Environ. Sci. Technol. 2009, 43, 3712–3719
Surface Association of Motile Bacteria at Granular Porous Media Interfaces KEVIN KUSY AND ROSEANNE M. FORD* Department of Chemical Engineering, University of Virginia, Charlottesville, Virginia 22904
Received November 26, 2008. Revised manuscript received March 24, 2009. Accepted March 26, 2009.
Bacterial populations were observed using dark-field light scattering at porous media interfaces comprised of a dilute solution containing the polymer additives methylcellulose and a transparent particulate suspension composed of mechanically agitated Gelrite gellan gum. Population-scale experiments with a nonchemotactic smooth-swimming mutant, Escherichia coli HCB 437, yielded a variety of distinct and reproducible bacterial distributions that included highly concentrated bands of bacteria near the interface. While no physical attachment was observed between the bacteria and granular Gelrite media, the population exhibited surface associations characterized by reversible physical obstructions of the motile bacteria at the solid granular surfaces. These interactions decreased translational motion, which reduced bacterial migration and concentrated bacterial populations near the interface. Results from glass bead experiments indicated similar surface associations in high-surface area glass bead environments. Experimental results were semiquantitatively analyzed using a onedimensional population-scale transport model. Theoretical profiles were generated using a single set of parameters and simultaneously compared with averaged bacterial distributions from multiple interface configurations. Parameter estimates were consistent with expected values. The agreement between the theoretical and experimental data suggests a quantifiable approach for modeling bacterial migration within highsurface area granular media environments.
Introduction Many bioremediation strategies require bacterial populations to migrate through complex porous media environments to regions of chemical contamination. Heterogeneities within the subsurface environment can affect the quantity of bacteria that reach the chemical contamination and thus the effectiveness of the bioremediation process. Packed-column experiments (1, 2), swarm plate assays (3), and in situ field studies (4) have illustrated that porous media can reduce migration rates and alter bacterial distributions. However, spatial bacterial distributions are often difficult to observe because the optical properties of the porous media limit direct observation of the bacteria without the use of invasive sampling techniques. Consequently, further research is required to fully understand bacterial migration near heterogeneous porous media interfaces. Motile bacteria disperse within their environment via a series of runs and tumbles referred to as random motility. * Corresponding author phone: (434) 924-6283; fax: (434) 9822865; e-mail:
[email protected]. 3712
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During chemotaxis the bacterial run times increase in response to chemical stimuli and concentrate bacteria in distinct bands near the source. Observations of a nonchemotactic mutant at a transparent porous media interface also demonstrated the formation of distinct bacterial bands (5). This result was unexpected because the bacteria were assumed to migrate by the diffusive process of random motility. Individual-scale observations revealed that the bands were produced by a nonchemical interaction or surface association between the motile bacteria and solid granular particles (5). Tethered cell experiments have shown bacteria to exhibit a distribution of predetermined run times (6). During surface association bacteria encountering solid surfaces are physically obstructed and retained at the surface for the duration of their run time. Though no physical attachment is evident, the reversible obstructions result in inelastic surface interactions that are similar to particles dispersing by sticky-Brownian motion (7). The surface association mechanism reduces population-scale migration rates and increases localized concentrations of bacteria near interfaces of heterogeneous porous media. Researchers have described bacterial migration in porous media using mechanistic adsorption approaches like colloid filtration theory (8, 9), effective motility coefficients that adjust free-solution motility values to account for path tortuosity (1), reaction-transport equations that use reaction terms to describe bacteria/surface interactions (10-12), and complex transport models with realistic up-scaling capabilities that account for variations in flow and subsurface heterogeneities (13). Our previous investigation introduced a two-phase transport model that combined the concepts of effective motility coefficients and surface association to predict population-scale migrations within high-surface area porous media environments (5). The model focused on the biologically driven migration of bacteria without the contributions of adsorption or advection which were negligible in our experimental system. The model was especially useful for determining bacterial distributions within heterogeneous particle environments and correlated with preliminary population-scale observations and a two-dimensional Monte Carlo computer simulation that modeled surface association in a heterogeneous particle system (5). Our objective is to investigate the biologically driven nonchemotactic migration of bacteria at granular porous media interfaces to evaluate population-scale responses including surface association. Thus, we present results of a smooth-swimming nonchemotactic mutant, E. coli HCB 437, at approximated one-dimensional interfaces composed of free-solution (methylcellulose) and/or granular porous media (Gelrite). These observations are compared with glass bead experiments that provide surface association observations at interfaces composed of media with well-defined particle size and geometry. In addition, the bacterial distributions from the polymer interface experiments are semiquantitatively analyzed using the one-dimensional two-phase transport model.
Materials and Methods Bacterial Strain. Experiments were performed with a K12 derivative smooth-swimming mutant (HCB437) of Escherichia coli obtained from H. C. Berg at Harvard University (14). E. coli HCB437 is deficient in the four signal transducers (Tar, Tsr, Trg, and Tap) and the chemotaxis genes: cheA, cheW, cheR, cheB, cheY, and cheZ (14), and cannot respond to chemical gradients by chemotaxis (14). 10.1021/es8033632 CCC: $40.75
2009 American Chemical Society
Published on Web 04/15/2009
Growth Conditions. Bacteria were grown and harvested according to a previously described protocol (5). Harvested bacteria were resuspended at a concentration of 1-3 × 108 cells/mL in random motility buffer (11.4 g K2H(PO)4, 4.8 g KH2(PO)4 and 0.029 g ethylenediaminetetraacetic acid (EDTA) per 1 L DI water) that contained methylcellulose or Gelrite particulates prior to performing an experiment. Solutions. Solutions were prepared following previous procedures (5). Fluid convection was reduced in the freesolution by dissolving 3.8 g/L Methocel HG90 hydroxypropyl methylcellulose (Biochemika no. 64680) in random motility buffer. The long-chained linear polymer increases macroscopic viscosity and has been reported to increase cell swimming speed by up to 20% (15). Visual inspection (see the Supporting Information (SI)) confirmed previous observations that bacteria can swim uninhibited through unbranched long-chain polymer solutions because the viscosity experienced by the bacteria is that of the surrounding aqueous solvent rather than the macroscopic viscosity of the solution (16). The granular porous medium was created by dissolving 1 g/L Gelrite in random motility buffer at 100 °C. The solution was mechanically agitated while cooling to room temperature to produce a transparent suspension (as opposed to a solid impermeable gel when agitation was omitted). Staining with a cationic dye revealed that the mechanically agitated Gelrite suspension was composed of irregularly shaped (50-500 µm) impermeable particles separated by voids of free-solution (see SI). Bacteria were observed to swim freely within the voids of the suspension (see SI) and abruptly stop translation motion when near the particle surfaces. Cell-body motion indicated that the bacteria attempted to swim even when physically obstructed by the Gelrite particles. Experimental Procedure. Bacteria were observed within borosilicate square capillaries obtained from Vitrocom Inc. (Mountain Lakes, NJ). Each capillary had a 1 mm2 inside cross-section, 0.2 mm thick walls, and was 2 in. in length. Each capillary was heat-sealed on one end with a Bunsen burner. Capillaries were filled by inserting a piece of Cole-Parmer Teflon microtubing attached to a 1 mL Becton-Dickinson syringe into the open end of a square capillary. After positioning the mouth of the tubing at the sealed end of the capillary, the syringe could be compressed to load the entire capillary with the first buffer solution. Then a second solution was injected into a portion of the capillary through a blunted 23-gauge stainless steel needle attached to a 1 mL BectonDickenson syringe. The increased viscosity of the solutions reduced mixing and produced an approximately onedimensional interface perpendicular to the length of the capillary. Different interface scenarios could be investigated by choosing to suspend the bacteria within either the methylcellulose solution or Gelrite suspension. Glass bead interface experiments were performed with Spheriglass Solid Glass Beads from Potters Industries Inc. The beads were prewashed in buffer solution and had average size distributions of 203, 119, 71, and 35 µm. Beads were inserted into capillaries loaded with a methylcellulose solution containing bacteria until approximately half of the capillary was filled. Prior to an experiment a new interface was established by agitating the contents and allowing the beads to settle in the sealed end of the capillary. Filled capillaries were inserted and secured inside a chamber constructed of two aluminum plates (see SI). The chamber oriented the capillary vertically to stabilize the interface and the narrow observation window milled through the chamber reduced glare associated with the external lighting apparatus. Light from Schott KL 1500 fiber optic light guides was angled to provide maximum contrast for dark-field light scattering analysis along the length of the
capillary. The capillary was observed using a Zeiss SV8 stereomicroscope attached to a Dage-MTI CCD72 camera. Images were captured every 60 s for 30 min using a Quick Image (Mass Microsystems) software program on a Macintosh IIci computer. Data Analysis. Portions of the capillary images were selected to minimize experimental artifacts associated with interface curvature and nonbacterial light scatter. Light intensity was averaged across the width of the capillaries using NIH Image Version 1.61/ppc to produce onedimensional normalized bacterial profiles. The normalized bacterial profiles were imported into a MATLAB program where multiple profiles for each interface scenario were compared by interpolating data points at a step size smaller than the minimum pixel width of the data. Using this technique, duplicate experiments with different image magnifications could be evaluated simultaneously to give the mean values and standard deviations of the experimental profiles. Population-Scale Mathematical Model. Kusy and Ford (5) provide a detailed description of the two-phase mathematical transport model presented as eqs 1a and 1b: ˆ µo,eff ∂2C ˆ ∂C ˆ + koffaSˆ - konaC ) ∂t ε ∂x2
(1a)
∂Sˆ ˆ - koffaSˆ ) konaC ∂t
(1b)
ˆ ) represents the bacterial concentration in freewhere (C solution, (Sˆ) represents the concentration of obstructed bacteria located at the solid surfaces of the porous media, t is time (seconds), ε is porosity, and µo,eff is the effective random motility coefficient (cm2/s), which accounts for the changes in bacterial swimming behavior due to the presence of the porous media. The term obstructed implies that the bacteria are in close proximity to or associating with the porous media surfaces without physical or chemical attachment. Therefore, the process was modeled using adsorption terms, although it was not a conventional adsorption process. Thus, kon is the association rate constant describing the rate that free-swimming bacteria are physically obstructed by the porous media surfaces (cm/s), koff is the dissociation rate constant describing the rate that obstructed bacteria depart from the porous media surfaces and return to the bulk liquid (cm/s), and a represents the surface area of the gel particulates per total volume (cm2/cm3). The quantity of bacteria associated with the solid surfaces of the porous media was described using a concentration per total volume which included free-solution and porous media. The free-solution and surface associated concentrations could be added to produce a total concentration for direct comparison with experimental profiles obtained through dark-field light scattering.
Results and Discussion Preliminary Observations of Wild-Type Bacteria. The swimming behavior of a wild-type strain and smoothswimming mutant of E. coli was investigated within granular porous media (see SI). Both strains of bacteria displayed surface associations, which were characterized by the reversible physical obstructions of the swimming bacteria. The smooth-swimming bacteria displayed longer interactions with the solid surfaces of the granular media because of their inability to tumble. However, population-scale experiments with the wild-type strain did not produce consistent bacterial distributions and were difficult to interpret. One possibility we considered was that the metabolic activity of the wildtype bacteria was consuming nutrients (i.e., substrates, oxygen, etc.) to threshold levels that could induce chemotactic responses within the environment. Because of the length of VOL. 43, NO. 10, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. The initial (left column) and 30 min (right column) sample images of various methylcellulose (MC) and Gelrite interfaces are presented: (a-b) MC/MC, (c-d) MC/Gelrite, (e-f) Gelrite/MC, and (g-h) Gelrite/Gelrite. Each frame displays the image of bacteria within the capillary (light regions) above a light intensity profile of the bacterial distribution. Although the images are shown with a horizontal orientation the experiments were conducted with capillaries that were oriented vertically. our experiments and the uncertainty of whether chemotactic responses were complicating our results, we chose to investigate smooth-swimming bacteria that were incapable of responding by chemotaxis (14). Thus, chemotaxis was excluded as a mechanism for the formation of the distinct localized bands. Interface Experiments with a Step-Change in Bacterial Concentration. To begin our investigation, we constructed four interface scenarios using a methylcellulose solution that represented a free-solution and a mechanically agitated Gelrite suspension that represented a porous media. Figure 3714
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1 displays the initial and 30 min images for a population of smooth-swimming bacteria initially located on the left-hand side of an approximate step change in methylcellulose/ methylcellulose, methylcellulose/Gelrite, Gelrite/methylcellulose, and Gelrite/Gelrite. Light intensity profiles located in the lower portion of each image corresponded with bacterial concentrations (lighter regions). Averaged profiles (solid lines) for replicate experiments are displayed in Figure 2. The first set of images in Figure 1 display bacterial profiles at a methylcellulose/methylcellulose interface. This scenario approximates a one-dimensional step change in bacterial
FIGURE 2. Bacterial profiles averaged from multiple methylcellulose (MC) and Gelrite experiments are presented. Data from multiple experimental profiles were interpolated to a common step size and compared. The resulting initial (gray) and 30 min (black) profiles are presented for interfaces between (a) MC/MC, (b) MC/Gelrite, (c) Gelrite/MC, and (d) Gelrite/Gelrite. Error bars represent standard deviations from multiple profiles, where the value n is the number of experimental samples. Standard deviations were presented for every 25th data point to represent the scatter between the profiles. concentration within a free-solution. As a result, the averaged bacterial profiles in Figure 2a have a diffusive shape that approximates results from other population-based experimental approaches such as the stopped flow diffusion chamber (17). The only deviations in the diffusive profile are observed in the right portion of the 30 min profile and can be attributed to light scatter on the outer surface of the capillary during a replicate experiment. The second set of images (c and d) in Figure 1 display the initial and 30 min bacterial distributions at a methylcellulose/ Gelrite interface. The images illustrate the migration of bacteria from the methylcellulose solution into the Gelrite suspension. Averaged profiles from multiple experiments are presented in Figure 2b. The averaged profiles are significantly different between the initial and 30 min time points, and are characterized by the development of a trough within the methylcellulose region and large peak just within the Gelrite suspension. The concentration of the peak in Figure 2b exceeds the initial concentration of bacteria (Co) within the system and provides evidence that a nondiffusive mechanism is affecting bacterial migration. A similar scenario within a thin cross section of media confirmed that the bacteria congregated near the Gelrite particle surfaces and suggested that the mechanism of surface association was influencing migration (see SI). In Figure 1e and f, very little change is observed between the initial and 30 min experimental images. Figure 2c supports these observations by illustrating no significant difference
between the averaged bacterial profiles. Thus, we can conclude that the presence of the Gelrite suspension is reducing the bacterial flux across the interface and that the mechanism of surface association is retaining the bacteria within the porous media region. Figure 1g and h display bacterial migration at a Gelrite/ Gelrite interface. Comparison between Figure 2a and d supports results from previous packed-column experiments by illustrating that the presence of porous media reduces bacterial migration (1, 2). However, the averaged profiles in Figure 2d show a slightly larger migration across the interface than presented in Figure 2c. In Figure 2d the bacteria are retained equally at the interface imitating a reduced diffusion profile. In Figure 2c, surface association only retains bacteria within the Gelrite suspension and profile changes within the methylcellulose solution (right side) are in such low quantities that they are not detectable via light scattering (see transport model solution in Figure 4c). Combining the results in Figures 1 and 2, we begin to develop a clearer picture of bacterial migration within our system. Unlike conventional representations of particle diffusion, the bacteria are not elastically colliding with the solid surfaces of the porous media. Instead, the predetermined run times of the bacteria are causing prolonged interactions that produce adsorption-like behavior without physical attachment. Consequently, we can observe nondiffusive bacterial migrations as represented in Figure 2b and c. These responses are only observed on a populationVOL. 43, NO. 10, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 3. Bacterial responses were observed at 203 µm average diameter (left column) and 71 µm average diameter (right column) glass bead interfaces initially (a and b) and after 30 min (c and d). The left edge of each sample image shows glass beads and bacteria, and the remainder of each image contains bacteria only. Graphs e and f represent the average profiles for multiple experiments, where n denotes the number of profiles that were analyzed. The initial profile (gray) and final profile (black) are presented as well as a standard deviation for every 25th data point (error bars). The white dots in panel b are glass beads that are still settling to the interface. scale at heterogeneous porous media interfaces where the juxtaposition of the porous media and free-solution causes the bacteria to exhibit surface association within only one of the two regions. The reversible surface interactions retard bacterial migration out of the porous media region and provide a driving force for the unique bacterial distributions observed in Figure 2b and c. Bead Interface Experiments with an Initially Uniform Concentration of Bacteria. Similar profile shapes were observed at porous media interfaces created with glass 3716
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beads. Glass beads were allowed to settle within vertically oriented capillaries that contained a uniform distribution of bacteria within a methylcellulose solution. Experiments were performed with glass bead average size distributions of 203, 119, 71, and 35 µm, but only the initial and 30 min images of the 203 and 71 µm bead experiments are presented in Figure 3. The images focus on the free-solution portion of the capillary because the optical properties of the glass beads prevented light scattering analysis of the bacterial population.
Figure 3a and c are the initial and 30 min images of an interface composed of 203 µm glass beads. Notice that a trough does not form over the observation period. Figure 3e supports this conclusion by illustrating no significant change between the initial and 30 min bacterial profiles for multiple experiments. In contrast, the initial and 30 min images in Figure 3b and d display the formation of a trough at an interface composed of 71 µm glass beads. Profiles from multiple experiments were consistent, and the shape of the trough for the averaged profile mimicked the free-solution profile presented in Figure 2b (trough on left side). While optical properties of the glass beads prevented direct observation, we presume a corresponding increase in bacterial concentration also occurred within the glass bead pack. The 119 and 35 µm bead experiments illustrated similar tendencies to the 203 and 71 µm bead-packed interfaces (see SI). Interfaces composed with 119 µm glass beads imposed no significant change in the bacterial profiles, while interfaces composed of 35 µm glass beads displayed similar sized troughs when compared to the 71 µm bead experiments. Thus, troughs were only observed for smaller-sized bead interfaces that were characterized by larger surface areas. Results from the glass bead interfaces suggested larger surface area environments promoted the nondiffusive mechanism of surface association (5). Data from the glass bead experiments also excluded viscosity effects as a possible mechanism for producing the bacterial responses. The viscosity of the solution was identical within both the free-solution and packed bead region yet only bacterial responses were observed for the smaller size distribution glass beads. Also of interest, the packed bead interface could be agitated and reestablished multiple times and yield reproducible results. This suggested that the bacteria were not physically attaching to the glass beads but were only physically obstructed and could return to the freesolution if reoriented in direction. Population-Scale Modeling of Bacterial Migration at Porous Media Interfaces. A previously described mathematical model was used to analyze the averaged bacterial profiles generated from the methylcellulose/Gelrite interface experiments (5). The model eqs 1a and 1b were solved using a semi-implicit finite difference algorithm that generated bacterial density distributions as a function of time. Figure 4 illustrates bacterial profiles generated from numerical solutions of eqs 1a and 1b. The transport model assumed that bacteria responded to the porous media environments by a combination of the effective motility and surface association mechanisms. These profiles are plotted versus the experimental data from the four interfacial scenarios presented in Figure 2 (points with error bars). The solid lines represent the solution of the finite difference code. The initial solution profile accounted for the delay in the initial image capture by relaxing the step change in bacterial density for a period of time; this same delay was amended to the 30 min solutions to maintain consistency. An identical set of parameters (µo,µo,eff/ε, kona, and koffa) was used to fit all four interface scenarios. The system was too complex to efficiently derive a mathematical best-fit profile; often the four parameters produced competing or synergistic effects. Thus, we used the following approach. The value for µo was determined using only the methylcellulose/methylcellulose data, because this scenario represented migration within a free-solution. The final three parameters were systematically fit by visual inspection according to the remaining profiles (Figure 4b-d) with a bias toward Figure 4b, because this scenario yielded a profile with more complexity than the others and justified the use of additional fitting parameters. The degree of partitioning across the interface was determined by the ratio of kona to koffa, where larger ratios produced higher degrees of parti-
tioning. In addition, the magnitude of kona and koffa affected the rate at which bacteria equilibrated between the bulk liquid and surface associated phases. As the values for kona and koffa increased, the system approached instantaneous equilibrium. Most of our investigations appeared to be within the instantaneous equilibrium region and therefore, the ratio of kona to koffa was a primary factor in controlling profile shape. The parameter values for µo,eff/ε determined the degree of bacterial front penetration and altered profile symmetry around the solution interface. Comparison of the experimental data and numerical solutions revealed a higher value for the random motility coefficient, µo ) 2 × 10-5 cm2/s, than normally expected for wild-type E. coli (µo ) ∼3 × 10-6 cm2/s) (17). Our experiments used smooth-swimming mutant E. coli that has been documented to display higher diffusion rates because of an increase in swimming run length and persistence (18). Thus, we would expect the random motility coefficient (µo) to be greater than the value reported for wild-type E. coli. Using the swimming parameters reported in Berg and Brown (18) for another smooth-swimming mutant and the equation relating these parameters to a random motility coefficient (19) we calculated µo ) 5 × 10-5 cm2/s. The ratio of the porosity to tortuosity, ε/τ ) 0.25 (calculated from(µo,eff/ε)/µo), also seemed reasonable given that the typical porosity value in a packed-bed of spheres with uniform diameter is 0.37 (20), and that size dependent tortuosity values typically range between 2 and 6 (21). While the Gelrite structure is neither uniform nor spherical, the bacterial profiles generated using the methylcellulose and Gelrite interfaces are consistent with the smaller-sized glass bead experiments illustrated in Figure 3 and suggest that the Gelrite suspension represents a highly packed porous media environment. The values for the association (kona) and dissociation (koffa) rate constants were 2.8 × 10-1 s-1 and 2.5 × 10-2 s-1, respectively. Comparison of the two values suggests that the system was in instantaneous equilibrium and that the ratio of kona to koffa was controlling the numerical profile shape. The presence of reversible adsorption is to be expected because the obstructed bacteria are not physically attached to the surface and can return to the bulk liquid at any time after reorienting in direction. Implications for Bioremediation. Surface association is another mechanism that affects bacterial migration in porous media environments. Olson et al. reported different tortuosity values for the migration of chemical tracers and bacterial strains through static columns packed with glass-coated polystyrene beads (22). To account for these discrepancies, they investigated the contributions of effective motility coefficients and Knudsen diffusion on the migration rate of the bacteria. Still, the final tortuosity values were larger for the bacterial strains than for the chemical tracer. Surface association offers an alternate explanation for the reduced bacterial migration rates. We would contend that both the chemical tracer and bacteria were experiencing similar tortuosities within the system, but that the chemical tracer was elastically interacting with the porous media surfaces. In contrast, the swimming persistence of the bacteria was producing extended interactions with those surfaces and retaining bacteria during their migration. In addition, field experiments by Becker et al. (4) have revealed that different strains of bacteria migrate through fractured crystalline bedrock at varied rates and motile strains of bacteria exhibited slower breakthrough curves at an observation point than nonmotile strains. The mechanism of surface association promotes extended surface interactions only for bacteria that are actively swimming, thus we would expect motile bacteria to be retained at higher rates than nonmotile bacteria. In addition, we might also expect VOL. 43, NO. 10, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Experimental data are compared with transport model predictions for a set of porous media interface experiments involving a population of smooth-swimming E. coli mutants. Four interface scenarios were investigated: (a) MC/MC, (b) MC/Gelrite, (c) Gelrite/MC, and (d) Gelrite/Gelrite, where the initial time point data is presented in gray and the 30 min time point data is presented in black. The error bars represent the standard deviations presented in Figure 2, where n denotes the number of profiles that were analyzed. The solid lines are output from a transport model that uses the concepts of effective motility coefficients and surface association to predict the bacterial profiles. The fitted model parameters used to evaluate eqs 1a and 1b were held constant for all four scenarios: µo ) 2 × 10-5 cm2/s, µo,eff/ε ) 5 × 10-6 cm2/s, kona ) 2.8 × 10-1 s-1, and koffa ) 2.5 × 10-2 s-1. the degree of surface association to change depending on the run length and swimming approaches employed by the bacteria leading to differences in migration rate according to strain variety. Consequently, individual-scale bacteria/ surface interactions like surface association can produce population-scale profile changes that can offer alternate explanations for field-scale related observations.
Acknowledgments We gratefully acknowledge J. Smith and E. Fernandez for their valuable discussions concerning the experimental data and G. Hornberger for conveying his knowledge about mathematical programming. Acknowledgment is made to the Donors of the American Chemical Society Petroleum Research Fund for support of this research (grant no. 38031AC9).
Supporting Information Available Digital recordings of bacterial swimming behavior are provided within random motility buffer (Figure S1), methylcellulose solution (Figure S2), Gelrite suspensions (Figure S4, S6, and S7), and a thin cross-section of a methylcellulose/ Gelrite interface (Figure S8). In addition, Figure S3 displays an image of the stained Gelrite suspension that illustrates the irregularly shaped particle structure. Figure S5 presents background information describing the capillary chamber 3718
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and provides further detail about the experimental procedure. Figure S9 supplies additional data and images for the 119 and 35 µm glass bead experiments. This material is available free of charge via the Internet at http://pubs.acs.org.
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(15) Shoesmith, J. G. The measurement of bacterial motility. J. Gen. Microbiol. 1960, 22, 528–535. (16) Berg, H. C.; Turner, L. Movement of microorganisms in viscous environments. Nature (London) 1979, 278, 349–351. (17) Lewus, P.; Ford, R. M. Quantification of random motility and chemotaxis bacterial transport coefficients using individualcell and population-scale assays. Biotechnol. Bioeng. 2001, 75, 292–304. (18) Berg, H. C.; Brown, D. A. Chemotaxis in Escherichia coli analyzed by three-dimensional tracking. Antibiot. Chemother. 1974, 19, 55–78. (19) Lovely, P. S.; Dahlquist, F. W. Statistical measures of bacterial motility and chemotaxis. J. Theor. Biol. 1975, 50, 477–496. (20) Duffy, K. J.; Cummings, P. T.; Ford, R. M. Random walk calculations for bacterial migration in porous media. Biophys. J. 1995, 68, 800–806. (21) Cussler, E. L. Diffusion Mass Transfer in Fluid Systems, 2nd ed.; Cambridge University Press: New York, NY, 1997. (22) Olson, M. S.; Ford, R. M.; Smith, J. A.; Fernandez, E. J. Analysis of column tortuosity for Mncl2 and bacterial diffusion using magnetic resonance imaging. Environ. Sci. Technol. 2005, 39, 149–154.
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