Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

Spatial organization of biomass controls intrinsic permeability of porous systems

T0 review · 3 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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

Pith's one-line read Spatial biomass layout, not total amount, controls how much biofilms clog porous media.

desk verdict Worth a referee: the motile/non-motile permeability contrast is a real result, but 'same biomass' is uncalibrated GFP and the model's quantitative match is a fit wearing a prediction's clothes. read the letter →

arxiv 2510.27262 v2 pith:H6QQNK3D submitted 2025-10-31 physics.bio-ph cond-mat.mtrl-sciphysics.app-phphysics.flu-dyn

Wenqiao Jiao , David Scheidweiler , Nolwenn Delouche , Alberto Guadagnini , Pietro de Anna This is my paper · ORCID
classification physics.bio-phcond-mat.mtrl-sciphysics.app-phphysics.flu-dyn
keywords biofilmporousmediapermeabilitybiocloggingbacterialmotilitymicrofluidicspore-scalemodelPseudomonasputida
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 claims that the spatial organization of biomass, not its total quantity, is the primary control on permeability in biofilm-colonized porous media. The evidence comes from microfluidic experiments comparing motile wild-type bacteria with non-motile mutants: both strains reach nearly identical total biomass, yet the motile strain reduces permeability by 78±7% while the non-motile strain reduces it by 94±4%. The difference arises because motile bacteria stop accumulating downstream where nutrients are scarce, whereas non-motile cells continue growing and progressively clog the entire pore space. The authors build a pore-scale mechanistic model that predicts measured permeability dynamics using only one fitted parameter—biofilm permeability—by treating each pore as a central open channel surrounded by a biomass annulus. If correct, this reframes how bio-clogging should be predicted: measurements of total biomass alone are insufficient; where that biomass sits in the pore network matters more.

What carries the argument

The central object is a two-pathway pore-scale flow model: each pore is idealized as a central biofilm-free pipe of diameter d1i with Hagen-Poiseuille permeability d1i²/32, surrounded by an annular biofilm layer of thickness d2i/2 with an effective biofilm permeability kbf. The equivalent pore permeability is the thickness-weighted average of these two parallel flow paths. Biomass thickness in each pore is inferred from fluorescence intensity converted to a biomass density ρ_pi, which shrinks the effective open-pore diameter. The macroscopic permeability is then computed as the harmonic mean over ~896 serial pore elements, with kbf as the only fitted parameter. This machinery links pore-scal

What would settle it

Measure the actual biofilm thickness and volume in the same microfluidic pores using confocal microscopy or optical coherence tomography and compare these to the fluorescence-derived thicknesses used in the model; if fluorescence-to-thickness conversion is off by more than the stated uncertainties, the quantitative permeability predictions would fail for both strains.

Watch

Extended reading notes

Core claim

The central claim is that the spatial organization of biomass, not its total amount, is the primary factor controlling intrinsic permeability of porous systems. This is supported by experiments where wild-type (motile) and ΔfliC (non-motile) Pseudomonas putida reach nearly identical total biomass carrying capacity (K≈4.6–4.7×10^4 in the logistic growth model), yet cause drastically different permeability reductions: 78±7% for motile versus 94±4% for non-motile. Motility limits downstream biomass accumulation—motile cells escape resource-depleted zones and are advected away—whereas non-motile cells continue slow growth even under resource limitation, filling and clogging the entire system. A

Load-bearing premise

The model assumes that GFP fluorescence intensity is a quantitative, linearly comparable measure of flow-occluding biomass volume, converting fluorescence into biofilm thickness without independent calibration against actual biomass density or thickness.

Editorial extensions

If this is right

  • Predicting bioclogging in soils, filters, and aquifers requires spatial biomass distribution data, not just total biomass or bulk porosity measurements.
  • Bacterial motility is a key control: motile strains that escape nutrient-depleted regions leave downstream pores open, preserving higher overall permeability.
  • The two-regime growth model predicts that space-limited growth clogs pores far more effectively per unit biomass than nutrient-limited growth, and that heterogeneous pore networks clog faster than homogeneous ones at the same biomass.
  • The single fitted biofilm permeability value (kbf=2.5 darcy) is consistent with independent literature estimates, suggesting the model captures a physically meaningful biofilm property rather than an arbitrary fit.
  • Under constant-pressure conditions, permeability can continue dropping long after total biomass plateaus, as seen for the non-motile strain, implying post-plateau biomass redistribution is hydraulically significant.

Reading between the lines

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

  • A testable extension: the same experimental setup applied to a fixed flow-rate boundary (instead of constant pressure) should show different permeability dynamics because local shear would promote detachment; the model could be adapted with a detachment term.
  • The framework suggests that engineered bio-barriers could be made more effective by suppressing bacterial motility, while antifouling strategies in filtration should target spatial coverage rather than total biofilm mass.
  • If fluorescence does not linearly report occluding biomass volume, the quantitative comparison between WT and ΔfliC may be biased; independent thickness measurements would strengthen or correct the central claim.
  • The contrast between nutrient-limited and space-limited regimes hints that the pore-size distribution of a medium could be used to predict whether biomass will clog it uniformly or leave flow pathways open.
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 combines microfluidic porous-media experiments, constant-pressure flow control, and time-lapse fluorescence microscopy to compare biofilm formation by motile wild-type (WT) and non-motile (ΔfliC) Pseudomonas putida. It reports that the two strains reach nearly identical integrated GFP biomass while reducing permeability by different amounts (78±7% for WT vs 94±4% for ΔfliC), and interprets this as evidence that spatial organization of biomass, not total biomass, controls permeability. A pore-scale model is developed in which fluorescence intensity is converted into an effective biofilm thickness within each pore, and the permeability of the medium is computed by treating each pore as two parallel flow pathways. The model, with one fitted parameter k_bf, reproduces the measured permeability curves. The paper also presents idealized nutrient-limited vs space-limited growth scenarios to argue that growth regime interacts with pore-size heterogeneity to determine permeability decline.

Significance. If the central claim is correct, the paper provides a valuable experimental demonstration that biomass spatial distribution can matter more than total biomass for bioclogging, with direct implications for filtration, bioremediation, and subsurface engineering. The experimental setup is a strength: triplicate experiments, direct flow-rate measurements under controlled pressure, spatial maps of biomass, and a pore-scale modeling framework. The authors also state that data and code are available. However, the quantitative conclusions rest on an uncalibrated GFP-to-biomass thickness conversion and on a model whose single free parameter is fitted to the same data it is compared with. These issues must be addressed before the central claim is fully supported, but they are not disqualifying if calibration or independent validation can be supplied.

major comments (3)
  1. [§II.A and Methods F, Eq. (8)] The conversion from fluorescence intensity to biofilm thickness is uncalibrated and dimensionally suspect. The text defines ρ_pi^i = ΣI_mic^i/(I_max A_mic^i) and then sets d2i(t) = ρ_pi^i(t) D_i^MIC / I_max, which appears to use I_max twice and yields an inconsistent dimension. More importantly, no independent calibration links GFP pixel intensity to biofilm volume, thickness, dry mass, or cell number, and no check establishes that WT and ΔfliC fluoresce identically per unit occluding biomass. Since d2i is the key input to Eq. (2), the model's quantitative agreement in Fig. 5 depends on this unverified proportionality. The authors should either calibrate the fluorescence-to-thickness relationship (e.g., confocal thickness measurements, cell counts, or EPS quantification) or clearly reframe the model as a qualitative demonstration. This issue also affects the global 'same total biomass' c
  2. [§II.A, Fig. 5d,e; Eq. (2)] The model's agreement with experiments is not a prediction in the current form, because k_bf is estimated as the best fit to the same k_exp curves shown in Fig. 5d,e. The statement that the model 'fully consistent' with measurements is therefore circular at the parameter level. The authors should obtain k_bf independently (e.g., from the cited literature range, from a separate set of experiments, or via cross-validation) and then show the resulting permeability curves. This would not change the core empirical observation, but it would strengthen the claim that the pore-scale biomass distribution quantitatively explains the permeability dynamics.
  3. [§I.C and §I.D] The central empirical claim—'nearly identical total biomass' causing different permeability reductions—relies entirely on GFP fluorescence as a proxy for biomass amount. If the ΔfliC mutant produces more extracellular polymeric substance per cell, has different GFP expression in stationary phase, or packs more densely in the pore space, then the two strains may not actually have the same occluding biomass. The paper provides no independent biomass measurement (e.g., cell counts, dry weight, confocal volume, or staining of EPS). Without such a control, the possibility remains that the permeability difference is due to a difference in total occluding volume rather than spatial organization. This is a load-bearing assumption for the headline conclusion and should be explicitly tested or discussed as a limitation.
minor comments (5)
  1. [§II.A, text around Eq. (1)] The formula for d2i(t) appears to have a typo: d2i(t) = ρ_pi^i(t) D_i^MIC / I_max is dimensionally inconsistent because ρ_pi^i already includes 1/I_max. This should be corrected.
  2. [§Methods B] The mutant is referred to as 'ΔfilC' in one place and 'ΔfliC' elsewhere; the spelling should be consistent (the gene is fliC).
  3. [§Methods F] The Logistic Growth (LG) model is cited to reference [55], which is an ISME paper on multispecies biofilms and not a standard source for the logistic equation. A methods or modeling reference would be more appropriate.
  4. [Author Contributions] The contributions list 'N.W. developed the feedback loop', but the author list contains Nolwenn Delouche (N.D.), not N.W. This appears to be a typo.
  5. [§II.B, Fig. 6] The homogeneous-structure control in Fig. 6d is described in the text but not clearly distinguished in the figure legend; adding an explicit label or color key would improve readability.

Circularity Check

1 steps flagged · score 4.0 of 10

Model's quantitative 'prediction' of permeability is partly a fit, but the central two-strain comparison is independent; no full circularity.

  1. fitted input called prediction [Abstract; Section II.A, Eqs. (1)-(2); Fig. 5d,e]
    "Note that biofilm permeability, kbf, is the only parameter that needs to be estimated in our model, all other quantities being directly measured. As illustrated in Fig. 5d, e, the resulting theoretical permeability model kt is fully consistent with the measured permeability kexp ... The biofilm permeability is estimated as the best parameter value to fit the data in Fig. 5d, e and results to be kbf = 2.5 darcy in both scenarios."

    The abstract claims the model 'accurately predicts permeability dynamics from the pore-scale biomass distribution,' but the model's only free parameter, kbf, is fitted to the same experimental kexp curves that kt is then compared with in Fig. 5d,e. The agreement is therefore partly enforced by the fit rather than being an independent prediction. However, the shape and strain difference of kt still come from directly measured biomass maps, and the main two-strain result—similar GFP biomass but different measured permeability—does not rely on this fit, so the circularity is partial rather than total.

full rationale

The paper's central empirical claim—WT and ΔfliC reach nearly identical total GFP biomass yet reduce directly measured permeability by different amounts—does not reduce to the model. Permeability is measured from flow rate under constant pressure, while biomass is quantified separately from fluorescence images. The pore-scale permeability model (Eqs. 1-2) is a mechanistic construction (pipes in series, each pore with a central channel and an annular biofilm layer), and the only fitted parameter kbf is explicitly fit to the experimental kexp curves in Fig. 5d,e. Thus, calling kt a parameter-free 'prediction' is overstated; the match is partly a post-fit description. That said, the model's qualitative predictions in Sec. II.B are based on stated logistic-growth assumptions and do not import the target result. Citations to the authors' prior work [20,44,46,55] are load-bearing for the device and pore-network representation, but they are transparent mechanistic constructs revalidated with fresh data here, not unverified uniqueness claims. The uncalibrated GFP-to-biomass conversion is a genuine validity risk for the 'same total biomass' comparison, but it is a measurement assumption, not a derivation step that reduces to its inputs, so it does not further raise the circularity score.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

No new physical entities are introduced. The model's quantitative content rests on a measured biomass field, geometric pore measurements, and one fitted biofilm permeability; the main load-bearing assumptions are the series-pore representation, statistical independence of pore size and biomass density, linear fluorescence-to-thickness conversion, and the single-gene attribution of the behavioral difference to motility.

free parameters (3)
  • k_bf (biofilm intrinsic permeability) = 2.5 darcy
    Only free parameter in the permeability model (Eqs. 1–2); estimated by fitting the model to the same experimental k_exp curves in Fig. 5d,e it is then said to predict.
  • nutrient-limited carrying capacity K = not specified (arbitrary)
    In the nutrient-limited simulation (Fig. 6), a single homogeneous K is imposed for all pores; absolute value sets the biomass plateau and affects the predicted permeability-decline timing.
  • space-limited carrying capacity coefficient (K∝A) = not specified (arbitrary)
    In the space-limited simulation (Fig. 6), per-pore carrying capacity is set proportional to pore area; the proportionality constant is not measured.
assumptions (6)
  • domain assumption The whole porous domain can be treated as m=896 independent pores in series, with overall permeability as harmonic mean.
    Used in Eq. 2 and §II.A; inherited from the authors' earlier work [44] and not re-derived here.
  • domain assumption Pore size and biomass density are statistically independent, so they can be sampled and paired randomly.
    Stated in §II.A; the paper says support is in Supplementary Fig. S9, which is not available in the main text.
  • domain assumption GFP fluorescence intensity is proportional to biomass volume and converts linearly to biofilm thickness d2.
    Eq. 8 and §II.A derive d2 from ρ_pi; no calibration against independent biofilm thickness or porosity is reported.
  • domain assumption Biofilm grows as a homogeneous wall coating (no streamers) and is itself a porous medium with a single permeability k_bf.
    §II.A; streamer absence is asserted but not shown; biofilm is treated as a uniform annulus with constant k_bf.
  • domain assumption WT and ΔfliC differ only by the presence/absence of flagella (motility).
    The causal claim that motility causes the different spatial pattern assumes the mutant differs only in swimming; flagella also mediate surface sensing and biofilm formation.
  • standard math Hagen-Poiseuille and Darcy laws apply to pore-scale and biofilm-scale flow.
    Used throughout Methods G and Eqs. 1–2; standard for the low-Reynolds regime.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Spatial organization of biomass controls intrinsic permeability of porous systems." pith.science (2026). https://pith.science/paper/H6QQNK3D

@misc{pith2026251027262,
  author       = {Pith},
  title        = {Pith review of: Spatial organization of biomass controls intrinsic permeability of porous systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H6QQNK3D}},
  note         = {Machine review of arXiv:2510.27262}
}
read the original abstract

Biofilms in porous media critically influence hydraulic properties in environmental and engineered systems. However, a mechanistic understanding of how microbial life controls permeability remains elusive. By combining microfluidics, controlled pressure gradient and time-lapse microscopy, we quantify how motile and non-motile bacteria colonize a porous landscape and alter its resistance to flow. We find that while both strains achieve nearly identical total biomass, they cause drastically different permeability reductions - 78% for motile cells versus 94% for non-motile cells. This divergence stems from motility, which limits biomass spatial accumulation, whereas non-motile cells clog the entire system. We develop a mechanistic model that accurately predicts permeability dynamics from the pore-scale biomass distribution. We conclude that the spatial organization of biomass, not its total amount, is the primary factor controlling permeability.

Figures

Figures reproduced from arXiv: 2510.27262 by the authors.

Figure 1
Figure 1. FIG. 1. Microfluidic system designed for monitoring intrinsic permeability and controlled flow dynamics. (a) Schematic of [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Dynamics of the permeability (Methods.G) and biomass for (a) ∆fliC and (b) WT strains. The experimental biomass [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Dynamics of [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. Dynamics of [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Theoretical model and predictions. (a) Schematic view of the permeability model, conceptualizing the system as a [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: FIG. 6. Prediction of permeability decrease for nutrient-limited and space-limited growth conditions in heterogeneous pores. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Pore-shape and its spatial organization control intrinsic permeability of porous media

    cond-mat.soft 2026-06 unverdicted novelty 6.0 of 10

    Dead-end pore density along percolating paths enhances permeability via localized hydrodynamic interactions at junctions, while depth and orientation have little effect.

Reference graph

Works this paper leans on

70 extracted references · cited by 1 Pith paper

  1. [1]

    Mi- crobial biofilms

    J William Costerton, Zbigniew Lewandowski, Douglas E Caldwell, Darren R Korber, Hilary M Lappin-Scott, et al. Mi- crobial biofilms. 1995

    1995

  2. [2]

    Confined flow: consequences and implications for bacteria and biofilms

    Jacinta C Conrad and Ryan Poling-Skutvik. Confined flow: consequences and implications for bacteria and biofilms. Annual review of chemical and biomolecular engineering, 9(1):175–200, 2018

    2018

  3. [3]

    Bacterial exo-polysaccharides in biofilms: role in antimicrobial resistance and treatments

    Shivani Singh, Saptashwa Datta, Kannan Badri Narayanan, and K Narayanan Rajnish. Bacterial exo-polysaccharides in biofilms: role in antimicrobial resistance and treatments. Journal of Genetic Engineering and Biotechnology, 19:1–19, 2021

    2021

  4. [4]

    Subsurface biofilm barriers for the containment and remediation of contaminated groundwater

    Alfred B Cunningham, Robert R Sharp, Randy Hiebert, and Garth James. Subsurface biofilm barriers for the containment and remediation of contaminated groundwater. Bioremediation Journal, 7(3-4):151–164, 2003

    2003

  5. [5]

    Benefits of bacterial biomineralization

    VR Phoenix and KO Konhauser. Benefits of bacterial biomineralization. Geobiology, 6(3), 2008

    2008

  6. [6]

    Biomineralization of gold: biofilms on bacterioform gold

    Frank Reith, Stephen L Rogers, DC McPhail, and Daryl Webb. Biomineralization of gold: biofilms on bacterioform gold. science, 313(5784):233–236, 2006

    2006

  7. [7]

    Biofilms as biobarriers

    John Lennox and Jeffrey Ashe. Biofilms as biobarriers. The American Biology Teacher, 71(1):20–26, 2009

    2009

  8. [8]

    Bacterial biofilm and extracellular polymeric substances in the moving bed biofilm reactor for wastewater treatment: A review

    Kumari Uma Mahto and Surajit Das. Bacterial biofilm and extracellular polymeric substances in the moving bed biofilm reactor for wastewater treatment: A review. Bioresource Technology, 345:126476, 2022

    2022

Show all 70 references
  1. [9]

    Investigating the potential of microbial enhanced oil recovery in carbonate reservoirs using bacillus persicus

    Hengameh Haddad, Ali Reza Khaz’ali, Arjomand Mehrabani-Zeinabad, and Mohammadhadi Jazini. Investigating the potential of microbial enhanced oil recovery in carbonate reservoirs using bacillus persicus. Fuel, 334:126757, 2023

    2023

  2. [10]

    Biofilm streamers cause catastrophic disruption of flow with consequences for environmental and medical systems

    Knut Drescher, Yi Shen, Bonnie L Bassler, and Howard A Stone. Biofilm streamers cause catastrophic disruption of flow with consequences for environmental and medical systems. Proceedings of the National Academy of Sciences, 110(11):4345– 4350, 2013

    2013

  3. [11]

    Hydraulic aspects of riverbank filtration—field studies

    J¨ urgen Schubert. Hydraulic aspects of riverbank filtration—field studies. Journal of Hydrology, 266(3-4):145–161, 2002

    2002

  4. [12]

    Environmental 19 impact and mechanisms of the biological clogging of saturated soils and aquifer materials

    Philippe Baveye, Philippe Vandevivere, Blythe L Hoyle, Paul C DeLeo, and Diego Sanchez de Lozada. Environmental 19 impact and mechanisms of the biological clogging of saturated soils and aquifer materials. Critical reviews in environmental science and technology, 28(2):123–191, 1998

    1998

  5. [13]

    Microbial competi- tion in porous environments can select against rapid biofilm growth

    Katharine Z Coyte, Herv´ e Tabuteau, Eamonn A Gaffney, Kevin R Foster, and William M Durham. Microbial competi- tion in porous environments can select against rapid biofilm growth. Proceedings of the National Academy of Sciences, 114(2):E161–E170, 2017

    2017

  6. [14]

    Biofilms: an emergent form of bacterial life

    Hans-Curt Flemming, Jost Wingender, Ulrich Szewzyk, Peter Steinberg, Scott A Rice, and Staffan Kjelleberg. Biofilms: an emergent form of bacterial life. Nature Reviews Microbiology, 14(9):563–575, 2016

    2016

  7. [15]

    Microbial biofilms: from ecology to molecular genetics

    Mary Ellen Davey and George A O’toole. Microbial biofilms: from ecology to molecular genetics. Microbiology and molecular biology reviews, 64(4):847–867, 2000

    2000

  8. [16]

    The biofilm matrix

    Hans-Curt Flemming and Jost Wingender. The biofilm matrix. Nature reviews microbiology, 8(9):623–633, 2010

    2010

  9. [17]

    Biofilms as complex fluids

    James N Wilking, Thomas E Angelini, Agnese Seminara, Michael P Brenner, and David A Weitz. Biofilms as complex fluids. MRS bulletin, 36(5):385–391, 2011

    2011

  10. [18]

    Sociomicrobiology: the connections between quorum sensing and biofilms

    Matthew R Parsek and EP Greenberg. Sociomicrobiology: the connections between quorum sensing and biofilms. Trends in microbiology, 13(1):27–33, 2005

    2005

  11. [19]

    Targeting the holy triangle of quorum sensing, biofilm formation, and antibiotic resistance in pathogenic bacteria

    Ronit Vogt Sionov and Doron Steinberg. Targeting the holy triangle of quorum sensing, biofilm formation, and antibiotic resistance in pathogenic bacteria. Microorganisms, 10(6):1239, 2022

    2022

  12. [20]

    Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media

    David Scheidweiler, Ankur Deep Bordoloi, Wenqiao Jiao, Vladimir Sentchilo, Monica Bollani, Audam Chhun, Philipp Engel, and Pietro de Anna. Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media. Nature Communications, 15(1...

    2024

  13. [21]

    Morphology and size of bacterial colonies control anoxic microenvironment formation in porous media

    Giulia Ceriotti, Sergey M Borisov, Jasmine S Berg, and Pietro De Anna. Morphology and size of bacterial colonies control anoxic microenvironment formation in porous media. Environmental Science & Technology, 56(23):17471–17480, 2022

    2022

  14. [22]

    Dynamic changes in biofilm structures under dynamic flow conditions

    Shuai Wang, Huiyan Zhu, Gexi Zheng, Feng Dong, and Chongxuan Liu. Dynamic changes in biofilm structures under dynamic flow conditions. Applied and Environmental Microbiology, 88(22):e01072–22, 2022

    2022

  15. [23]

    Emergence of three-dimensional order and structure in growing biofilms

    Raimo Hartmann, Praveen K Singh, Philip Pearce, Rachel Mok, Boya Song, Francisco D ´ ıaz-Pascual, J¨ orn Dunkel, and Knut Drescher. Emergence of three-dimensional order and structure in growing biofilms. Nature physics, 15(3):251–256, 2019

    2019

  16. [24]

    The ecology and biogeochemistry of stream biofilms

    Tom J Battin, Katharina Besemer, Mia M Bengtsson, Anna M Romani, and Aaron I Packmann. The ecology and biogeochemistry of stream biofilms. Nature Reviews Microbiology, 14(4):251–263, 2016

    2016

  17. [25]

    Oscillation characteristics of biofilm streamers in turbulent flowing water as related to drag and pressure drop.Biotechnology and bioengineering, 57(5):536–544, 1998

    Paul Stoodley, Zbigniew Lewandowski, John D Boyle, and Hilary M Lappin-Scott. Oscillation characteristics of biofilm streamers in turbulent flowing water as related to drag and pressure drop.Biotechnology and bioengineering, 57(5):536–544, 1998

    1998

  18. [26]

    Cooperation and conflict in microbial biofilms

    Joao B Xavier and Kevin R Foster. Cooperation and conflict in microbial biofilms. Proceedings of the National Academy of Sciences, 104(3):876–881, 2007

    2007

  19. [27]

    Microbial adhesion in flow displacement systems

    Henk J Busscher and Henny C Van Der Mei. Microbial adhesion in flow displacement systems. Clinical microbiology reviews, 19(1):127–141, 2006

    2006

  20. [28]

    Involvement of bacterial migration in the development of complex multicellular structures in pseudomonas aeruginosa biofilms

    Mikkel Klausen, Anders Aaes-Jørgensen, Søren Molin, and Tim Tolker-Nielsen. Involvement of bacterial migration in the development of complex multicellular structures in pseudomonas aeruginosa biofilms. Molecular microbiology, 50(1):61–68, 2003

    2003

  21. [29]

    Mechanosensing of shear by pseudomonas aeruginosa leads to increased levels of the cyclic-di-gmp signal initiating biofilm development

    Christopher A Rodesney, Brian Roman, Numa Dhamani, Benjamin J Cooley, Parag Katira, Ahmed Touhami, and Ver- nita D Gordon. Mechanosensing of shear by pseudomonas aeruginosa leads to increased levels of the cyclic-di-gmp signal initiating biofilm development. Proceedings of the...

    2017

  22. [30]

    Influence of microbial growth on hydraulic properties of pore networks

    Martin Thullner, Josef Zeyer, and Wolfgang Kinzelbach. Influence of microbial growth on hydraulic properties of pore networks. Transport in porous media, 49:99–122, 2002

    2002

  23. [31]

    Influence of biofilm accumulation on porous media hydrodynamics

    Alfred B Cunningham, William G Characklis, Feisal Abedeen, and David Crawford. Influence of biofilm accumulation on porous media hydrodynamics. Environmental science & technology, 25(7):1305–1311, 1991

    1991

  24. [32]

    Modeling the impact of evolving biofilms on flow in porous media inside a microfluidic channel

    Shahab Karimifard, Xu Li, Christian Elowsky, and Yusong Li. Modeling the impact of evolving biofilms on flow in porous media inside a microfluidic channel. Water Research, 188:116536, 2021

    2021

  25. [33]

    Pore-scale hydrodynamics influence the spatial evolution of bacterial biofilms in a microfluidic porous network

    Jayde A Aufrecht, Jason D Fowlkes, Amber N Bible, Jennifer Morrell-Falvey, Mitchel J Doktycz, and Scott T Retterer. Pore-scale hydrodynamics influence the spatial evolution of bacterial biofilms in a microfluidic porous network. PloS one, 14(6):e0218316, 2019

    2019

  26. [34]

    Biomass evolution in porous media and its effects on permeability under starvation conditions

    Dong-Shik Kim and H Scott Fogler. Biomass evolution in porous media and its effects on permeability under starvation conditions. Biotechnology and bioengineering, 69(1):47–56, 2000

    2000

  27. [35]

    Investigation of clogging in porous 20 media induced by microorganisms using a microfluidic application

    Calvin Lumban Gaol, Leonhard Ganzer, Soujatya Mukherjee, and Hakan Alkan. Investigation of clogging in porous 20 media induced by microorganisms using a microfluidic application. Environmental Science: Water Research & Technology, 7(2):441–454, 2021

    2021

  28. [36]

    Substrate and biomass transport in a porous medium

    Stewart W Taylor and Peter R Jaff´ e. Substrate and biomass transport in a porous medium. Water Resources Research, 26(9):2181–2194, 1990

    1990

  29. [37]

    Contributions of biofilm-induced flow hetero- geneities to solute retention and anomalous transport features in porous media

    Lazaro J Perez, Rishi Parashar, Andrew Plymale, and Timothy D Scheibe. Contributions of biofilm-induced flow hetero- geneities to solute retention and anomalous transport features in porous media. Water Research, 209:117896, 2022

    2022

  30. [38]

    Biofilm structure, behavior, and hydrodynamics

    LB Purevdorj-Gage and Paul Stoodley. Biofilm structure, behavior, and hydrodynamics. Microbial biofilms, pages 160–173, 2004

    2004

  31. [39]

    Comparison of bioclogging effects in saturated porous media within one-and two-dimensional flow systems

    Martin Thullner. Comparison of bioclogging effects in saturated porous media within one-and two-dimensional flow systems. Ecological Engineering, 36(2):176–196, 2010

    2010

  32. [40]

    The effect of flow on swimming bacteria controls the initial colonization of curved surfaces

    Eleonora Secchi, Alessandra Vitale, Gast´ on L Mi˜ no, Vasily Kantsler, Leo Eberl, Roberto Rusconi, and Roman Stocker. The effect of flow on swimming bacteria controls the initial colonization of curved surfaces. Nature communications, 11(1):2851, 2020

    2020

  33. [41]

    Time-resolved biofilm deformation measurements using optical coher- ence tomography

    Florian Blauert, Harald Horn, and Michael Wagner. Time-resolved biofilm deformation measurements using optical coher- ence tomography. Biotechnology and bioengineering, 112(9):1893–1905, 2015

    1905

  34. [42]

    Effect of shear stress and growth conditions on detachment and physical properties of biofilms

    Etienne Paul, Juan Carlos Ochoa, Yoan Pechaud, Yu Liu, and Alain Lin´ e. Effect of shear stress and growth conditions on detachment and physical properties of biofilms. Water research, 46(17):5499–5508, 2012

    2012

  35. [43]

    Impact of biofilm-induced heterogeneities on solute transport in porous media

    T Kone, F Golfier, L Orgogozo, C Olt´ ean, E Lef` evre, JC Block, and MA Bu` es. Impact of biofilm-induced heterogeneities on solute transport in porous media. Water Resources Research, 50(11):9103–9119, 2014

    2014

  36. [44]

    Intrinsic permeability of heterogeneous porous media

    Wenqiao Jiao, David Scheidweiler, Nolwenn Delouche, Pietro de Anna, and Alberto Guadagnini. Intrinsic permeability of heterogeneous porous media. Physical Review Fluids, 9(9):094102, 2024

    2024

  37. [45]

    Chemotaxis under flow disorder shapes microbial dispersion in porous media

    Pietro de Anna, Amir A Pahlavan, Yutaka Yawata, Roman Stocker, and Ruben Juanes. Chemotaxis under flow disorder shapes microbial dispersion in porous media. Nature Physics, 17(1):68–73, 2021

    2021

  38. [46]

    Structure induced laminar vortices control anomalous dispersion in porous media

    Ankur Deep Bordoloi, David Scheidweiler, Marco Dentz, Mohammed Bouabdellaoui, Marco Abbarchi, and Pietro de Anna. Structure induced laminar vortices control anomalous dispersion in porous media. Nature communications, 13(1):3820, 2022

    2022

  39. [47]

    Dynamics of fluids in porous media–american elsevier pub

    J Bear. Dynamics of fluids in porous media–american elsevier pub. Comp., inc. New York, 764p, 1972

    1972

  40. [48]

    Material properties of biofilms—a review of methods for understanding permeability and mechanics.Reports on Progress in Physics, 78(3):036601, 2015

    Nicole Billings, Alona Birjiniuk, Tahoura S Samad, Patrick S Doyle, and Katharina Ribbeck. Material properties of biofilms—a review of methods for understanding permeability and mechanics.Reports on Progress in Physics, 78(3):036601, 2015

    2015

  41. [49]

    Biofilms: investigative methods and applications

    Hans-Curt Flemming, Ulrich Szewzyk, and Thomas Griebe. Biofilms: investigative methods and applications. CRC Press, 2000

    2000

  42. [50]

    The effect of biofilm perme- ability on bio-clogging of porous media

    Thomas RR Pintelon, Cristian Picioreanu, Mark CM van Loosdrecht, and Michael L Johns. The effect of biofilm perme- ability on bio-clogging of porous media. Biotechnology and bioengineering, 109(4):1031–1042, 2012

    2012

  43. [51]

    Hydraulic resistance of biofilms

    Claudia Dreszer, Johannes S Vrouwenvelder, Astrid H Paulitsch-Fuchs, Arie Zwijnenburg, Joop C Kruithof, and H-C Flemming. Hydraulic resistance of biofilms. Journal of membrane science, 429:436–447, 2013

    2013

  44. [52]

    The permeability of biofouling layers on membranes

    Richard McDonogh, Gabriela Schaule, and Hans-Curt Flemming. The permeability of biofouling layers on membranes. Journal of Membrane Science, 87(1-2):199–217, 1994

    1994

  45. [53]

    Comparison of definitions of the lag phase and the exponential phase in bacterial growth

    MH Zwietering, FM Rombouts, and K van’t Riet. Comparison of definitions of the lag phase and the exponential phase in bacterial growth. Journal of Applied Bacteriology, 72(2):139–145, 1992

    1992

  46. [54]

    Laminar flow around corners triggers the formation of biofilm streamers

    Roberto Rusconi, Sigolene Lecuyer, Laura Guglielmini, and Howard A Stone. Laminar flow around corners triggers the formation of biofilm streamers. Journal of the Royal Society Interface, 7(50):1293–1299, 2010

    2010

  47. [55]

    Unraveling the biophysical underpinnings to the success of multispecies biofilms in porous environments

    David Scheidweiler, Hannes Peter, Paraskevi Pramateftaki, Pietro De Anna, and Tom J Battin. Unraveling the biophysical underpinnings to the success of multispecies biofilms in porous environments. The ISME journal, 13(7):1700–1710, 2019

    2019

  48. [56]

    Morphogenesis of biofilms in porous media and control on hydrodynamics

    Dorothee L Kurz, Eleonora Secchi, Roman Stocker, and Joaquin Jimenez-Martinez. Morphogenesis of biofilms in porous media and control on hydrodynamics. Environmental Science & Technology, 57(14):5666–5677, 2023

    2023

  49. [57]

    Brock biology of microorganisms, volume 11

    Michael T Madigan, John M Martinko, Jack Parker, et al. Brock biology of microorganisms, volume 11. Pearson Prentice Hall Upper Saddle River, NJ, 2006

    2006

  50. [58]

    Quantitative modelling of nutrient-limited growth of bacterial colonies in microfluidic cultivation

    Raphael Hornung, Alexander Gr¨ unberger, Christoph Westerwalbesloh, Dietrich Kohlheyer, Gerhard Gompper, and Jens Elgeti. Quantitative modelling of nutrient-limited growth of bacterial colonies in microfluidic cultivation. Journal of the Royal Society Interface, 15(139):20170713, 2018

    2018

  51. [59]

    Stochastic model for filtration by porous materials

    Filippo Miele, Pietro De Anna, and Marco Dentz. Stochastic model for filtration by porous materials. Physical Review Fluids, 4(9):094101, 2019. 21

    2019

  52. [60]

    Dynamics of fluids in porous media

    Jacob Bear. Dynamics of fluids in porous media. Courier Corporation, 1988

    1988

  53. [61]

    The implications of groundwater velocity variations on microbial transport and wellhead protection–review of field evidence

    Richard Taylor, Aidan Cronin, Steve Pedley, John Barker, and Tim Atkinson. The implications of groundwater velocity variations on microbial transport and wellhead protection–review of field evidence. FEMS Microbiology Ecology, 49(1):17– 26, 2004

    2004

  54. [62]

    Combining fluidic devices with microscopy and flow cytometry to study microbial transport in porous media across spatial scales

    David Scheidweiler, Pietro De Anna, Tom J Battin, and Hannes Peter. Combining fluidic devices with microscopy and flow cytometry to study microbial transport in porous media across spatial scales. J. Vis. Exp, 165:e60701, 2020

    2020

  55. [63]

    The rna chaperone hfq enables the environmental stress tolerance super-phenotype of p seudomonas putida

    Alejandro Arce-Rodr ´ ıguez, Bel´ en Calles, Pablo I Nikel, and V ´ ıctor de Lorenzo. The rna chaperone hfq enables the environmental stress tolerance super-phenotype of p seudomonas putida. Environmental microbiology, 18(10):3309–3326, 2016

    2016

  56. [64]

    Complete genome sequence and comparative analysis of the metabolically versatile pseudomonas putida kt2440

    KE Nelson, C Weinel, IT Paulsen, RJ Dodson, H Hilbert, V AP Martins dos Santos, DE Fouts, SR Gill, M Pop, M Holmes, et al. Complete genome sequence and comparative analysis of the metabolically versatile pseudomonas putida kt2440. Environmental microbiology, 4(12):799–808, 2002

    2002

  57. [65]

    The revisited genome of pseudomonas putida kt2440 enlightens its value as a robust metabolic chassis

    Eugeni Belda, Ruben GA Van Heck, Maria Jos´ e Lopez-Sanchez, St´ ephane Cruveiller, Val´ erie Barbe, Claire Fraser, Hans- Peter Klenk, J¨ orn Petersen, Anne Morgat, Pablo I Nikel, et al. The revisited genome of pseudomonas putida kt2440 enlightens its value as a robust metabol...

    2016

  58. [66]

    Inducible and tunable gene expression systems for pseudomonas putida kt2440

    Chandran Sathesh-Prabu, Rameshwar Tiwari, Doyun Kim, and Sung Kuk Lee. Inducible and tunable gene expression systems for pseudomonas putida kt2440. Scientific reports, 11(1):1–8, 2021

    2021

  59. [67]

    Trait-specific dispersal of bacteria in heterogeneous porous environments: from pore to porous medium scale

    David Scheidweiler, Filippo Miele, Hannes Peter, Tom J Battin, and Pietro de Anna. Trait-specific dispersal of bacteria in heterogeneous porous environments: from pore to porous medium scale. Journal of The Royal Society Interface, 17(164):20200046, 2020

    2020

  60. [68]

    Ultraviolet control of bacterial biofilms in microfluidic chips

    Gabriel Ramos, Clara Toulouze, Maya Rima, Olivier Liot, Paul Duru, and Yohan Davit. Ultraviolet control of bacterial biofilms in microfluidic chips. Biomicrofluidics, 17(2), 2023

    2023

  61. [69]

    Les fontaines publiques de la ville de Dijon: exposition et application

    Henry Darcy. Les fontaines publiques de la ville de Dijon: exposition et application... Victor Dalmont, 1856

  62. [70]

    Theoretical microfluidics, volume 18

    Henrik Bruus. Theoretical microfluidics, volume 18. Oxford university press, 2007

    2007

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.