the latter, fundamentally changing the characteristics of the cell surface. This impacts motility behaviours at the single‐cell level and the aggregative capacity of cells in groups via cell‐surface EPS fibril formation as well as T4P production, stability, and positioning. These changes modulate the structure of swarm biofilms via cell layering, likely contributing to the formation of internal swarm polysaccharide architecture. Together, these data reveal the manner by which the combined secretion of two distinct polymers induces single‐cell changes that modulate swarm biofilm communities. Production of a recently‐identified biosurfactant polysaccharide (BPS) by Myxococcus xanthus results in destabilization of the surface exopolysaccharide (EPS) layer at the single‐cell level. This destabilization impacts all aspects of M. xanthus multicellular physiology including single‐cell and group modes of motility, fruiting body formation during development, and biofilm formation and structuration.
... Prediction 5: All retrievable biological memories are stigmergic. Beginning with bacteria (Gloag et al. 2015), biological systems ubiquitously employ stigmergic memories (Heylighen 2016). This is not a surprising observation to be explained, but rather an empirical confirmation in MP. ...
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Theories of consciousness and cognition that assume a neural substrate automatically regard phylogenetically basal, nonneural systems as nonconscious and noncognitive. Here, we advance a scale-free characterization of consciousness and cognition that regards basal systems, including synthetic constructs, as not only informative about the structure and function of experience in more complex systems but also as offering distinct advantages for experimental manipulation. Our "minimal physicalist" approach makes no assumptions beyond those of quantum information theory, and hence is applicable from the molecular scale upwards. We show that standard concepts including integrated information, state broadcasting via small-world networks, and hierarchical Bayesian inference emerge naturally in this setting, and that common phenomena including stigmergic memory, perceptual coarse-graining, and attention switching follow directly from the thermodynamic requirements of classical computation. We show that the self-representation that lies at the heart of human autonoetic awareness can be traced as far back as, and serves the same basic functions as, the stress response in bacteria and other basal systems.
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Collective behavior has become a recent topic of investigation in systems chemistry. In pursuing this phenomenon, we present polymersome stomatocytes loaded with the enzyme urease, which show basic stigmergy-based communication and are capable of signal production, reception, and response by clustering with surface complementary binding partners. The collective behavior is transient and based on the widely known pH-sensitive non-covalent interactions between nitrilotriacetic acid (NTA) and histidine (His) moieties attached to the surface of urease-loaded and empty stomacytes, respectively. Upon the addition of the substrate urea, the urease stomatocytes are able to increase the environmental pH, allowing the NTA units to interact with the surface histidines on the complementary species, triggering the formation of transient clusters. The stomatocytes display a maximum clustering interaction at a pH between 6.3 and 7.3, and interparticle repulsive behavior outside this range. This leads to oscillating behavior, as the aggregates disassemble when the pH increases due to high local urease activity. After bulk pH conditions are restored, clustering can take place again. Within the detectable region of dynamic light scattering, individual stomatocytes can aggregate to agglomerates with 10 times their volume. Understanding and designing population behavior of active colloids can facilitate the execution of cooperative tasks, which are not feasible for individual colloids.
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In quasi-one-dimensional circularly symmetric systems of active particles, experiments and simulations reveal an indirect interplay between particles and environmental drag effects, proving crucial in the realm of generalized parametrically controlled stigmergy. Our investigation goes deeper into understanding how stigmergy manifests itself, closely examining unconventional, more physically grounded interpretations in contrast to established concepts. Deeper insights into the complex dynamics of stigmergically interacting particle systems are gained by systematically studying the transition regions between short- and long-term stigmergic effects. Mechanical and computational modeling techniques complement each other to provide a comprehensive understanding of various clustering patterns, oscillatory modes, and system dynamics, where hysteresis may occur depending on the conditions.
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Self-organization was introduced originally in the context of physics and chemistry to describe how microscopic processes give rise to macroscopic stuctures in out-of-equilibrium systems, Recent research that extends this concept to ethology suggests that it provides a concise description of a wide range of collective phenomena in animals, especially in social insects. This description does not rely on individual complexity to account for complex spatiotemporal features that emerge at the colony level, but rather assumes that intractions among simple individuals can produce highly structured collective behaviours.
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Bacterial biofilms are complex multicellular communities that are often associated with the emergence of large-scale patterns across the biofilm. How bacteria self-organize to form these structured communities is an area of active research. We have recently determined that the emergence of an intricate network of trails that forms during the twitching motility mediated expansion of Pseudomonas aeruginosa biofilms is attributed to an interconnected furrow system that is forged in the solidified nutrient media by aggregates of cells as they migrate across the media surface. This network acts as a means for self-organization of collective behavior during biofilm expansion as the cells following these vanguard aggregates were preferentially confined within the furrow network resulting in the formation of an intricate network of trails of cells. Here we further explore the process by which the intricate network of trails emerges. We have determined that the formation of the intricate network of furrows is associated with significant remodeling of the sub-stratum underlying the biofilm. The concept of stigmergy has been used to describe a variety of self-organization processes observed in higher organisms and abiotic systems that involve indirect communication via persistent cues in the environment left by individuals that influence the behavior of other individuals of the group at a later point in time. We propose that the concept of stigmergy can also be applied to describe self-organization of bacterial biofilms and can be included in the repertoire of systems used by bacteria to coordinate complex multicellular behaviors.
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Microbes are intensely social organisms that routinely cooperate and coordinate their activities to express elaborate population level phenotypes. Such coordination requires a process of collective decision-making, in which individuals detect and collate information not only from their physical environment, but also from their social environment, in order to arrive at an appropriately calibrated response. Here, we present a conceptual overview of collective decision-making as it applies to all group-living organisms; we introduce key concepts and principles developed in the context of animal and human group decisions; and we discuss, with appropriate examples, the applicability of each of these concepts in microbial contexts. In particular, we discuss the roles of information pooling, control skew, speed vs. accuracy trade-offs, local feedbacks, quorum thresholds, conflicts of interest, and the reliability of social information. We conclude that collective decision-making in microbes shares many features with collective decision-making in higher taxa, and we call for greater integration between this fledgling field and other allied areas of research, including in the humanities and the physical sciences.
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Stigmergy is a concept of coordination that may be employed to analyse human practice in complex work settings such as the building process. However, the concept of stigmergy was not originally developed in order to describe human practice, rather it was developed within the field of entomology i.e. the study of social insects. Transposing the concept of stigmergy from the field of entomology to the study of human practice raises a central question: Does the concept of stigmergy add anything to our ability to account for the coordination of human cooperative work? We will argue that it does. We will (1) explicitly compare and delimit the concept of stigmergy to well-established concept describing human coordinative practices and show that it differs from these concepts, and we will (2) apply the concept of stigmergy to an analysis of the coordination of construction work in order to explore the utility of the concept in the analysis of human practice.
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The editors introduce the themed issue “stigmergy 3.0”.
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We have developed an experimental platform for studying the trail systems that spontaneously emerge when people are motivated to take advantage of the trails left by others. In this virtual environment, the participants' task is to reach randomly selected destinations while minimizing travel costs. The travel cost of every patch in the environment is inversely related to the number of times the patch was visited by others. The resulting trail systems are a compromise between people going to their destinations and going where many people have previously traveled. We compare the results from our group experiments to the Active Walker model of pedestrian motion from biophysics. The Active Walker model accounted for deviations of trails from the beeline paths, the gradual merging of trails over time, and the influences of scale and configuration of destinations on trail systems, as well as correctly predicting the approximate spatial distribution of people's steps. Two deviations of the model from empirically obtained results were corrected by (1) incorporating a distance metric sensitive to canonical horizontal and vertical axes, and (2) increasing the influence of a trail's travel cost on an agent's route as the agent approaches its destination. © 2006 Wiley Periodicals, Inc. Complexity 11: 43–50, 2006This paper was submitted as an invited paper resulting from the “Understanding Complex Systems” conference held at the University of Illinois–Urbana Champaign, May 2005
Article
The beautiful patterns formed by motile bacteria have always intrigued the curious (Ben-Jacob et al., Eur. Phys. J. B 2008;65:315-322). The mechanisms underlying their formation are believed to play a role in a range of natural phenomena, including embryogenesis, animal behavior, and economics. There has been significant effort to develop tools for characterizing the behavior of individual cells within large populations of migrating bacteria; a prerequisite for studying self-organization in this context (Garner, Mol. Micro. 2011;80:577-579). Here, I apply powerful computer vision methods to study P. vortex interstitial colony expansion. Quantitative observations show how exceptionally long bacteria play a catalytic role-both in vortex formation, which had to date remained somewhat mysterious-and in facilitating colony expansion. This highlights the functional importance of bacterial morphology in bridging the microscopic and macroscopic scales, and it reshapes our understanding of vortex-forming bacteria. © 2013 International Society for Advancement of Cytometry.