Pith. sign in

REVIEW 3 major objections 5 minor 88 references

Evolution of specialized microbial cooperation in dynamic fluids

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read In a simulated microbial community, physical transport—diffusion, decay, and fluid flow—can override fitness economics, keeping generalist cooperation stable where invasion fitness alone predicts specialists or cheaters should win.

desk verdict A credible simulation study showing fluid flow can flip microbial specialisation outcomes, but the abstract's broad claim outstrips what one passive Turing mechanism can carry. read the letter →

arxiv 1908.07884 v2 pith:2F6Y2HO6 submitted 2019-08-21 q-bio.PE physics.bio-ph

Gurdip Uppal , Dervis Can Vural This is my paper · ORCID
classification q-bio.PEphysics.bio-ph
keywords specializationpublicgoodsTuringpatternsmicrobialcooperationfluidshearcheaterresistancespatialassortmentevolutionarygametheory
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that the physical transport of chemicals—how fast the two public goods diffuse, how quickly they decay, and how the surrounding fluid flows—can be as important as fitness costs and benefits in deciding whether a microbial community evolves toward generalism, specialism, or exploitation. It builds a first-principles simulation of bacteria that secrete two public goods and a waste product, letting these chemicals diffuse, decay, and be carried by fluid motion, and asks which cooperative phenotypes win under different physical conditions. The central finding is that communities can resist evolutionary predictions made from invasion fitness alone: generalists can hold off specialists, both can hold off cheaters, and different community structures can coexist in one fluid niche. The result matters because it suggests that division of labor in microbes is partly a mechanical phenomenon, so the physical environment could be used to control social evolution experimentally or industrially.

What carries the argument

The load-bearing machinery is a reaction-diffusion-advection model of seven coupled fields: number densities of four phenotypes (generalist, two specialists, cheater) and concentrations of two public goods plus waste. Growth follows saturating Monod/Hill kinetics with either AND logic (both goods required) or OR logic (goods substitute), mutations toggle secretion functions on and off, and chemicals diffuse, decay, and are advected by the flow field. A Turing instability—waste diffusing faster than public goods—produces the self-organized cooperative clusters that are the real evolutionary units; an effective group model then tracks these clusters as growing, fragmenting entities. The key quantities are the fragmentation rates of group types measured from simulation, compared with mutation supply: if fragmentation outruns mutation, the group type is stable; if mutation outruns fragmentation, the group is taken over. This is what connects physical parameters (diffusion constants, decay rates, flow shear) to evolutionary outcomes.

What would settle it

A controlled microfluidic experiment with an engineered non-motile, non-adherent bacterium secreting two diffusible public goods could test the claim: if measured population composition across increasing shear rates does not shift from generalist to specialist, or if generalists are already absent at low shear where invasion fitness predicts specialization, the proposed dominance of physical transport over fitness economics would be contradicted. Alternatively, measuring group fragmentation and mutation supply directly and showing that specialists win even when fragmentation outruns mutation would break the mechanism.

Watch

Extended reading notes

Core claim

The paper claims that abiotic transport can override fitness economics in determining microbial community structure. In the model, microbes that secrete both public goods (generalists), one good (specialists), or none (cheaters) compete while their secretions and metabolic waste diffuse, decay, and advect in a flowing fluid. Because waste diffuses faster than public goods under the chosen parameters, the population self-organizes into Turing-like spots and stripes—cooperative clusters that grow, fragment, and reproduce as units. Group structure then governs evolution: large, dense groups generate more mutant specialists or cheaters and are taken over, whereas small, frequently fragmenting groups shed mutants and remain stable. The paper reports three results that contradict pure fitness-economics reasoning: generalist communities can resist invasion by specialists despite specialists' higher invasion fitness; generalist and specialist communities can resist cheaters despite the free-riding fitness advantage; and multiple community structures can coexist in a single fluid niche, including stable coexistence across shear zones in pipe and vortex flows.

Load-bearing premise

The central claim assumes that real microbial community structure is set by abiotic diffusion-advection instabilities among passive point-particle cells, and the parameters are chosen to make those Turing patterns strong and sensitive to cost; if active biological clustering (taxis, adhesion, or flow feedback) actually determines group formation, the override of fitness economics could be an artifact of that choice.

Editorial extensions

If this is right

  • A shearing flow promotes specialization: shear fragments specialist groups faster and enlarges generalist groups, making generalists more mutation-prone, so increasing shear can switch a coexisting population to a specialist state.
  • Spatially varying shear, as in a pipe or vortex, partitions the niche: generalists persist in low-shear regions, specialists in high-shear regions, and in some regions all three types coexist, counteracting competitive exclusion.
  • High waste diffusion and high public-good benefit favor specialists and cheaters; high secretion cost favors generalists; there are two distinct extinction regimes, one driven by cheater takeover of 'too fit' groups and one by self-pollution of dense groups.
  • The AND fitness form enables true division of labor with mixed specialist groups, while the OR form yields pure specialist groups, because specialist mutations sweep generalist groups before complementary specialists arise.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the mechanism is right, any manipulation that changes group fragmentation—stirring rate, confinement geometry, biofilm matrix strength, or cell-cell adhesion—should be a practical lever for steering microbial communities toward or away from specialization, even when payoff parameters are unchanged.
  • The paper notes asymmetric diffusion or decay between the two goods is unexplored; a natural extension is that the more private (shorter diffusion length) good will be produced by more microbes and the more public good will be exploited more heavily, a prediction that could be tested by varying the molecular size of the two goods.
  • Because microbes are modeled as passive point particles with no taxis, adhesion, or feedback on flow, an equally plausible world is one where active clustering dominates; comparing motile or adherent strains with non-motile passive ones under identical flow would show whether abiotic transport alone is the causal driver.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper presents a mechanistic simulation and analytical study of the evolution of specialization in microbial communities that secrete two public goods and a waste compound in a fluid environment. The authors use agent-based stochastic simulations coupled to diffusion-advection equations for three chemicals, with two fitness architectures (AND and OR), and supplement these with a Turing instability analysis of homogeneous generalist and specialist states. They then extract group-level fragmentation rates and group sizes from the simulations and build a mean-field 'effective group model' to reproduce the simulated proportions of generalists, specialists, and cheaters. The central claims are that abiotic physical factors—diffusion constants, molecular decay rates, and fluid flow patterns—can be as influential as fitness economics, so that generalists can resist invasion by specialists, cooperators can resist cheaters, and multiple community structures can coexist despite competitive exclusion. The main results are presented as phase diagrams and flow-profile-dependent spatial patterns (Figs. 4-6).

Significance. If the central claim is supported, the paper is significant: it demonstrates a mechanism by which passive transport processes can alter evolutionary outcomes in social microbial systems, going beyond invasion-fitness arguments. The manuscript has notable strengths: the Turing analysis is a genuine linear-stability derivation, the agent-based model is described in enough detail that the source code is provided, and the qualitative agreement between the pattern-formation region and simulations (Fig. 3) is a useful validation. The prediction that higher secretion cost can favor generalists over specialists, and that spatially varying shear can create coexistence, is falsifiable in principle. However, the broader claim that physical factors are 'as influential as fitness economics' currently rests on a parameterized regime and on an effective model calibrated on the same simulations it explains, which weakens the generality of the conclusion.

major comments (3)
  1. [Supplementary Section III; Fig. 4] The effective-group model is not an independent theoretical test. The fragmentation/extinction rates (rg, rt, rm, rc, rp) and group sizes (mg, mt, mm, mc, mp) are measured from the same complex simulations whose outcomes the model then reproduces in Fig. 4. The agreement is therefore partly by construction, not an independent prediction. To support the claim that the analytical formulas 'match' the simulations, the authors should either derive these rates from the Turing-scale physics, or explicitly frame the effective model as a descriptive reduction and validate it out-of-sample (e.g., predict Fig. 5 or a different parameter region without re-measuring the rates). As written, the theoretical curves in Fig. 4 do not provide independent evidence for the abstract's claim that physical factors override fitness economics.
  2. [Fig. 5 caption] The phase diagrams in Fig. 5 are generated from a single simulation run per parameter set, time-averaged over T = 1e6 to 2e6 s. With stochastic mutation, reproduction, and death, single-run outcomes are not sufficient to establish phase boundaries, particularly the extinction and coexistence regions. The authors should report multiple independent runs (at least the 5 used in Fig. 4) with variability measures, or provide convergence diagnostics showing that the time averages are stationary and independent of initial conditions. Without this, the quantitative structure of the phase diagrams is not robustly supported.
  3. [Supplementary Section II; Discussion] The broad causal claim in the abstract that diffusion, flow, and decay are 'as influential as fitness economics' is not established for microbial communities generally. The parameters were explicitly chosen to produce strong Turing patterns and 'sensitive dependence on cost' (Supplementary Section II), and the model omits taxis, adhesion, and microbial feedback on flow, as acknowledged in the Discussion. The paper therefore demonstrates a possible mechanism for passive point particles in a selected parameter regime, but not that physical factors are generally as influential as fitness. The authors should either soften the general claim or add a systematic sensitivity analysis over a wider range of diffusion/decay ratios and including weak-patterning regimes, to show that the reported phenomena are not an artifact of the chosen parameter region. This is a correctness-risk concern about external validity, not an internal inconsistency.
minor comments (5)
  1. [Introduction, references] The reference list contains literal '?' placeholders, e.g., '(Cooper and West 2018, Gavrilets 2010,?, Ispolatov et al. 2012...)' and '(Willensdorfer 2008,?)'. These incomplete citations should be completed before publication.
  2. [Results, 'Fluid dyanmical forces'] There is a typo in the Results section: 'Fluid dyanmical forces' should be 'Fluid dynamical forces'.
  3. [Fig. 5h caption] The caption of Fig. 5h contains 'fintess' which should be 'fitness'.
  4. [Supplementary Section II] The text states 'We see in Fig. 1 that varying decay and secretion rates...' but the referenced figure appears to be Supplementary Figure 1, not main-text Fig. 1. The cross-reference should be corrected.
  5. [Effective model, Eqs. (5)-(8)] The factor 1/2 in the mutation terms is stated to be an approximation for fixation probability. The authors note that the exact fixation probability could be added as a measured parameter; since this factor is used in the central stability conditions (e.g., rg > max(μmg/2, ...)), a sensitivity check on this assumption would be useful.

Circularity Check

1 steps flagged · score 6.0 of 10

The main analytical curves (Fig. 4) are produced by an effective model whose key parameters are measured from the very same simulations it is then used to reproduce, making that agreement a consistency check rather than an independent prediction.

  1. fitted input called prediction [Methods, 'Simple Effective Model'; Supplementary Section III 'Effective Group Models']
    "These parameters are “measured” from our complex simulations and depend on the physical properties of the system (see Supplementary Figures 2,3). The results of our effective model are compared to simulation results in Fig. 4. ... Parameter values were obtained from simulations without mutations to determine the natural state and growth of an isolated phenotype."

    The effective model's inputs—group fragmentation rates, extinction rates, group sizes, and carrying capacities—are measured by fitting to the same agent-based simulations whose outcomes the model is then used to reproduce in Fig. 4. The steady-state composition computed from Eqs. (5)–(11) is a direct function of these measured rates (e.g., generalists persist when rg > max(µmg/2, mgrm/mm)), so the agreement between the solid curves and the simulation points is not an independent test of the model; it is a consistency check of the coarse-graining.

full rationale

The agent-based simulations and the Turing analysis are self-contained and not circular: the pattern-forming region shown in Fig. 3 is derived from a linear stability analysis of the model equations and compared to independent simulations, and the simulation outcomes themselves follow from the specified stochastic rules. The circularity is confined to the effective group model of Supplementary Section III, which is calibrated on simulation outputs (fragmentation rates, group sizes, carrying capacities) and then used to generate the theoretical curves in Fig. 4. Because those curves are the paper's main quantitative support for the claim that generalists can resist specialists at high cost, one central piece of the argument reduces to fitting the same simulation data it claims to explain. The overall score reflects that this is a partial circularity: the qualitative simulation results are independent, but the 'matching analytical formulas' advertised in the abstract and results are not independent predictions. The discussion's acknowledged neglect of taxis, adhesion, and microbial feedback on flow is a threat to external validity, not a circularity, and the authors' self-citation for shear-enhanced fragmentation is backed by the present simulations as well.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities. Its central claims rest on a heavily parameterized simulation model, a derived Turing condition, and an effective group-level model whose parameters are fitted to the simulations rather than independently measured. The listed free parameters and domain assumptions are the load-bearing inputs.

free parameters (3)
  • Effective model fragmentation/extinction rates (rg, rt, rm, rc/rp) and group sizes (mg, mt, mm, mc/mp) = Measured from agent-based simulations; values shown only in Supplementary Figures 2 and 3
    These are calibrated on the same simulations the effective model is compared with, so the solid curves in Fig. 4 are fits rather than independent predictions.
  • Mutation fixation probability factor (1/2) = 0.5
    The factor 1/2 for a mutation fixing before group division is an ad hoc approximation chosen 'for the sake of simplicity' (Supplementary Section III), not derived from a fixation probability model.
  • Diffusion constants d1, d2, dw and flow parameters = e.g., d1 = d2 = 5e-6 cm2/s, dw = 15e-6 cm2/s; flow rates in figure captions
    Diffusion constants were chosen 'to give sensitive dependence on cost and benefit parameters' and to produce Turing patterns (Methods, System Parameters), so the observed strong physical effects are partly a consequence of selecting regimes where they matter.
assumptions (4)
  • domain assumption Fitness is a saturating (Hill) function of public good concentration and waste concentration (Eq. 3 and 4)
    The Monod-type functional forms are standard and empirically motivated, but they are a modeling choice that determines when specialization is favored.
  • domain assumption Waste is produced at no metabolic cost, and public-good secretion is binary with symmetric mutation rates between phenotypes (M matrix)
    Stated in Methods; the symmetry of mutations is acknowledged as an idealization in the Discussion.
  • domain assumption Microbes are passive point particles in a prescribed flow field; there is no taxis, adhesion, or cell-chemistry feedback on the fluid
    The authors list these as simplifications in the Discussion; the central claim about physical factors requires that diffusion and advection, not active biological transport, dominate group formation.
  • standard math Linear Turing stability analysis of the no-mutation, no-flow reaction-diffusion system gives the pattern-forming region used to interpret the simulations
    Standard linear stability analysis; the result is a genuine derivation from the model equations in Supplementary Section I.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Evolution of specialized microbial cooperation in dynamic fluids." pith.science (2026). https://pith.science/paper/2F6Y2HO6

@misc{pith2026190807884,
  author       = {Pith},
  title        = {Pith review of: Evolution of specialized microbial cooperation in dynamic fluids},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2F6Y2HO6}},
  note         = {Machine review of arXiv:1908.07884}
}
read the original abstract

Here, we study the evolution of specialization using realistic computer simulations of bacteria that secrete two public goods in a dynamic fluid. Through this first principles approach, we find physical factors such as diffusion, flow patterns, and decay rates are as influential as fitness economics in governing the evolution of community structure, to the extent that when mechanical factors are taken into account, (1) Generalist communities can resist becoming specialists, despite the invasion fitness of specialization, (2) Generalist and specialists can both resist cheaters despite the invasion fitness of free-riding, (3) Multiple community structures can coexist despite the opposing force of competitive exclusion. Our results emphasize the role of spatial assortment and physical forces on niche partitioning and the evolution of diverse community structures.

Figures

Figures reproduced from arXiv: 1908.07884 by the authors.

Figure 1
Figure 1. FIG. 1 [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3 [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: FIG. 4 [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5 [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6 [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

88 extracted references · 80 canonical work pages

  1. [1]

    C., McNally, L., Popat, R., and Brown, S

    Allen, R. C., McNally, L., Popat, R., and Brown, S. P. (2016). Quorum sensing protects bacterial co-operation from exploitation by cheats. The ISME journal

    2016

  2. [2]

    Allen, R. J. and Waclaw, B. (2018). Bacterial growth: A statistical physicist’s guide. Reports on Progress in Physics , 82(1):016601

    2018

  3. [3]

    An, D., Danhorn, T., Fuqua, C., and Parsek, M. R. (2006). Quorum sensing and motility mediate interactions between pseudomonas aeruginosa and agrobacterium tumefaciens in biofilm cocultures. Proceedings of the National Academy of Sciences , 103(10):3828--3833

    2006

  4. [4]

    Bachmann, H., Molenaar, D., Kleerebezem, M., and van Hylckama Vlieg, J. E. (2011). High local substrate availability stabilizes a cooperative trait. The ISME journal , 5(5):929

    2011

  5. [5]

    Beshers, S. N. and Fewell, J. H. (2001). Models of division of labor in social insects. Annual review of entomology , 46(1):413--440

    2001

  6. [6]

    Biancalani, T., Fanelli, D., and Di Patti, F. (2010). Stochastic turing patterns in the brusselator model. Physical Review E , 81(4):046215

    2010

  7. [7]

    and Eisenbach, M

    Blat, Y. and Eisenbach, M. (1995). Tar-dependent and-independent pattern formation by salmonella typhimurium. Journal of bacteriology , 177(7):1683--1691

    1995

  8. [8]

    Bonner, J. T. (1998). The origins of multicellularity. Integrative Biology: Issues, News, and Reviews: Published in Association with The Society for Integrative and Comparative Biology , 1(1):27--36

    1998

Show all 88 references
  1. [9]

    Budrene, E. O. and Berg, H. C. (1991). Complex patterns formed by motile cells of escherichia coli. Nature , 349(6310):630

    1991

  2. [10]

    and Goldenfeld, N

    Butler, T. and Goldenfeld, N. (2009). Robust ecological pattern formation induced by demographic noise. Physical Review E , 80(3):030902

    2009

  3. [11]

    Carroll, S. B. (2001). Chance and necessity: the evolution of morphological complexity and diversity. Nature , 409(6823):1102

    2001

  4. [12]

    Cooper, G. A. and West, S. A. (2018). Division of labour and the evolution of extreme specialization. Nature ecology & evolution , 2(7):1161

    2018

  5. [13]

    Davies, D. (2003). Understanding biofilm resistance to antibacterial agents. Nature reviews Drug discovery , 2(2):114

    2003

  6. [14]

    Dobay, A., Bagheri, H., Messina, A., K \"u mmerli, R., and Rankin, D. (2014). Interaction effects of cell diffusion, cell density and public goods properties on the evolution of cooperation in digital microbes. Journal of evolutionary biology , 27(9):1869--1877

    2014

  7. [15]

    Drago s , A., Kiesewalter, H., Martin, M., Hsu, C.-Y., Hartmann, R., Wechsler, T., Eriksen, C., Brix, S., Drescher, K., Stanley-Wall, N., et al. (2018). Division of labor during biofilm matrix production. Current Biology , 28(12):1903--1913

    2018

  8. [16]

    W., Charlesworth, B., Charlesworth, D., and Crow, J

    Drake, J. W., Charlesworth, B., Charlesworth, D., and Crow, J. F. (1998). Rates of spontaneous mutation. Genetics , 148(4):1667--1686

    1998

  9. [17]

    and Levin, S

    Durrett, R. and Levin, S. (1994). The importance of being discrete (and spatial). Theoretical population biology , 46(3):363--394

    1994

  10. [18]

    Fletcher, J. A. and Doebeli, M. (2009). A simple and general explanation for the evolution of altruism. Proceedings of the Royal Society of London B: Biological Sciences , 276(1654):13--19

    2009

  11. [19]

    H., He, C., Vural, D

    Fu, X., Kato, S., Long, J., Mattingly, H. H., He, C., Vural, D. C., Zucker, S. W., and Emonet, T. (2018). Spatial self-organization resolves conflicts between individuality and collective migration. Nature communications , 9(1):2177

    2018

  12. [20]

    Futuyma, D. J. and Moreno, G. (1988). The evolution of ecological specialization. Annual review of Ecology and Systematics , 19(1):207--233

    1988

  13. [21]

    Gavrilets, S. (2010). Rapid transition towards the division of labor via evolution of developmental plasticity. PLoS computational biology , 6(6):e1000805

    2010

  14. [22]

    J., Feil, E., and Eyre-Walker, A

    Gibson, B., Wilson, D. J., Feil, E., and Eyre-Walker, A. (2018). The distribution of bacterial doubling times in the wild. Proceedings of the Royal Society B: Biological Sciences , 285(1880):20180789

    2018

  15. [23]

    J., Dornhaus, A., Kerr, B., and Ofria, C

    Goldsby, H. J., Dornhaus, A., Kerr, B., and Ofria, C. (2012). Task-switching costs promote the evolution of division of labor and shifts in individuality. Proceedings of the National Academy of Sciences , 109(34):13686--13691

    2012

  16. [24]

    and Travisano, M

    Greig, D. and Travisano, M. (2004). The prisoner's dilemma and polymorphism in yeast suc genes. Proceedings of the Royal Society of London B: Biological Sciences , 271(Suppl 3):S25--S26

    2004

  17. [25]

    S., West, S

    Griffin, A. S., West, S. A., and Buckling, A. (2004). Cooperation and competition in pathogenic bacteria. Nature , 430(7003):1024

    2004

  18. [26]

    Guerinot, M. L. (1994). Microbial iron transport. Annual Reviews in Microbiology , 48(1):743--772

    1994

  19. [27]

    Harrison F, B. A. (2009). Cooperative production of siderophores by pseudomonas aeruginosa. Frontiers in Bioscience , 14(14):4113--26

    2009

  20. [28]

    B., Blair, J

    Hedges, S. B., Blair, J. E., Venturi, M. L., and Shoe, J. L. (2004). A molecular timescale of eukaryote evolution and the rise of complex multicellular life. BMC evolutionary biology , 4(1):2

    2004

  21. [29]

    F., Gardner, A., Cornelis, P., and Buckling, A

    Inglis, R. F., Gardner, A., Cornelis, P., and Buckling, A. (2009). Spite and virulence in the bacterium pseudomonas aeruginosa. Proceedings of the National Academy of Sciences , 106(14):5703--5707

    2009

  22. [30]

    Ispolatov, I., Ackermann, M., and Doebeli, M. (2012). Division of labour and the evolution of multicellularity. Proceedings of the Royal Society of London B: Biological Sciences , 279(1734):1768--1776

    2012

  23. [31]

    JB, N. (1984). Siderophores of bacteria and fungi. Microbiological sciences , 1(1):9--14

    1984

  24. [32]

    M., Lu, T., DeLateur, N

    Karig, D., Martini, K. M., Lu, T., DeLateur, N. A., Goldenfeld, N., and Weiss, R. (2018). Stochastic turing patterns in a synthetic bacterial population. Proceedings of the National Academy of Sciences , 115(26):6572--6577

    2018

  25. [33]

    Kearns, D. B. (2010). A field guide to bacterial swarming motility. Nature Reviews Microbiology , 8(9):634

    2010

  26. [34]

    Kim, Y.-C. (1996). Diffusivity of bacteria. Korean Journal of Chemical Engineering , 13(3):282--287

    1996

  27. [35]

    Kirk, D. L. (2001). Germ--soma differentiation in volvox. Developmental biology , 238(2):213--223

    2001

  28. [36]

    Kirk, D. L. and Kirk, M. M. (1983). Protein synthetic patterns during the asexual life cycle of volvox carteri. Developmental biology , 96(2):493--506

    1983

  29. [37]

    Kohler T, Buckling A, V. D. C. (2009). Cooperation and virulence of clinical pseudomonas aeruginosa populations. Proceedings of the National Academy of Sciences , 106(14):6339--44

    2009

  30. [38]

    Koufopanou, V. (1994). The evolution of soma in the volvocales. The American Naturalist , 143(5):907--931

    1994

  31. [39]

    and Niklas, K

    Kutschera, U. and Niklas, K. J. (2005). Endosymbiosis, cell evolution, and speciation. Theory in Biosciences , 124(1):1--24

    2005

  32. [40]

    Kümmerli R, B. S. (2010). Molecular and regulatory properties of a public good shape the evolution of cooperation. Proceedings of the National Academy of Sciences , 107(107):18921--6

    2010

  33. [41]

    Lewis, K. (2007). Persister cells, dormancy and infectious disease. Nature Reviews Microbiology , 5(1):48

    2007

  34. [42]

    and Baalen, M

    Lion, S. and Baalen, M. v. (2008). Self-structuring in spatial evolutionary ecology. Ecology Letters , 11(3):277--295

    2008

  35. [43]

    Ma, Y., Zhu, C., Ma, P., and Yu, K. (2005). Studies on the diffusion coefficients of amino acids in aqueous solutions. Journal of Chemical & Engineering Data , 50(4):1192--1196

    2005

  36. [44]

    Mah, T.-F. C. and O'toole, G. A. (2001). Mechanisms of biofilm resistance to antimicrobial agents. Trends in microbiology , 9(1):34--39

    2001

  37. [45]

    May, R. M. and Seger, J. (1986). Ideas in ecology. American Scientist , 74(3):256--267

    1986

  38. [46]

    Mazzola, M., Cook, R., Thomashow, L., Weller, D., and Pierson, L. (1992). Contribution of phenazine antibiotic biosynthesis to the ecological competence of fluorescent pseudomonads in soil habitats. Appl. Environ. Microbiol. , 58(8):2616--2624

    1992

  39. [47]

    P., Hammer, B

    McNally, L., Bernardy, E., Thomas, J., Kalziqi, A., Pentz, J., Brown, S. P., Hammer, B. K., Yunker, P. J., and Ratcliff, W. C. (2017). Killing by type vi secretion drives genetic phase separation and correlates with increased cooperation. Nature communications , 8:14371

    2017

  40. [48]

    Mendelson, N. H. and Lega, J. (1998). A complex pattern of traveling stripes is produced by swimming cells of bacillus subtilis. Journal of bacteriology , 180(13):3285--3294

    1998

  41. [49]

    and Korolev, K

    Menon, R. and Korolev, K. S. (2015). Public good diffusion limits microbial mutualism. Physical review letters , 114(16):168102

    2015

  42. [50]

    Michod, R. E. (2007). Evolution of individuality during the transition from unicellular to multicellular life. Proceedings of the National Academy of Sciences , 104(suppl 1):8613--8618

    2007

  43. [51]

    E., Viossat, Y., Solari, C

    Michod, R. E., Viossat, Y., Solari, C. A., Hurand, M., and Nedelcu, A. M. (2006). Life-history evolution and the origin of multicellularity. Journal of theoretical Biology , 239(2):257--272

    2006

  44. [52]

    Monod, J. (1949). The growth of bacterial cultures. Annual Reviews in Microbiology , 3(1):371--394

    1949

  45. [53]

    Moons, P., Van Houdt, R., Aertsen, A., Vanoirbeek, K., Engelborghs, Y., and Michiels, C. W. (2006). Role of quorum sensing and antimicrobial component production by serratia plymuthica in formation of biofilms, including mixed biofilms with escherichia coli. Appl. Environ. Mic...

    2006

  46. [54]

    Moons, P., Van Houdt, R., Aertsen, A., Vanoirbeek, K., and Michiels, C. (2005). Quorum sensing dependent production of antimicrobial component influences establishment of e. coli in dual species biofilms with serratia plymuthica. Communications in agricultural and applied biol...

    2005

  47. [55]

    J., Lenski, R

    Morris, J. J., Lenski, R. E., and Zinser, E. R. (2012). The black queen hypothesis: evolution of dependencies through adaptive gene loss. MBio , 3(2):e00036--12

    2012

  48. [56]

    D., Ricaurte, D., Yan, J., Drescher, K., and Bassler, B

    Nadell, C. D., Ricaurte, D., Yan, J., Drescher, K., and Bassler, B. L. (2017). Flow environment and matrix structure interact to determine spatial competition in pseudomonas aeruginosa biofilms. Elife , 6:e21855

    2017

  49. [57]

    M., Niehus, R., and Foster, K

    Oliveira, N. M., Niehus, R., and Foster, K. R. (2014). Evolutionary limits to cooperation in microbial communities. Proceedings of the National Academy of Sciences , 111(50):17941--17946

    2014

  50. [58]

    and Bonhoeffer, S

    Pfeiffer, T. and Bonhoeffer, S. (2003). An evolutionary scenario for the transition to undifferentiated multicellularity. Proceedings of the National Academy of Sciences , 100(3):1095--1098

    2003

  51. [59]

    Pirhonen, M., Flego, D., Heikinheimo, R., and Palva, E. T. (1993). A small diffusible signal molecule is responsible for the global control of virulence and exoenzyme production in the plant pathogen erwinia carotovora. The EMBO journal , 12(6):2467--2476

    1993

  52. [60]

    and Dover, L

    Ratledge, C. and Dover, L. G. (2000). Iron metabolism in pathogenic bacteria. Annual reviews in microbiology , 54(1):881--941

    2000

  53. [61]

    E., Bernasconi, M

    Rossetti, V., Schirrmeister, B. E., Bernasconi, M. V., and Bagheri, H. C. (2010). The evolutionary path to terminal differentiation and division of labor in cyanobacteria. Journal of Theoretical Biology , 262(1):23--34

    2010

  54. [62]

    Rueffler, C., Hermisson, J., and Wagner, G. P. (2012). Evolution of functional specialization and division of labor. Proceedings of the National Academy of Sciences , 109(6):E326--E335

    2012

  55. [63]

    and Stocker, R

    Rusconi, R. and Stocker, R. (2015). Microbes in flow. Current opinion in microbiology , 25:1--8

    2015

  56. [64]

    and Hollowell, A

    Sachs, J. and Hollowell, A. (2012). The origins of cooperative bacterial communities. MBio , 3(3):e00099--12

    2012

  57. [65]

    M., Mitzimberg, S

    Sandoz, K. M., Mitzimberg, S. M., and Schuster, M. (2007). Social cheating in pseudomonas aeruginosa quorum sensing. Proceedings of the National Academy of Sciences , 104(40):15876--15881

    2007

  58. [66]

    T., Ross-Gillespie, A., Cornforth, D

    Schiessl, K. T., Ross-Gillespie, A., Cornforth, D. M., Weigert, M., Bigosch, C., Brown, S. P., Ackermann, M., and K \"u mmerli, R. (2019). Individual-versus group-optimality in the production of secreted bacterial compounds. Evolution , 73(4):675--688

    2019

  59. [67]

    Siegel, R. (1960). Hereditary endosymbiosis in paramecium bursaria. Experimental cell research , 19(2):239--252

    1960

  60. [68]

    R., Toth, A

    Smith, C. R., Toth, A. L., Suarez, A. V., and Robinson, G. E. (2008). Genetic and genomic analyses of the division of labour in insect societies. Nature Reviews Genetics , 9(10):735

    2008

  61. [69]

    A., Kessler, J

    Solari, C. A., Kessler, J. O., and Goldstein, R. E. (2013). A general allometric and life-history model for cellular differentiation in the transition to multicellularity. The American Naturalist , 181(3):369--380

    2013

  62. [70]

    A., Kessler, J

    Solari, C. A., Kessler, J. O., and Michod, R. E. (2006). A hydrodynamics approach to the evolution of multicellularity: flagellar motility and germ-soma differentiation in volvocalean green algae. The American Naturalist , 167(4):537--554

    2006

  63. [71]

    Sriswasdi, S., Yang, C.-c., and Iwasaki, W. (2017). Generalist species drive microbial dispersion and evolution. Nature communications , 8(1):1162

    2017

  64. [72]

    M., Johnson, E

    Stump, S. M., Johnson, E. C., and Klausmeier, C. A. (2018). Local interactions and self-organized spatial patterns stabilize microbial cross-feeding against cheaters. Journal of The Royal Society Interface , 15(140):20170822

    2018

  65. [73]

    Tannenbaum, E. (2007). When does division of labor lead to increased system output? Journal of theoretical biology , 247(3):413--425

    2007

  66. [74]

    Taylor, P. D. (1992). Altruism in viscous populations—an inclusive fitness model. Evolutionary ecology , 6(4):352--356

    1992

  67. [75]

    Terborgh, J. (1986). Guilds and their utility in ecology. Community ecology: pattern and process

    1986

  68. [76]

    Tsimring, L., Levine, H., Aranson, I., Ben-Jacob, E., Cohen, I., Shochet, O., and Reynolds, W. N. (1995). Aggregation patterns in stressed bacteria. Physical review letters , 75(9):1859

    1995

  69. [77]

    and Vural, D

    Uppal, G. and Vural, D. C. (2018). Shearing in flow environment promotes evolution of social behavior in microbial populations. eLife , 7:e34862

    2018

  70. [78]

    C., Isakov, A., and L., M

    Vural, D. C., Isakov, A., and L., M. (2015). The organization and control of an evolving interdependent population. Proceedings of the Royal Society, B , 12(108):20150044

    2015

  71. [79]

    Y., Nowak, M

    Wakano, J. Y., Nowak, M. A., and Hauert, C. (2009). Spatial dynamics of ecological public goods. Proceedings of the National Academy of Sciences , 106(19):7910--7914

    2009

  72. [80]

    A., Diggle, S

    West, S. A., Diggle, S. P., Buckling, A., Gardner, A., and Griffin, A. S. (2007). The social lives of microbes. Annu. Rev. Ecol. Evol. Syst. , 38:53--77

    2007

  73. [81]

    A., Fisher, R

    West, S. A., Fisher, R. M., Gardner, A., and Kiers, E. T. (2015). Major evolutionary transitions in individuality. Proceedings of the National Academy of Sciences , 112(33):10112--10119

    2015

  74. [82]

    Willensdorfer, M. (2008). Organism size promotes the evolution of specialized cells in multicellular digital organisms. Journal of evolutionary biology , 21(1):104--110

    2008

  75. [83]

    Willensdorfer, M. (2009). On the evolution of differentiated multicellularity. Evolution: International Journal of Organic Evolution , 63(2):306--323

    2009

  76. [84]

    Wilson, W., Morris, W., and Bronstein, J. (2003). Coexistence of mutualists and exploiters on spatial landscapes. Ecological monographs , 73(3):397--413

    2003

  77. [85]

    B., Kim, W., and Foster, K

    Xavier, J. B., Kim, W., and Foster, K. R. (2011). A molecular mechanism that stabilizes cooperative secretions in pseudomonas aeruginosa. Molecular microbiology , 79(1):166--179

    2011

  78. [86]

    R., Gruden, C

    Xue, Z., Sendamangalam, V. R., Gruden, C. L., and Seo, Y. (2012). Multiple roles of extracellular polymeric substances on resistance of biofilm and detached clusters. Environmental science & technology , 46(24):13212--13219

    2012

  79. [87]

    R., Bork, P., and Patil, K

    Zelezniak, A., Andrejev, S., Ponomarova, O., Mende, D. R., Bork, P., and Patil, K. R. (2015). Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proceedings of the National Academy of Sciences , 112(20):6449--6454

    2015

  80. [88]

    B., Vance, R

    Zhu, J., Miller, M. B., Vance, R. E., Dziejman, M., Bassler, B. L., and Mekalanos, J. J. (2002). Quorum-sensing regulators control virulence gene expression in vibrio cholerae. Proceedings of the National Academy of Sciences , 99(5):3129--3134

    2002

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.