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REVIEW 2 major objections 6 minor 95 references

Bacterial proliferation pattern formation

T0 review · 2 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The bands a cloned bacterial population forms in a gel column are modular: the bottom band and the top bands are controlled by separate acid and base mechanisms, and each can be tuned independently.

desk verdict A solid experimental core establishes reproducible, modular bacterial bands; the proposed pH-neutralization mechanism for top bands is plausible but indirect. read the letter →

arxiv 2501.09546 v1 pith:LKFTZMJ5 submitted 2025-01-16 q-bio.PE nlin.AOnlin.PSphysics.bio-phq-bio.CB

John S. Chuang , Riccardo Rao , Stanislas Leibler This is my paper · ORCID
classification q-bio.PEnlin.AOnlin.PSphysics.bio-phq-bio.CB
keywords bacterialpatternformationgel-stabilizedgradientsystemmetabolicstatespHmodularitylateralinhibitionanomalousdiffusionSerratiamarcescens
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

Sequential high-density bands of bacteria growing in a gel column, a phenomenon first seen more than eighty years ago, turn out to be assembled from independent modules. The paper shows, with time-lapse imaging of replicate columns, that while the position of each band is highly reproducible across replicates, its time of appearance is not, and that band positions follow a subdiffusive scaling law. The central claim is modularity: the bottom band is governed by acid produced by glucose fermentation just above the glucose layer, while the upper bands are governed by a base countergradient, ammonia released by amino-acid catabolism at the air interface, that neutralizes that acid where the bands form. This is supported by gene-expression data showing bottom and top bands run different genetic programs, and by manipulations that abolish one band group while leaving the other intact. A two-state model with an acid-induced hysteresis reproduces both band formation and the modular control.

What carries the argument

The paper's explanatory core is a two-state reaction-diffusion model with hysteresis. Cells sit in a quiescent state $S_0$ and switch to a proliferating state $S_1$ only when the glucose concentration exceeds $g^*$ and the proton concentration is below a lower threshold $h^*$; proliferation continues as long as glucose is high and protons stay below a higher threshold $h^{**}$ with $h^* < h^{**}$, so there is a window of acid levels in which already-proliferating cells keep growing while quiescent cells remain idle. Proliferation consumes glucose and releases protons, and a diffusing base $B$ neutralizes protons by mass action. The lower boundary supplies glucose, the upper boundary supplies base (in the experiments, ammonia from amino-acid catabolism at the air interface, mimicked in the model by an added buffer), and the interaction of these two fronts creates the sequential bands. This mechanism instantiates local activation (glucose arrival) and lateral inhibition (proton buildup and glucose depletion), and the two-threshold hysteresis is what lets a band stay active while the front above it is still inhibited.

What would settle it

Directly measure the pH and ammonia concentration profiles along the gel column with microelectrodes or a pH dye (as in the paper's own movie) while tracking band appearance. If the countergradient picture is right, top bands should appear where the acidic front descending from the bottom meets the alkaline front descending from the top, and removing the ammonia source should abolish them; if the top bands persist when a non-basic nitrogen source is supplied, the base-neutralization role is not essential.

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Extended reading notes

Core claim

The paper claims that the proliferation pattern of an immobilized clonal bacterial population in a gel-stabilized gradient is modular: the bottom band and the top band group can be selectively turned on or off and shifted by separate experimental controls. RNA-seq of cells dissected from individual bands shows the bottom band is transcriptionally distinct from the top bands, and the top bands differ among themselves. Adding glucose to the cell layer removes the bottom band without eliminating the top bands, whereas capping the column with mineral oil, which stops aerobic amino-acid catabolism at the top, removes the top bands while leaving a dim bottom band. The paper attributes the bottom band to glucose fermentation and the protons that fermentation releases near the bottom interface, and the top bands to a countergradient of base (ammonia produced by amino-acid catabolism) that diffuses downward and neutralizes the fermentation acids; replacing the amino-acid nitrogen source with ammonium chloride destroys the top bands, and adding a nitrogen-free buffer at the top restores them. A reaction-diffusion model with cells in quiescent and proliferating states and two distinct pH thresholds (with $h^* < h^{**}$) reproduces band formation and reproduces the modular response to perturbations.

Load-bearing premise

The load-bearing premise is that the top bands form because ammonia released by amino-acid metabolism acts mainly as a diffusing base that neutralizes fermentation acid, rather than as a nitrogen source, and that cells really have two distinct pH thresholds with a hysteretic gap; both are inferred indirectly rather than measured directly.

Editorial extensions

If this is right

  • If modularity is real, the bottom band and the top bands can be targeted separately in experiments: fermentative acid production controls the bottom band, and any diffusing base supplied from the top controls the upper bands.
  • Because band position is reproducible to within about a millimeter while appearance times vary by hours to days, position and timing are set by different cues; the paper identifies acid/base countergradients as the positional cue and glucose arrival as the timing cue.
  • The observed subdiffusive scaling of band coordinates, $x^2 = K(t-\tau)^\alpha$ with $\alpha \approx 0.27$ and $\alpha$ varying with glucose and buffer concentration, means glucose consumption by bacteria slows transport and the scaling law itself can be used to detect metabolic load.
  • Modifying fermentation genes (e.g., $\Delta slaAB$), aerobic metabolism genes ($\Delta sucD$), or nitrogen-metabolism genes ($\Delta asnB$) shifts different bands in different ways, so genetic perturbations can be used to 'program' a desired pattern.
  • The system offers a model, more than eighty years old but only now made quantitative, for studying how primary metabolism responds to spatiotemporally heterogeneous chemical environments without the confound of cell motility.

Reading between the lines

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

  • Direct measurement of pH and ammonia profiles inside the gel would settle the paper's mechanism; the authors infer the base countergradient indirectly from NH4Cl and buffer rescues, and their model assumes it.
  • The two-threshold hysteresis ($h^* < h^{**}$) is the model's most distinctive assumption; if single-cell experiments showed quiescent and proliferating cells respond to the same pH threshold, the modularity explanation would need a different mechanism.
  • The same local-activation/lateral-inhibition framework might apply to other growth-inhibitor pairs (for example, oxygen and metabolic acid) in colonies or biofilms, and the paper's phase diagram in terms of the lateral-inhibition scale $\Lambda$ and threshold ratio $h^*/h^{**}$ gives a guide for where to look.
  • Mixing two strains in varying proportions changes the pattern continuously; this suggests a simple compositional control strategy could be developed where the final pattern is predicted from strain ratios and metabolic traits.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 6 minor

Summary. The paper presents a quantitative study of 'proliferation pattern formation' (PPF) in gel-stabilized columns of Serratia marcescens, in which immobilized bacterial cells proliferate in spatially periodic bands as glucose diffuses from the bottom and oxygen is supplied from the top. Using time-lapse imaging of 12 replicate columns, the authors show that band positions are highly reproducible (standard deviation below measurement uncertainty) while appearance times vary increasingly across replicates, and that band coordinates obey a sub-diffusive scaling law x^2 = K(t−τ)^α with α≈0.27. They introduce the concept of 'modularity': the bottom band and the top bands can be modulated independently by changing the medium or top conditions (adding glucose to the cell layer, overlaying with mineral oil, replacing amino acids with NH4Cl, adding a top buffer), and cells in different bands have distinct transcriptomes. This modularity is interpreted through a pH-based mechanism in which fermentation acids are neutralized by a countergradient of ammonia released from aerobic amino acid catabolism at the top interface. A PDE model with two internal cell states (quiescent and proliferating) and hysteresis in proton thresholds reproduces the qualitative features of band formation and the effect of a top base source. Additional manipulations (single amino acids, glutamine concentration, red/white phenotypes, gene knockouts, strain mixing) further demonstrate the tunability of the patterns.

Significance. If the results hold, this paper makes a valuable contribution to microbial pattern formation by reviving and modernizing an 80-year-old experimental system, providing high-quality quantitative data on reproducibility and a scaling law, and demonstrating modular control of different bands. The strengths include the extensive replicate design (12 columns), multiple independent perturbations (glucose addition, oil overlay, NH4Cl replacement with buffer rescue, gene deletions), RNA-seq with three replicates per condition, and a transparent modeling approach whose parameters are not fitted to the specific pattern outputs. The model is a useful proof-of-concept that a local activation–lateral inhibition scheme with a diffusing base can reproduce modular band formation. The main weakness is that the central mechanistic interpretation—endogenous ammonia acts as a diffusing base that neutralizes fermentation acids—rests on indirect perturbation evidence, and the model relies on an unverified hysteresis assumption. Nevertheless, the empirical phenomenology of modularity and the quantitative characterization are significant regardless of the specific mechanism.

major comments (2)
  1. [§III, Fig. 3c; SI §IV] The claim that endogenous ammonia acts predominantly as a diffusing base that neutralizes fermentation acids is supported only indirectly. The key experiment replaces amino acids with NH4Cl (which disrupts top bands) and then restores top bands by adding 0.45–0.75 M potassium phosphate buffer at the top interface. However, the buffer also changes ionic strength, osmolarity, and phosphate availability, and the paper does not report quantitative pH or ammonia profiles in the NH4Cl-containing rescue conditions. The qualitative pH-dye movie (Mov. S2) is shown for glutamine columns, not for the rescue experiment. To make the mechanistic claim load-bearing, please provide direct pH measurements in the NH4Cl and NH4Cl+buffer conditions (or a calibrated dye, not just a movie), or include a control with a non-buffering osmolyte such as NaCl or sorbitol at matched osmolarity to rule out osmotic and ionic effects.
  2. [App. B, Eq. (B2); §V; Fig. S8] The hysteresis h* < h** is essential for the model's ability to produce bands and for the separation between bottom and top bands, as the phase diagram in Fig. S8 shows that band formation depends on this ratio. No direct experimental evidence is provided for the existence of two distinct proton thresholds for entry into versus maintenance of proliferation. Please either test this assumption (e.g., with pH-controlled growth experiments on cells in different physiological states) or explicitly reframe the model as a purely phenomenological illustration whose biological validity is not yet established. The current wording in the abstract and §V implies that the internal metabolic states and hysteresis are established properties, which overstates the evidence.
minor comments (6)
  1. [Fig. 3a; App. A] The caption of Fig. 3a does not state the number of replicate samples per band; the text in App. A mentions '3 replicates each', but the figure should carry this information as well, especially because the PCA separation is based on only three points per group.
  2. [SI §IV, Eq. (S5)] The conclusion that increasing buffer concentration decreases the scaling exponent α is based on fitting Eq. (S3) with buffer concentration as a regressor, but no model comparison or residual diagnostics are shown for this fit. Reporting the uncertainty in α as ±0.02 from a single ML fit may understate the model uncertainty given the small number of buffer concentrations.
  3. [§II and Discussion] The scaling-law fit in Fig. 2b treats each band coordinate within a replicate as an independent data point, but coordinates from the same column are correlated. Please show residuals or discuss whether the ML confidence intervals are robust to this correlation.
  4. [Mov. S2; §III] The pH-dye movie is qualitative and uses a single indicator dye (chlorophenol red). In the main text, this is cited as evidence of pH gradients; please state explicitly in the text that this is a qualitative measurement and not quantitative.
  5. [Equation (B5) and Fig. 3b] The model's initial condition for glucose places it exclusively in the Glc-layer, whereas the experimental perturbation in Fig. 3b adds glucose to the C-layer. The model therefore does not simulate the 'Glc' kymograph directly; please clarify whether the model can capture that perturbation or whether this is intended only as a qualitative analogy.
  6. [Header, first page] The manuscript header states 'Manuscript accepted in PRX Life'. In a submitted manuscript, this line is unusual and should be removed for peer review, as it may be taken as an indication that the review process is a formality.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the empirical modularity claims stand independently of the model, and the model's band simulations are a parameter-coarse sufficiency demonstration rather than a prediction derived from fitted targets.

full rationale

The paper's central claims (reproducibility, modularity, gene-expression differences, buffer rescue) are experimental and do not depend on the model for support. The model in App. B is not fitted to the band patterns it is said to reproduce; the main text states that parameters were 'coarsely adjusted within biologically-plausible ranges,' and App. B identifies only k_r, k_q, k_n, y_h, and h** as free parameters from a coarse parameter search, with no fitting to the kymograph positions or appearance times. The scaling law is explicitly an ML fit to band coordinates (Eq. S1, Tab. S4) and is presented as a descriptive characterization, not as a prediction generated from the model. The buffer-rescue experiment (Fig. 3c, SI Fig. S5) is an independent perturbation: NH4Cl suppresses top bands and nitrogen-free potassium phosphate buffer restores them, which is direct evidence for the base-countergradient hypothesis rather than a self-citation or circular inference. No uniqueness theorem or load-bearing self-citation is invoked; the cited SI ([43]) provides replicate data and controls. The unverified hysteresis assumption h* < h** (Eq. B2) is an acknowledged modeling assumption and a potential correctness risk, but it is not circular because the model's output is not used to prove the assumption. The model's reproduction of modularity is a sufficiency demonstration that the hypothesized acid/base ingredients can produce the phenomenology; it is not presented as an independent prediction, so it does not reduce to its inputs by construction.

Assumptions & free parameters 9 free parameters · 5 assumptions · 0 invented entities

No new physical entities are postulated; the model states S0/S1 are covered under axioms. The central experimental claims rest on standard microbiology and image analysis, while the theoretical model adds several free parameters and unverified biological assumptions (hysteretic states, ammonia-base dominance).

free parameters (9)
  • alpha (anomalous diffusion exponent) = 0.27 +/- 0.01 (with time-shift model; 0.43 without)
    Maximum-likelihood fit to positions and appearance times of bands (Eq. S1); central to the subdiffusive scaling claim but fitted, not predicted.
  • K (anomalous diffusion coefficient) = 18.1 +/- 0.3 cm^2 d^-alpha
    Fit parameter in x^2 = K(t-tau)^alpha; reported in Fig. 2b and SI Table S4.
  • tau (time lag) = 1.03 +/- 0.03 d
    Fit parameter in the scaling relation; included via BIC model selection.
  • kr (rate of transition to proliferation) = 1 h^-1
    Declared a free parameter; value from coarse parameter search (SI Tab. S6).
  • kq (rate of transition to quiescence) = 1e2 h^-1
    Declared a free parameter; value from coarse parameter search (SI Tab. S6).
  • kn (rate of neutralization) = 1e4 h^-1
    Declared a free parameter; value from coarse parameter search (SI Tab. S6).
  • yh (proton production per cell mass) = 0.005 g H+ / g DW
    Declared a free parameter; value is substantially smaller than E. coli's mixed-acid value, justified by S. marcescens 2,3-butanediol fermentation (SI Tab. S6).
  • h** (proton threshold for maintaining proliferation) = 5e-8 g H+ / cm (approx pH 4.5)
    Declared a free parameter; chosen from coarse search.
  • h* (proton threshold for entering proliferation) = 2.6e-8 g H+ / cm (approx pH 4.8)
    Set by hand just below the lower pH bound for S. marcescens growth; not included in the paper's free-parameter list but is an adjustable threshold.
assumptions (5)
  • standard math Cell state transitions and proliferation follow sharp Heaviside thresholds (Eqs. B2-B3).
    Mathematical idealization in the model; not centrally load-bearing for experimental claims.
  • ad hoc to paper There exist two internal cell states, quiescent S0 and proliferating S1, with h* < h** so hysteresis is possible (Eq. B2c and Sec. V).
    This ingredient is needed for the model to produce bands and modularity; no direct molecular evidence is provided.
  • domain assumption Cells are immobilized in the gel and do not move (Sec. I, Introduction).
    Supported by fliC knockout experiment (SI Fig. S10) and high gel viscosity; standard.
  • ad hoc to paper Ammonia released by amino acid catabolism at the top interface acts as a diffusing base that neutralizes fermentation acid, and this is the dominant role for top-band formation (Sec. III, SI Sec. IV).
    Inferred from NH4Cl and buffer perturbation experiments; not directly measured in the columns.
  • domain assumption Model kinetic parameters for S. marcescens can be approximated by E. coli values (SI Tab. S6).
    Acknowledged in parameter table; likely reasonable but unvalidated for this species.

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Cite this review

Pith. "Pith review of Bacterial proliferation pattern formation." pith.science (2026). https://pith.science/paper/LKFTZMJ5

@misc{pith2026250109546,
  author       = {Pith},
  title        = {Pith review of: Bacterial proliferation pattern formation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LKFTZMJ5}},
  note         = {Machine review of arXiv:2501.09546}
}
read the original abstract

Bacteria can form a great variety of spatially heterogeneous cell density patterns, ranging from simple concentric rings to dynamical spiral waves appearing in growing colonies. These pattern formation phenomena are important as they reflect how cellular processes such as metabolism operate in heterogeneous chemical environments. In the laboratory, they can be studied in simplified set-ups, where spatial gradients of oxygen and nutrients are externally imposed, and cells are immobilized in a gel matrix. An intriguing example, observed in such set-ups over 80 years ago, is the sequential formation of narrow bands of high cell density, taking place even for a clonal population. However, key aspects of the dynamics of band formation remained obscure. Using time-lapse imaging of replicate transparent columns in simplified growth media, we first quantify the precision of the positioning and timing of band formation. We also show that the appearance and position of different bands can be modulated independently. This "modularity" is suggested by the observation that different bands differ in their gene expression, and it is reproduced by a theoretical model based on the existence of internal metabolic states and the induction of a pH gradient. Finally, we can also modify the observed pattern formation by introducing genetic modifications that impair selected metabolic pathways. In our opinion, the possibility of precise measurements and controls, together with the simplicity and richness of the "proliferation pattern formation" phenomenon, can make it a model system to study the response of cellular processes to heterogeneous environments.

Figures

Figures reproduced from arXiv: 2501.09546 by the authors.

Figure 1
Figure 1. FIG. 1 [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. a show the spatiotemporal dynamics of band formation in three representative columns from a 12-replicate experi￾ment (additional details in caption, and Extended Methods [43, §IA], and [43, Fig. S1–2]). These graphical representations exploit the fact that the pattern formation is effectively one dimensional: the distance of a generic point from the bottom interface changes along the vertical axis, while time from t… view at source ↗
Figure 3
Figure 3. FIG. 3 [PITH_FULL_IMAGE:figures/full_fig_p004_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_p005_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5 [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6 [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

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Works this paper leans on

95 extracted references · 78 canonical work pages

  1. [1]

    E. G. Jobbágy and R. B. Jackson, The distribution of soil nu- trients with depth: Global patterns and the imprint of plants, Biogeochemistry 53, 51 (2001)

    2001

  2. [2]

    Hinsinger, G

    P. Hinsinger, G. R. Gobran, P. J. Gregory, and W. W. Wenzel, Rhizosphere geometry and heterogeneity arising from root- mediated physical and chemical processes, New Phytol. 168, 293 (2005)

    2005

  3. [3]

    Stocker, Marine microbes see a sea of gradients, Science 338, 628 (2012)

    R. Stocker, Marine microbes see a sea of gradients, Science 338, 628 (2012)

    2012

  4. [4]

    P. G. Falkowski, T. Fenchel, and E. F. Delong, The microbial 11 engines that drive Earth’s biogeochemical cycles, Science 320, 1034 (2008)

    2008

  5. [5]

    Fenchel, H

    T. Fenchel, H. W. Blackburn, and G. M. King, Bacterial Bio- geochemistry: the Ecophysiology of Mineral Cycling (Elsevier Science, 2014)

    2014

  6. [6]

    N. W. Sokol, E. Slessarev, G. L. Marschmann, A. Nicolas, S. J. Blazewicz, E. L. Brodie, M. K. Firestone, M. M. Foley, R. Hestrin, B. A. Hungate, B. J. Koch, B. W. Stone, M. B. Sullivan, O. Zablocki, G. Trubl, K. McFarlane, R. Stuart, E. Nuccio, P. Weber, Y. Jiao, M. Zavarin, J. Kimbrel, K. Morrison, D. Adhikari, A. Bhat- tacharaya, P. Nico, J. Tang, N. Di...

    2022

  7. [7]

    A. P. Petroff, F. Tejera, and A. Libchaber, Subsurface microbial ecosystems: A photon flux and a metabolic cascade, J. Stat. Phys. 167, 763 (2017)

    2017

  8. [8]

    R. A. Quinn, W. Comstock, T. Zhang, J. T. Morton, R. da Silva, A. Tran, A. Aksenov, L.-F. Nothias, D. Wangpraseurt, A. V. Melnik, G. Ackermann, D. Conrad, I. Klapper, R. Knight, and P. C. Dorrestein, Niche partitioning of a pathogenic microbiome driven by chemical gradients, Sci. Adv. 4, eaau1908 (2018)

    2018

Show all 95 references
  1. [9]

    Dal Co, S

    A. Dal Co, S. van Vliet, and M. Ackermann, Emergent microscale gradients give rise to metabolic cross-feeding and antibiotic tolerance in clonal bacterial populations, Philos. Trans. R. Soc. B 374, 20190080 (2019)

    2019

  2. [10]

    K. J. Lee, E. C. Cox, and R. E. Goldstein, Competing patterns of signaling activity in Dictyostelium discoideum, Phys. Rev. Lett. 76, 1174 (1996)

    1996

  3. [11]

    S. Basu, Y. Gerchman, C. H. Collins, F. H. Arnold, and R. Weiss, A synthetic multicellular system for programmed pattern for- mation, Nature 434, 1130 (2005)

    2005

  4. [12]

    Karig, K

    D. Karig, K. M. Martini, T. Lu, N. A. DeLateur, N. Goldenfeld, and R. Weiss, Stochastic Turing patterns in a synthetic bacterial population, Proc. Natl. Acad. Sci. 115, 6572 (2018)

    2018

  5. [13]

    Adler, Chemotaxis in bacteria: Motile Escherichia coli migrate in bands that are influenced by oxygen and organic nutrients., Science 153, 708 (1966)

    J. Adler, Chemotaxis in bacteria: Motile Escherichia coli migrate in bands that are influenced by oxygen and organic nutrients., Science 153, 708 (1966)

    1966

  6. [14]

    J. A. Shapiro, Pattern and control in bacterial colony develop- ment, Sci. Prog. 76, 399 (1992)

    1992

  7. [15]

    Ben-Jacob, O

    E. Ben-Jacob, O. Schochet, A. Tenenbaum, I. Cohen, A. Czirók, and T. Vicsek, Generic modelling of cooperative growth patterns in bacterial colonies, Nature 368, 46 (1994)

    1994

  8. [16]

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

    1991

  9. [17]

    E. O. Budrene and H. C. Berg, Dynamics of formation of sym- metrical patterns by chemotactic bacteria, Nature 376, 49 (1995)

    1995

  10. [18]

    Espada Burriel and R

    S. Espada Burriel and R. Colin, Active density pattern formation in bacterial binary mixtures, PRX Life 2, 023002 (2024)

    2024

  11. [19]

    Xiong, Y

    L. Xiong, Y. Cao, R. Cooper, W.-J. Rappel, J. Hasty, and L. Tsim- ring, Flower-like patterns in multi-species bacterial colonies, eLife 9, e48885 (2020)

    2020

  12. [20]

    S. Liu, Y. Li, Y. Wang, and Y. Wu, Emergence of large-scale mechanical spiral waves in bacterial living matter, Nat. Phys. 20, 1015 (2024)

    2024

  13. [21]

    Fuqua, M

    C. Fuqua, M. R. Parsek, and E. P. Greenberg, Regulation of gene expression by cell-to-cell communication: Acyl-homoserine lactone quorum sensing, Annu. Rev. Genet. 35, 439 (2001)

    2001

  14. [22]

    C. M. Waters and B. L. Bassler, Quorum sensing: Cell-to-cell communication in bacteria, Annu. Rev. Cell Dev. Bi. 21, 319 (2005)

    2005

  15. [23]

    Keller and M

    L. Keller and M. G. Surette, Communication in bacteria: an ecological and evolutionary perspective, Nat. Rev. Microbiol. 4, 249 (2006)

    2006

  16. [24]

    C. Liu, X. Fu, L. Liu, X. Ren, C. K. Chau, S. Li, L. Xiang, H. Zeng, G. Chen, L.-H. Tang, P. Lenz, X. Cui, W. Huang, T. Hwa, and J.-D. Huang, Sequential establishment of stripe patterns in an expanding cell population, Science 334, 238 (2011)

    2011

  17. [25]

    A. I. Curatolo, N. Zhou, Y. Zhao, C. Liu, A. Daerr, J. Tailleur, and J. Huang, Cooperative pattern formation in multi-component bacterial systems through reciprocal motility regulation, Nat. Phys. 16, 1152 (2020)

    2020

  18. [26]

    Duran-Nebreda, J

    S. Duran-Nebreda, J. Pla, B. Vidiella, J. Piñero, N. Conde-Pueyo, and R. Solé, Synthetic lateral inhibition in periodic pattern form- ing microbial colonies, ACS Synth. Biol. 10, 277 (2021)

    2021

  19. [27]

    J. Lu, E. Şimşek, A. Silver, and L. You, Advances and challenges in programming pattern formation using living cells, Curr. Opin. Chem. Biol. 68, 102147 (2022)

    2022

  20. [28]

    Barbier, H

    I. Barbier, H. Kusumawardhani, and Y. Schaerli, Engineering synthetic spatial patterns in microbial populations and commu- nities, Curr. Opin. Microbiol. 67, 102149 (2022)

    2022

  21. [29]

    Doshi, M

    A. Doshi, M. Shaw, R. Tonea, S. Moon, R. Minyety, A. Doshi, A. Laine, J. Guo, and T. Danino, Engineered bacterial swarm patterns as spatial records of environmental inputs, Nat. Chem. Biol. 19, 878 (2023)

    2023

  22. [30]

    Martínez-Calvo, T

    A. Martínez-Calvo, T. Bhattacharjee, R. K. Bay, H. N. Luu, A. M. Hancock, N. S. Wingreen, and S. S. Datta, Morphological insta- bility and roughening of growing 3D bacterial colonies, Proc. Natl. Acad. Sci. 119, e2208019119 (2022)

    2022

  23. [31]

    Dal Co, M

    A. Dal Co, M. Ackermann, and S. van Vliet, Spatial self- organization of metabolism in microbial systems: A matter of enzymes and chemicals, Cell Syst. 14, 98 (2023)

    2023

  24. [32]

    J. W. T. Wimpenny, J. P. Coombs, R. W. Lovitt, and S. G. Whit- taker, A gel-stabilized model ecosystem for investigating micro- bial growth in spatially ordered solute gradients, Microbiology 127, 277 (1981)

    1981

  25. [34]

    Lushi, H

    E. Lushi, H. Wioland, and R. E. Goldstein, Fluid flows created by swimming bacteria drive self-organization in confined sus- pensions, Proc. Natl. Acad. Sci. 111, 9733 (2014)

    2014

  26. [35]

    M. E. Cates and J. Tailleur, Motility-induced phase separation, Annu. Rev. Conden. Ma. P. 6, 219 (2015)

    2015

  27. [36]

    Rauprich, M

    O. Rauprich, M. Matsushita, C. J. Weijer, F. Siegert, S. E. Es- ipov, and J. A. Shapiro, Periodic phenomena inProteus mirabilis swarm colony development, J. Bacteriol. 178, 6525 (1996)

    1996

  28. [37]

    C. J. Ingham and E. Ben-Jacob, Swarming and complex pat- tern formation in Paenibacillus vortex studied by imaging and tracking cells, BMC Microbiol. 8, 36 (2008)

    2008

  29. [38]

    Thomen, J

    P. Thomen, J. D. P. Valentin, A.-F. Bitbol, and N. Henry, Spa- tiotemporal pattern formation in E. coli biofilms explained by a simple physical energy balance, Soft Matter 16, 494 (2020)

    2020

  30. [39]

    spectrum

    J. W. Williams, Bacterial growth “spectrum” analysis. I. Methods and application, Am. J. Med. Technol. 4, 58 (1938)

    1938

  31. [40]

    spectrum

    J. W. Williams, Bacterial growth “spectrum” analysis. II. Their significance in pathology and bacteriology, Am. J. Pathol. 14, 642 (1938)

    1938

  32. [41]

    J. W. T. Wimpenny, Responses of microorganisms to physical and chemical gradients, Phil. Trans. R. Soc. Lond. B 297, 497 (1982)

    1982

  33. [42]

    J. P. Coombs and J. W. T. Wimpenny, Growth ofBacillus cereus in a gel-stabilized nutrient gradient system, Microbiology 128, 3093 (1982)

    1982

  34. [43]

    Supplementary Information, Text

  35. [45]

    N. R. Williamson, P. C. Fineran, F. J. Leeper, and G. P. C. Salmond, 12 The biosynthesis and regulation of bacterial prodiginines, Nat. Rev. Microbiol. 4, 887 (2006)

    2006

  36. [46]

    M. I. Bunting, A description of some color variants produced by Serratia marcescens, strain 274, J. Bacteriol. 40, 57 (1940)

    1940

  37. [48]

    Jaeger and J

    J. Jaeger and J. Reinitz, On the dynamic nature of positional information, BioEssays 28, 1102 (2006)

    2006

  38. [49]

    Meinhardt and A

    H. Meinhardt and A. Gierer, Pattern formation by local self- activation and lateral inhibition, BioEssays 22, 753 (2000)

    2000

  39. [50]

    J. W. T. Wimpenny, S. Jaffe, and J. P. Coombs, Periodic growth phenomena in spatially organized microbial systems, in Lecture Notes in Biomathematics (Springer Berlin Heidelberg, 1984) pp. 388–405

    1984

  40. [51]

    F. C. Hoppensteadt and W. Jäger, Pattern formation by bacteria, in Lecture Notes in Biomathematics (Springer Berlin Heidelberg,

  41. [52]

    Nabika, M

    H. Nabika, M. Itatani, and I. Lagzi, Pattern formation in precipi- tation reactions: The Liesegang phenomenon, Langmuir 36, 481 (2019)

    2019

  42. [53]

    Antal, M

    T. Antal, M. Droz, J. Magnin, Z. Rácz, and M. Zrinyi, Derivation of the Matalon-Packter law for Liesegang patterns, J. Chem. Phys. 109, 9479 (1998)

    1998

  43. [54]

    C. H. Waddington, Organisers & Genes (Cambridge University Press, Cambridge (UK), 1940)

    1940

  44. [55]

    Jiang, Z

    K. Jiang, Z. Yan, M. Di Bernardo, S. R. Sgrizzi, L. Villiger, A. Kayabolen, B. Kim, J. K. Carscadden, M. Hiraizumi, H. Nishi- masu, J. S. Gootenberg, and O. O. Abudayyeh, Rapid in silico directed evolution by a protein language model with evolvepro, Science 10.1126/science.adr...

    2024 doi

  45. [56]

    D. R. Hekstra and S. Leibler, Contingency and statistical laws in replicate microbial closed ecosystems, Cell 149, 1164 (2012)

    2012

  46. [57]

    Frentz, S

    Z. Frentz, S. Kuehn, and S. Leibler, Strongly deterministic popu- lation dynamics in closed microbial communities, Phys. Rev. X 5, 041014 (2015)

    2015

  47. [58]

    J. S. Chuang, Z. Frentz, and S. Leibler, Homeorhesis and eco- logical succession quantified in synthetic microbial ecosystems, Proc. Natl. Acad. Sci. U.S.A. 116, 14852 (2019)

    2019

  48. [59]

    D. J. Esteban, B. Hysa, and C. Bartow-McKenney, Temporal and spatial distribution of the microbial community of Winogradsky columns, PLOS ONE 10, e0134588 (2015)

    2015

  49. [60]

    M. T. Madigan, K. S. Bender, D. H. Buckley, W. M. Sattley, D. A. Stahl, and T. D. Brock, Brock Biology of Microorganisms , 16th ed. (Pearson Education, Harlow (UK), 2022)

    2022

  50. [61]

    Grilli, Macroecological laws describe variation and diversity in microbial communities, Nat

    J. Grilli, Macroecological laws describe variation and diversity in microbial communities, Nat. Commun. 11, 4743 (2020)

    2020

  51. [64]

    Although inaccurate, in our simulations (e.g. Figs. 5b and 5c), cells density is initially non-zero also in the layer of added base (𝑥T <𝑥 <𝑥T+𝛥𝑥). Since cells do not grow in that region—due to either lack of glucose, or high concentration of protons—this inaccuracy is minor a...

  52. [65]

    Y. Ma, S. Gowda, R. Anantharaman, C. Laughman, V. Shah, and C. Rackauckas, Modelingtoolkit: A composable graph transformation system for equation-based modeling (2021), arXiv:2103.05244

    2021 arXiv

  53. [66]

    c1, c2, c3

    C. Rackauckas and Q. Nie, DifferentialEquations.jl—a perfor- mant and feature-rich ecosystem for solving differential equa- tions in Julia, J. Open Res. Softw. 5, 15 (2017). Supplementary Information Bacterial proliferation pattern formation John S. Chuang, 1,∗ Riccardo Rao,1,...

    2017

  54. [67]

    Platt, C

    R. Platt, C. Drescher, S.-K. Park, and G. J. Phillips, Genetic system for reversible integration of DNA constructs and lacZ gene fusions into the Escherichia coli chromosome, Plasmid 43, 12 (2000)

    2000

  55. [68]

    Ferrières, G

    L. Ferrières, G. Hèmery, T. Nham, A.-M. Gueèrout, D. Mazel, C. Beloin, and J.-M. Ghigo, Silent mischief: Bacteriophage mu insertions contaminate products of Escherichia coli random mutagenesis performed using suicidal transposon delivery plasmids mobilized by broad-host-range ...

    2010

  56. [69]

    J. A. Bernstein, A. B. Khodursky, P.-H. Lin, S. Lin-Chao, and S. N. Cohen, Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays, Proc. Natl. Acad. Sci. U.S.A. 99, 9697 (2002)

    2002

  57. [70]

    M. B. Stead, A. Agrawal, K. E. Bowden, R. Nasir, B. K. Mohanty, R. B. Meagher, and S. R. Kushner, RNAsnap™: a rapid, quantitative and inexpensive, method for isolating total RNA from bacteria, Nucleic Acids Res. 40, e156 (2012)

    2012

  58. [71]

    P. S. Aranda, D. M. LaJoie, and C. L. Jorcyk, Bleach gel: A simple agarose gel for analyzing RNA quality, Electrophoresis 33, 366 (2012)

    2012

  59. [72]

    Andrews, FastQC: a quality control tool for high throughput sequence data, https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010)

    S. Andrews, FastQC: a quality control tool for high throughput sequence data, https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010)

    2010

  60. [73]

    S. Chen, T. Huang, Y. Zhou, Y. Han, M. Xu, and J. Gu, AfterQC: automatic filtering, trimming, error removing and quality control for fastq data, BMC Bioinf. 18, 10.1186/s12859-017-1469-3 (2017)

    2017 doi

  61. [74]

    A. M. Bolger, M. Lohse, and B. Usadel, Trimmomatic: a flexible trimmer for Illumina sequence data, Bioinformatics 30, 2114 (2014)

    2014

  62. [75]

    Langmead, C

    B. Langmead, C. Trapnell, M. Pop, and S. L. Salzberg, Ultrafast and memory-efficient alignment of short DNA sequences to the human genome, Genome Biol. 10, R25 (2009)

    2009

  63. [76]

    H. E. Daligault, K. W. Davenport, T. D. Minogue, S. M. Broomall, D. C. Bruce, P. S. Chain, S. R. Coyne, H. S. Gibbons, J. Jaissle, C. N. Rosenzweig, M. Scholz, H. Teshima, and S. L. Johnson, Genome assembly of Serratia marcescens type strain ATCC 13880, Genome Announc. 2, 10.1...

    2014 doi

  64. [77]

    ATCC 13880 genome, https://sra-download.ncbi.nlm.nih.gov/traces/wgs03/wgs_aux/JO/VM/JOVM01/JOVM01.1.fsa_nt.gz ()

  65. [78]

    H. Li, B. Handsaker, A. Wysoker, T. Fennell, J. Ruan, N. Homer, G. Marth, G. Abecasis, and R. Durbin, The sequence alignment/map format and SAMtools, Bioinformatics 25, 2078 (2009). 15

    2009

  66. [79]

    Y. Liao, G. K. Smyth, and W. Shi, featureCounts: an efficient general purpose program for assigning sequence reads to genomic features, Bioinformatics 30, 923 (2014)

    2014

  67. [80]

    ATCC 13880 GSMA_DRAFT GFF3 annotation file, http://ftp.ensemblgenomes.org/pub/bacteria/release-42/gff3/bacteria_69_collection/ serratia_marcescens_subsp_marcescens_atcc_13880/Serratia_marcescens_subsp_marcescens_atcc_13880.GSMA_DRAFTv1.37.gff3.gz

  68. [81]

    K. L. Howe, B. Contreras-Moreira, N. De Silva, G. Maslen, W. Akanni, J. Allen, J. Alvarez-Jarreta, M. Barba, D. M. Bolser, L. Cambell, M. Carbajo, M. Chakiachvili, M. Christensen, C. Cummins, A. Cuzick, P. Davis, S. Fexova, A. Gall, N. George, L. Gil, P. Gupta, K. E. Hammond-K...

    2020

  69. [82]

    Lawrence, R

    M. Lawrence, R. Gentleman, and V. Carey, rtracklayer: an R package for interfacing with genome browsers, Bioinformatics 25, 1841 (2009)

    2009

  70. [83]

    M. I. Love, W. Huber, and S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol. 15, 550 (2014)

    2014

  71. [84]

    J. E. Lazarus, A. R. Warr, C. J. Kuehl, R. T. Giorgio, B. M. Davis, and M. K. Waldor, A new suite of allelic-exchange vectors for the scarless modification of proteobacterial genomes, Appl. Environ. Microb. 85, e00990-19 (2019)

    2019

  72. [85]

    Addgene plasmid # 127451, http://n2t.net/addgene:127451

  73. [86]

    ATCC 13880 genome used for primer design, https://sep2019-bacteria.ensembl.org/Serratia_marcescens_subsp_marcescens_atcc_13880/ Info/Index ()

  74. [87]

    H. Ge, K. Xu, and Z. Ghahramani, Turing: a language for flexible probabilistic inference, inInternational Conference on Artificial Intelligence and Statistics, AISTATS 2018, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, Spain (2018) pp. 1682–1690

    2018

  75. [88]

    P. K. Mogensen and A. N. Riseth, Optim: A mathematical optimization package for Julia, J. Open Source Softw. 3, 615 (2018)

    2018

  76. [89]

    Reitzer, Catabolism of amino acids and related compounds, EcoSal Plus 1, ecosalplus.3.4.7 (2005)

    L. Reitzer, Catabolism of amino acids and related compounds, EcoSal Plus 1, ecosalplus.3.4.7 (2005)

    2005

  77. [90]

    Diffusion coefficients, https://www.aqion.de/site/diffusion-coefficients

  78. [91]

    S. F. Karel and C. R. Robertson, Cell mass synthesis and degradation by immobilized Escherichia coli, Biotechnol. Bioeng. 34, 337 (1989)

    1989

  79. [92]

    Kovárová, A

    K. Kovárová, A. J. B. Zehnder, and T. Egli, Temperature-dependent growth kinetics ofEscherichia coli ML 30 in glucose-limited continuous culture, J. Bacteriol. 178, 4530 (1996)

    1996

  80. [93]

    B. H. Kim and G. M. Gadd, Bacterial Physiology and Metabolism (Cambridge University Press, 2008)

    2008

  81. [94]

    Bubelová, F

    Z. Bubelová, F. Buňka, M. Taťáková, K. štajnochová, K. Purevdorj, and L. Buňková, Effects of temperature, pH and NaCl content on in vitro putrescine and cadaverine production through the growth of Serratia marcescens CCM 303, J. Environ. Sci. Health B 50, 797 (2015)

    2015

  82. [95]

    Loferer-Krössbacher, J

    M. Loferer-Krössbacher, J. Klima, and R. Psenner, Determination of bacterial cell dry mass by transmission electron microscopy and densitometric image analysis, Appl. Environ. Microb. 64, 688 (1998)

    1998

  83. [96]

    BioNumbers, https://bionumbers.hms.harvard.edu/search.aspx

  84. [97]

    A. M. Turing, The chemical basis of morphogenesis, Philos. Trans. R. Soc. Lond. B. Biol. Sci. 237, 37 (1952)

    1952

  85. [98]

    asnB, https://www.ecocyc.org/gene?orgid=ECOLI&id=EG10092

  86. [99]

    sucD, https://www.ecocyc.org/gene?orgid=ECOLI&id=EG10982

  87. [100]

    wt” refers to the wild type S. marcescens (ATCC 13880) whereas “ΔfliC 1

    nuoA, https://www.ecocyc.org/gene?orgid=ECOLI&id=EG12082. VI. SUPPLEMENTARY MOVIES AND FIGURES MOV. S1: Time-lapse movie of S. marcescens ATCC 13880 growing in 12-replicate Phytagel columns. Medium composition: synthetic medium 1 (Tab. S2) + 6.8 mM glutamate. The snapshots in ...

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