1. Physics of Living Systems
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A multiphase theory for spreading microbial swarms and films

  1. Siddarth Srinivasan
  2. C Nadir Kaplan
  3. L Mahadevan  Is a corresponding author
  1. Harvard University, United States
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Cite this article as: eLife 2019;8:e42697 doi: 10.7554/eLife.42697

Abstract

Bacterial swarming and biofilm formation are collective multicellular phenomena through which diverse microbial species colonize and spread over water-permeable tissue. During both modes of surface translocation, fluid uptake and transport play a key role in shaping the overall morphology and spreading dynamics. Here we develop a generalized two-phase thin-film model that couples bacterial growth, extracellular matrix swelling, fluid flow, and nutrient transport to describe the expansion of both highly motile bacterial swarms, and sessile bacterial biofilms. We show that swarm expansion corresponds to steady-state solutions in a nutrient-rich, capillarity dominated regime. In contrast, biofilm colony growth is described by transient solutions associated with a nutrient-limited, extracellular polymer stress driven limit. We apply our unified framework to explain a range of recent experimental observations of steady and unsteady expansion of microbial swarms and biofilms. Our results demonstrate how the physics of flow and transport in slender geometries serve to constrain biological organization in microbial communities.

https://doi.org/10.7554/eLife.42697.001

eLife digest

Bacteria can grow and thrive in many different environments. Although we usually think of bacteria as single-celled organisms, they are not always solitary; they can also form groups containing large numbers of individuals. These aggregates work together as one super-colony, allowing the bacteria to feed and protect themselves more efficiently than they could as isolated cells.

These colonies move and grow in characteristic patterns as they respond to their environment. They can form swarms, like insects, or biofilms, which are thin, flat structures containing both cells and a film-like substance that the cells secrete. Availability of food and water influences the way colonies spread; however, since movement and growth are accompanied by mechanical forces, physical constraints are also important. These include the ability of the bacteria to change the water balance and their local mechanical environment, and the forces they create as they grow and move.

Previous research has used a variety of experimental and theoretical approaches to explain the dynamics of bacterial swarms and biofilms as separate phenomena. However, while they do differ biologically, they also share many physical characteristics.

Srinivasan et al. wanted to exploit these similarities, and use them to predict the growth and shape of biofilms and bacterial swarms under different conditions. To do this, a unified mathematical model for the growth of both swarms and biofilms was created. The model accounted for various factors, such as the transport of nutrients into the colony, the movement of water between the colony and the surface on which it grew, and mechanical changes in the environment (e.g. swelling/softening). The theoretical results were then compared with results from experimental measurements of different bacterial aggregates grown on a soft, hydrated gel. For both swarms and biofilms, the model correctly predicted how fast the colony expanded overall, as well as the shape and location of actively growing regions.

Biofilms and other bacterial aggregates can cause diseases and increase inflammation in tissues, and also hinder industrial processes by damage to submerged surfaces, such as ships and waterpipes. The results described here may open up new approaches to restrict the spreading of bacterial aggregates by focusing on their physical constraints.

https://doi.org/10.7554/eLife.42697.002

Introduction

Bacteria employ sophisticated surface translocation machinery to actively swarm, twitch, glide or slide over solid surfaces (Kearns, 2010; Mattick, 2002; Spormann, 1999; Hölscher and Kovács, 2017). Collectively, they also aggregate into multicellular communities on hydrated surfaces and exhibit large-scale coordinated movement (Verstraeten et al., 2008). Surface motility in macroscopic colonies on hydrated surfaces such as gels occurs primarily via two distinct modes: either by rapid flagella-mediated swarming expansion (Harshey, 1994; Harshey, 2003), or alternatively by slow biofilm expansion driven by extracellular polymer matrix production (Hall-Stoodley et al., 2004). In both cases, an interplay between mechanical constraints and biological organization sets limits on the overall colony morphology and expansion dynamics (Persat et al., 2015). The forces driving colony expansion are generated by non-homogeneous patterns of biological activity, originating from spatial localizations in cell growth and division (Hamouche et al., 2017), extracellular polymer matrix production (Seminara et al., 2012; Yan et al., 2017; Srinivasan et al., 2018), osmolyte secretion (Ping et al., 2014) and active stresses (Farrell et al., 2013; Delarue et al., 2016). Conversely, the formation of localized biologically active zones is tightly coupled to the heterogeneity of the environment, including the diffusion and transport of nutrients (Wang et al., 2017), accumulation of metabolic by-products (Liu et al., 2015; Gozzi et al., 2017) and presence of quorum sensing and signaling agents that regulate cell-differentiation and development.

Consequently, the dynamics of colony growth requires a mechanistic description that accounts for spatiotemporal inhomogeneities in biological activity, emergent forces, and flows that transport metabolic agents. In bacterial swarming, cells within the colony are actively propelled by the rotation of flagella in a thin layer of fluid extracted from the underlying soft tissue or gel (Kearns, 2010). In contrast, bacterial biofilms are surface aggregates of sessile bacteria embedded in a self-generated extracellular polymer matrix (Flemming and Wingender, 2010). Despite marked differences in regulatory genetic pathways, morphology and cell function (Verstraeten et al., 2008), physical characteristics such as the fluidization of the substrate/tissue, gradients in nutrient availability, the low-aspect-ratio geometry and the existence of multiple phases (i.e. cells, biopolymer and fluid) are common to both bacterial film and swarm colonies. Motivated by these similarities, we present a unified multiphase framework that couples mechanics, hydrodynamics and transport to explain the dynamics of bacterial swarm and film expansion.

Experimental background

Bacterial swarms

Experiments on swarming colonies of E. coli (Darnton et al., 2010; Wu and Berg, 2012; Ping et al., 2014), S. enterica (Harshey and Matsuyama, 1994; Butler et al., 2010; Kalai Chelvam et al., 2014; Chen et al., 2007) and P. aeruginosa (Yang et al., 2017) reveal certain reproducible features associated with this modality of collective behavior. For example, E. coli swarms on agarose gels have a steady front shape that propagates radially at a uniform speed (Wu and Berg, 2012). In these swarms, measurements of the osmotic pressure profiles were found to be consistent with the active secretion of wetting agents in regions of high cell density that serve to fluidize the swarm by extracting water from the underlying tissue, thus allowing it to spread (Ping et al., 2014). These observations are not unique to E. coli; indeed our experiments with B. subtilis swarms, following (Kearns and Losick, 2003), indicate the same phenomena, that is a steady-state front shape and speed, as shown in Figure 1A–1E. Close to the spreading front, we observe a multilayer region of width W = 195 µm ± 35 µm, indicated by the dashed white lines in Figure 1B and 1C. The multilayer region correlates with increased colony thickness and local bacterial density (Wu and Berg, 2012). At the edge, and in the interior, there is just a monolayer of cells. The swarm radial expansion velocity is constant at V = 2 mm/hr (see Figure 1D) and the swarm front maintains a steady-state profile during expansion (see Figure 1E). These observations raise a number of natural questions associated with the steady-state velocity and profile of the swarm colony. Given the observations of osmotic gradient-driven flow in the vicinity of the growing front (Ping et al., 2014), coupled with variations in the thickness and activity of bacteria, any framework to explain these requires a consideration of a dynamic bacterial population interacting with ambient fluid, necessitating a multiphase description.

Experimental features of microbial swarms and biofilms.

(A) Snapshot of a Bacillus subtilis swarm expanding on a 0.5 wt% LB/agar gel. (B,C) Brightfield and fluorescent zoom images of the leading swarm edge of a MTC822 strain containing the fluorescent Phyperspank-mKate2 reporter that is expressed constitutively. The dashed white lines indicates the extent of the multi-cellular region. (D) Expansion velocity of the swarm measured at intervals of 10 s over a 10 min period. The solid line corresponds to a mean steady-state velocity of V= 2 mm/h. (E) Mean intensity traces of the constitutive fluorophore (mKate2) representing bacterial densities profiles plotted in the moving steady-state frame. The dark grey traces represent separate density profile measurements taken every 10 s in the advancing swarm. The solid line represents the density profile averaged over a period of 30 min. (F) A Bacillus subtilis biofilm colony developing on a 1.5 wt% MSgg/agar gel. (G,H) Brightfield and fluorescent zoom images of the biofilm colony formed by a MTC832 strain harboring the PtapA-cfp fluorescent reporter expressed in cells synthesizing the extracellular polymeric matrix (EPS). The dashed white lines indicates the extent of an active peripheral zone signifying localized EPS production. (I) Expansion velocity of the biofilm colony measured at intervals of 10 mins over a 72 hr period. The peak expansion velocity of V = 0.22 mm/h occurs at t ∼ 18 h after inoculation. (J) Azimuthally averaged matrix reporter activity (cfp) as a function of spatial distance within the biofilm.

https://doi.org/10.7554/eLife.42697.003

Bacterial films

In contrast with bacterial swarms, the spreading of bacterial biofilms is faciliated by the extracellular polymeric substance (EPS) matrix that expands via osmotic fluid influx, for example in B. subtilis (Seminara et al., 2012) and V. cholerae (Yan et al., 2017) biofilm colonies. However, EPS synthesis is not homogeneous, and depends on the local nutrient concentration and environmental heterogeneities experienced by cells within the same biofilm (Vlamakis et al., 2008; Berk et al., 2012). Recently, it was shown that the EPS matrix production is localized to cells in the propagating front of B. subtilis biofilms (Srinivasan et al., 2018). In Figure 1F–1J, we show the results of repeating these experiments, but now focusing on a peripheral region of a biofilm colony using a B. subtilis strain (MTC832) that harbors the P𝑡𝑎𝑝𝐴-cfp construct as a reporter for matrix production activity (Wang et al., 2016; Srinivasan et al., 2018). This highlights a 1 mm zone of matrix production activity at the periphery, seen in Figure 1G and H; indeed plots of averaged matrix production reporter intensity exhibit a distinct peak at the periphery, as shown in Figure 1J. The dynamics of radial expansion shows the existence of an initial acceleration regime followed by a transition to a second regime characterized by a monotonic decrease in expansion velocity, as plotted in Figure 1I. This transient mode of biofilm spreading driven by EPS production and swelling is quite different from that of bacterial swarming, and suggests that we might need a fundamentally different way to address its origins. However, if we now consider the EPS matrix and fluid as distinct phases (Cogan and Keener, 2004; Cogan and Keener, 2005; Winstanley et al., 2011; Seminara et al., 2012), with the bacterial population being relatively small, we are again led to a multiphase description of the system, but with a different dominant balance relative to that seen in bacterial swarms, which we now turn to.

Table 1
Definitions of fluxes for swarms and films

Definitions of the active phase horizontal flux Q1, the fluid phase horizontal flux Q2, active phase growth term g1(h,ϕ,c), osmotic influx term V0(x), and nutrient consumption term g2(h,ϕ,c) for bacterial swarms and films in the generalized thin film evolution equations described by Equations (1–3). Here, μ1 is the biofilm viscosity, μ2 is the fluid viscosity, pf is the fluid phase pressure, Π is the effective pressure in the active phase, g0 is effective swarmer cell growth rate, G is the EPS production rate, Γ is the nutrient consumption rate per unit concentration, K is the nutrient half-velocity constant and d is the thickness of the substrate. For swarms, the active phase corresponds to the swarmer cell phase, and for biofilms, the active phase is the EPS polymer matrix.

https://doi.org/10.7554/eLife.42697.004
VariablesSwarmsBiofilms
Flux (Phase I)Q1(x)h33μ2ϕ1ϕpfxhϕζpfxh33μ1Πx
Flux (Phase II)Q2(x)h33μ2pfxh33μ11ϕϕΠxhζ(1ϕ)2ϕpfx
Osmotic influxV0(x)Q0(ϕ1ϕϕ01ϕ0)Q0(ϕ3-ϕ03)
Growth termg1(h,c,ϕ)g0hϕ(1hϕHϕ0)GhϕcK+c(1hϕHϕ0)
Nutrient uptakeg2(h,c,ϕ)-ΓϕhdcK+c

Theoretical framework

Recent theoretical approaches have considered specific physical factors such as the wettability of the biofilm (Trinschek et al., 2016; Trinschek et al., 2017), osmotic pressure in the EPS matrix (Winstanley et al., 2011; Seminara et al., 2012), or Marangoni stresses associated with the swarm fluid (Fauvart et al., 2012), as reviewed by Allen and Waclaw (2019). However, a description that captures the experimental observations described in Figure 1 remains lacking. Here, given the similarities between the bacterial swarming and biofilm systems, we provide a unified description of their spreading dynamics by recognizing that in both cases we need to consider large slender microbial colonies with H/R1, where H is the colony thickness and R is the radius. This approximation results in a quasi-2-dimensional, two-phase model (assuming axisymmetry) of a colony that spreads along the x-axis, with a varying thickness, as shown in Figure 2. The subscript i = (1,2) denotes the actively growing phase and passive phase, respectively. Within the swarm colonies, the highly motile cells constitute the actively growing phase whereas the fluid comprises the passive phase. Similarly, in biofilms, the EPS matrix constitutes the active phase, and the aqueous fluid is the passive phase.

Geometry and variables governing colony expansion in (A) microbial swarms, and (B) bacterial biofilms, respectively.

In both cases, the total thickness of the microbial colony is h(x,t), the averaged nutrient concentration field is c(x,t), the volume fraction of the active phase is ϕ(x,t), the volume fraction of the fluid phase is 1-ϕ(x,t), and the fluid influx across the agar/colony interface is denoted by V0(x,t). As shown on the bottom panel, the active phase constitutes swarmer cells in the microbial swarm, and secreted EPS polymer matrix in the biofilm. The pressure in the fluid phase is pf and the effective averaged pressure in the active phase is Π. In the swarm cell phase, Π=pf, while the EPS phase effective pressure is Π=pf+ϕΨ(ϕ), where Ψ(ϕ) is the swelling pressure and is related to Flory-Huggins osmotic polymer stress (see Equation A28). The momentum exchange between the two phases is denoted by M, which includes the sum of an interfacial drag term and an interphase term as detailed in Equation (A11) in the Appendix.

https://doi.org/10.7554/eLife.42697.005

In both cases, colony growth occurs over a semipermeable soft gel substrate, as shown in Figure 2. We develop a continuous description of colony expansion in terms of variables which are coarse-grained depth integrated averages (Drew, 1983; Ishii and Hibiki, 2011), The averaged height of the colony interface is h(x,t), the volume fraction of the active phase (i.e., swarmer cells or polymer matrix) is ϕ1=ϕ(x,t) and the volume fraction of the fluid phase is ϕ2=1-ϕ(x,t). The 1-D substrate depth-averaged nutrient concentration field within the substrate is c(x,t). As detailed in Appendix 2, combining mass and momentum balances yields the following generalized set of partial differential equations that governs the dynamics of both expanding swarms and biofilms,

(1) ((hϕ))t+(Q1(x))x=g1(h,ϕ,c),
(2) (h(1ϕ))t+(Q2(x))x=(1ϕ)V0(x),
(3) ctDcxx=g2(h,ϕ,c).

where, ()x=()/x, etc. Here, Q1(x) is the horizontal flux in the active phase, Q2(x) is the horizontal flux in the fluid phase and V0(x) is the osmotically-driven net vertical fluid influx per unit length across the permeable substrate. Furthermore, g1(h,c,ϕ) is the depth integrated active phase growth rate within the bacterial colony, and g2(h,c,ϕ) is the depth integrated nutrient uptake rate. The dynamics of swarms and biofilms differ in the details of the expressions for Q1,Q2, V0, which are provided in Table 1. While a full derivation of each term is provided in Appendix 2, a direct comparison of the terms listed in Table 1 reveals a number of structural similarities and differences.

Nutrient uptake

For both swarms and biofilms, the active phase (i.e., swarm cells or the EPS matrix) is generated within the bacterial colony by converting nutrient in the underlying substrate to biomass. The rate of change of nutrient concentration within the substrate depends on diffusion and nutrient uptake (see Equation (3) and Equation (A2)), and is derived in Appendix 2. When the substrate concentration is scaled by the initial concentration c0, the nutrient depletion rate depends on Γ/c0, the ratio of the specific nutrient consumption rate to the initial concentration. Bacterial swarming is typically associated with nutrient rich conditions, where c0Γ. As a result, the nutrient uptake term can be neglected in bacterial swarming as g20, and the concentration cc0 throughout swarm expansion. In contrast, biofilm growth occurs under nutrient limited conditions where Γ/c0O(1), resulting in a corresponding uptake term shown in Table 1. Therefore, biofilm expansion is necessarily unsteady and driven by the dynamics of the transient nutrient field.

Growth

In both swarms and biofilms, the generation of the active phase drives colony expansion and is described by the growth term in Equation (1) using a logistic function g1=g0hϕ(1-hϕ/(Hϕ0)) to model the active phase growth, where Hϕ0 is the limiting thickness, and g0 indicates a specific growth rate. In bacterial swarms, g0 is independent of the nutrient concentration (as cc0 during swarm expansion). Therefore, the spreading swarm films have a steady-state structure that exhibits a central spatial plateau about hϕ=Hϕ0. In contrast, biofilm growth corresponds to a nutrient poor environment. We model the biofilm growth dependence on nutrient concentration via a minimal Michaelis-Menten form g0=Gc/(K+c), . Unlike in nutrient rich conditions associated with swarms, this implies that biofilm growth is fundamentally transient; once the nutrient field at the interior is depleted as c0, biofilm growth term in that region is arrested and g10 independently of the vertical thickness (i.e., even if hϕHϕ0). As a result, the biofilm does not give form a central plateau and the dynamics of the biofilm rim is fixed by the dynamics of nutrient depletion. Eventually the effect of the finite-size of the system (the petri dish) also becomes important it determines the overall dynamics of nutrient depletion.

Active and passive fluxes

The terms Q1(x) and Q2(x) that represent the horizontal flux of the active and passive phases are obtained by depth integrating the momentum balance equations in the thin-film lubrication limit, as described in Appendix 2 (c.f. Equations (A9)-(A11)). Within bacterial swarms, the passive aqeuous fluid phase is modeled as a Newtonian liquid with viscosity μ2. The first term of Q1(x) and Q2(x) in Table 1 for swarms is generated by viscous and capillary stresses within the swarm fluid. The active swarmer cells are treated as inviscid and subjected to a hydrodynamic frictional drag force. Specifically, we assume that individual bacteria within the swarm are undergoing a random walk process with zero net displacement (upon averaging over sufficiently large time-intervals). Even though there is no overall displacement, there is a net time-averaged drift that arises from viscous stokes drag interaction between the fluid and the active bacteria. The second term for Q1(x) in Table 1 represents this time-averaged drift arising from frictional drag interaction of the bacteria with the swarm fluid.

In biofilms, the EPS matrix phase constitutes an active viscous hydrogel network with viscosity μ1, whereas the passive aqueous fluid phase is treated as a solvent with viscosity μ2. The dominant stress within the EPS phase in the biofilm model arises from a Flory-Huggins swelling pressure in the polymer chains (Cogan and Guy, 2010; Winstanley et al., 2011). In the fluid phase, the pressure pf is set by surface tension and curvature of the swarm fluid. Both these stresses contribute to the effective EPS phase pressure term Π(x), as described in Appendix 2. Consequently, the first term for Q1(x) and Q2(x) in Table 1 for biofilms is related to the gradient of the effective pressure. Moreover, following Winstanley et al. (2011), we assume that the capillary and viscous stresses in the swarm fluid are negligible when compared to the frictional drag due to flow between water and the EPS polymer chain network in the biofilm model. Therefore, the second term for Q2(x) in Table 1 represents a Darcy-type flow of the aqueous phase within the EPS matrix. The osmotic influx terms are considered separately in the following sections when describing the equations governing swarm and biofilm expansion.

Bacterial swarms

Species of bacteria that swarm on hydrated surfaces are known to secrete distinct wetting agents. For example, B. subtilis secretes the lipopeptide surfactin, whereas P. aeruginosa secrets rhamnolipids as the wetting agent. Consequently, existing thin-film models to describe bacterial swarming assume that gradients in wetting agent activity generate Marangoni stresses that drives swarming motility (Fauvart et al., 2012; Trinschek et al., 2018). However, E. coli exhibits swarming behavior despite the absence of lipopeptides or other agents that act as surfactants. Moreover, recent experiments (Yang et al., 2017) demonstrates that P. aeruginosa swarms robustly even after exogenously eliminating gradients in surfactant concentration within the swarm fluid, eliminating Marangoni flows as the principal mechanism that drives swarming. Here, we take a different approach based on experiments that show that steady-state swarm colony expansion maybe mediated by secretion of agents that are osmotically active (Wu and Berg, 2012). As we will see, this leads to fluid being extracted from the substrate near the front, then driven into the colony by capillary and viscous stresses, and eventually returns into the substrate in the interior of the swarm.

Within the bacterial swarms, the dominant phases are the swarmer cell phase, and the viscous aqueous phase, as shown in the bottom panel of Figure 2A. Fluid uptake from the substrate is regulated by the secretion of osmotically active agents by the swarmer cells (Ping et al., 2014). We represent the osmotic agent in the fluid by a concentration field, cosm(ϕ) that is proportional to the local volume fraction of cells such that cϕ/(1ϕ), and gives rise to an osmotic pressure described by van’t Hoff’s law as (van't Hoff, 1887), ΔΨ=(Ψ0ϕ/(1-ϕ)-Ψeq), that drives the fluid intake. Here, Ψ0 is the osmotic pressure scale in the swarm fluid and Ψeq is the equilibrium osmotic pressure within the underlying tissue/gel substrate. Away from the front, in the interior of the swarm colony, there is no net fluid influx (Ping et al., 2014). Therefore, the equilibirium volume fraction of the swarm cells at the interior is, ϕ0=Ψeq/(Ψ0+Ψeq). At the front itself, the difference in osmotic pressure results in a net Darcy-type fluid influx into the swarm, V0(x), expressed as,

(4) V0(x)=Q0(ϕ(x)1-ϕ(x)-ϕ01-ϕ0).

where Q0 is a velocity scale associated with fluid inflow from the substrate. Measurements of cell replication within swarms reveals that growth is restricted to swarmer cells at the periphery (Hamouche et al., 2017), which we model using a modified logistic growth term g1(h,ϕ) as listed in Table 1, that localizes all cell division to the periphery. Here, Hϕ0 is the limiting thickness of the swarmer-cell phase at the interior, and g0 is an effective specific growth rate, related to true specific cell growth rate by a geometric factor (see discussion in Appendix. [2]).

Parameters and scaling laws for bacterial swarms

To make sense of the scales in the problem, we use the dimensionless variables x^=x/L, z^=z/H and t^=tg0 where H is the vertical length scale, L is a horizontal length scale and 1/g0 is the time-scale associated with bacterial growth. The resultant horizontal velocity scale in the swarm colony is U=Lg0. Swarm expansion is fluid driven, and therefore balancing the viscous stresses generated in the swarm fluid, with the curvature pressure due to surface tension (Levich and Landau, 1942) results in μ2U/H2γH/L3, where μ2 is the viscosity and γ is the surface tension of the aqueous phase. As a result, the natural horizontal length scale is L=H(𝐶𝑎)-1/3, where 𝐶𝑎=(μ2U/γ) is a capillary number associated with the microbial swarm fluid. Consequently, in our model the expansion speed of the swarm colony, V=dR/dt, is determined by the product of the horizontal length scale and an effective growth rate, and is predicted to scale as,

(5) V=C1g0H𝐶𝑎-1/3.

whereas, the swarm front itself is analogous to a capillary ridge in thin fluid film with a width W that is predicted to scale as,

(6) W=C2𝐶𝑎-1/3.

where, C1 and C2 are dimensionless prefactors that require a detailed numerical calculation, and are discussed later. There are two important dimensionless parameters that describe swarm colony expansion. The first dimensionless parameter, α1, relates the magnitude of capillary forces to the viscous drag acting on cells within the swarm and is defined as α1=(γH/L2)/(ζLU). Here, ζ=ζc/Vc where ζc is the friction coefficient of a single swarmer cell and Vc is its volume. The second dimensionless parameter α2 is defined as the ratio of a vertical fluid influx velocity Q0, to a thickness velocity scale Hg0 associated with bacterial growth as α2=Q0/(Hg0).

The vertical length scale and equilibrium fluid volume fraction are estimated from the interior monolayer region as H = 0.5 μm and ϕ0=0.5 (Wu and Berg, 2012). We assume values of μ2=10-3 Pa.s for the (aqeuous) swarm fluid viscosity, and γ=10-2 N/m as its surface tension. The friction coefficient of a single cell is estimated from Stokes law as ζc=3πμ2a, and its volume is approximated as Vc=πa3/6, where a = 1 μm is the cell diameter. Therefore, the friction coefficient is ζ=ζc/Vc18μ2/a2. As a result of substituting the values of known parameters above, the dimensionless parameter α1 reduces to a constant geometric ratio, α12a2/H22/90.22.

The value of α2 depends on the ratio Q0/g0. Direct experimental measurements of the vertical influx fluid velocity profile V0(x) and the spatial profiles of cell division in swarm colonies remain scarce (Hamouche et al., 2017). In order to make progress in validating our model with real experimental data, the vertical fluid influx velocity scale is chosen as Q0=102μm/s. Consequently, we have chosen g0 as the only fitting parameter in our study, as detailed in Appendix 2. As an example, in the following section we will show that a choice of g0=0.013 s-1 in our model reproduces the experimental swarm expansion speed shown in Figure 1D, and leads to a horizontal length scale of L=H(Ca)1/3=100μm, velocity scale of U=Lg0=1.3μm s-1, 𝐶𝑎=1.3×10-7 and a value of α21.5. A complete set of parameters for three experimental measurements of swarm expansion in B. subtilis, and two existing measurements in E. coli previously reported by Darnton et al. (2010) and Wu and Berg (2012) are summarized in Appendix 2.

Steady state swarms

With these assumptions, and assuming that the nutrient concentration is constant, Equations (1–3) reduce to the following scaled equations in the swarming limit,

(7) (h^ϕ)t^+13(ϕh^3h^x^x^x^1-ϕ)x^+α1(h^h^x^x^x^ϕ)x^=h^ϕ(1-h^ϕ)
(8) (h^(1-ϕ))t^+13(h^3h^x^x^x^)x^=α2ϕ-ϕ01-ϕ0.

To complete the formulation of the problem, we need five boundary conditions which are h^x^(0)=h^x^(RP)=0, h^x^x^x^(0)=h^x^x^x^(RP)=0, and ϕ(0)=ϕ0, where RP is the dimensionless size of the petri-dish and is set much larger than the colony size (R^=150) in our simulations. The initial condition corresponds to a circularly inoculated swarm colony, along with a thin pre-wetting film where no bacterial growth occurs (see Appendix 3—figure 1).

Solving Equations (7) and (8) with the prescribed initial and boundary conditions numerically results in a steady state solution that advances at a constant speed (see Figure 3). In Figure 3, we plot a representative steady state solution in the frame of the advancing front for α1=0.2, α2=1.5 and ϕ0=0.5. At the interior of the swarm, the average cell volume fraction is ϕϕ0. Near the leading edge of the swarm, there is a region of enhanced thickness as indicated by the red line in Figure 3A. Immediately behind the leading edge, where the cell concentration is highest, so is the osmolyte concentration leading to fluid extraction from the substrate, while further behind, fluid is reabsorbed, as indicated by the arrows in Figure 3A. In Figure 3B, we show the steady-state osmotic flow solution and see that it correlates well with the experimentally measured osmotic pressure profile by Ping et al. (2014) in E. coli swarms. As shown in Appendix 3—figure 3, our numerical horizontal flow profiles are also consistent the scaled radial fluid velocity measurements of Wu and Berg (2012). In Figure 3C, we see that the radial expansion velocity scales as Hg0 and shows quantitative agreement with experiments and is insensitive to the fluid influx velocity scale when Q0g0H. Note that our model uses a coarse-graining procedure and represents the swarm thickness field using a continuum approximation. As a consequence, we are not able to quantitatively capture the decreasing height of the swarm (i.e., of the order of a few cells), that is experimentally observed over hundreds of micron towards the interior (see Figure 1E).

Steady-state morphology and fluid transport in a bacterial swarm obtained by solving (8) for α1 = 0.2, α2 = 1.5 and ϕ0 = 0.5.

(A) Plot of the steady-state thickness h^=h/H against the scaled distance x^-R^, where x^=x/(H𝐶𝑎-1/3) and R^ is the radius. The solid red line indicates a region of increased thickness, and the colormap quantifies variations in ϕ, the local volume fraction. (B) Plotted on the left-axis is the numerical steady-state fluid uptake profile within the swarm (solid line) calculated from Equation (4). On the right axis are experimental measurements of the steady-state osmotic pressure within an expanding E. coli swarm (filled circles), reproduced from Ping et al. (2014), with the baseline reference value shifted to zero, and with distances normalized by L = 50 μm. (C) Predicted steady-state radial colony expansion speeds within the swarm for values of α2=Q0/(Hg0)=1, 10 and 100 respectively. The data points are expansion speeds in B. subtilis swarms measured over 20 min, and scaled using U = 1.3 μms-1 and g00.013 s-1. (D) Comparison between the swarm expansion velocities dR/dt measured for five separate colonies (see Appendix 2) and the estimated capillary number. For each experiment, g0 was obtained by fitting the steady state solution of Equations (7) and (8) to the swarm velocity. The dashed line corresponds to the predicted scaling law in Equation (5).

https://doi.org/10.7554/eLife.42697.006

Furthermore, we corroborate our scaling law in Equation (5) by fitting our model to five independent experimental measurements of swarm expansion velocities for different systems, as shown in Figure 3D. These include measurements in B. subtilis swarms in this work, and in E. coli swarms previously reported by Darnton et al. (2010) and Wu and Berg (2012) that are summarized in Table A2 in Appendix 2. The expansion velocity follows the -1/3 exponent predicted by Equation (5) for Ca varying from ∼ 5 × 10-8 to 10-6. For each experiment, we have fit our theoretical model using the effective growth rate g0 as the fitting parameter and find that the numerical prefactor C10.42. However, as shown in Appendix 3—figure 5 in the Appendix, the measured multi-layer width does not follow the predicted scaling. From an experimental point of view, the width of the multi-layer region is not sharply defined in Figure 1E, and will depend on the choice of threshold. However, our multi-phase model is able to describe the zone of cellular and osmolyte activity near the leading edge that drives the advancing swarm front. This leads to a picture wherein the combination of a fluid-filled substrate and swarm front work together like a localized active circulatory system, quantitatively rationalizing the experimental observations of Wu and Berg (2012) and Ping et al. (2014).

Bacterial films

In bacterial biofilms, the EPS matrix secreted by bacteria constitutes the active phase and undergoes swelling, drawing in the fluid that acts as the passive phase. As shown in Figure 2B, the EPS is initially synthesized in a partially swollen, out-of-equilibrium state at the periphery. The polymer chains gradually relax to an equilibrium fully-swollen configuration by the generation of a swelling pressure Ψ within the biofilm, and via fluid uptake V0(x) from the substrate. As discussed in Appendix 2, the swelling pressure is Ψ(ϕ)=ψ(ϕ)/ϕ, where ψ(ϕ)=ψ0×ϕ3 is the osmotic pressure in the EPS matrix using the Flory-Huggins model for a polymer network in a θ-solvent (Rubinstein and Colby, 2003), where ψ0=kT/(b3) is the osmotic pressure scale, kT is the product of the Boltzmann constant with the temperature and b is the approximate size of the monomer unit. The net effective pressure term driving biofilm expansion is, Π=ψ0ϕ3+pf, where pf is the capillary pressure, so that the water influx across the substrate is

(9) V0(x)=Q0(ϕ3-ϕ03).

where Q0 is the influx fluid velocity scale, ϕ0=(Ψeq/Ψ0)1/3 is the fully-swollen EPS polymer volume fraction and Ψeq is the osmotic pressure of the substrate over which the colony grows. Finally, nutrient uptake is modeled by a Monod growth law, while the synthesis of the EPS matrix is modeled by a logistic term as listed in Table 1.

Parameters and scaling estimates for bacterial films

We consider dimensionless variables x^=x/L, z^=z/H, t^=tG, ϕ^=ϕ/ϕ0 and c^=c/c0, where H is now the maximum biofilm thickness, G is the rate of EPS production, and c0 is the initial nutrient concentration in the substrate. As biofilm growth is nutrient limited (Liu et al., 2015), the dimensionless length scale is determined from Equation (3) and is expected to scale as L=(D/G)1/2 and the corresponding velocity scale is U=(DG)1/2.

Using these scales, we can define the ratio of osmotic stresses relative to viscous stress in the EPS phase in terms of the dimensionless parameter, β1=(Ψ0/L)/(μ1U/H2), the ratio of capillary stresses relative to the EPS viscous stress in terms of another parameter, β2=(γH/L3)/(μ1U/H2), the ratio of capillary stress to the interfacial drag in the aqueous fluid phase, β3=(γH/L2)/(ζUL), and the ratio of the fluid influx velocity to the EPS swelling velocity, β4=Q0/(HG). As shown in Appendix 2, the effective nutrient uptake rate is S=(ΓHϕ0)/(c0d), where Γ is the nutrient consumption rate per unit concentration and d is the substrate thickness. Consequently, we define β5=S/G as the ratio of the effective nutrient uptake rate to the EPS production rate.

We set the EPS production time-scale as G=1/40 min-1, resulting in a horizontal length scale of L=(D/G)1/2=1.1 mm and velocity scale U=(DG)1/2=0.5μm/s. The effective nutrient uptake rate is estimated as S=1/25 min-1, where we have taken d=7 mm as the substrate thickness (Srinivasan et al., 2018), Γ=10-2 mM/s as the nutrient uptake rate (Zhang et al., 2010), and c0=35 mM as the initial concentration of the carbon source. The friction coefficient is ζμ2/ξ2, where the EPS mesh size is ξ=50 nm (Yan et al., 2017). Using measured estimates of the biofilm viscosity μ1=105 Pa.s (Stoodley et al., 2002; Lau et al., 2009), fluid phase viscosity μ2=10-3 Pa.s, surface tension γ=10-2 N/m, an osmotic scale Ψ0=2100 Pa (Yan et al., 2017) (i.e., ϕ0=0.04), biofilm thickness H=400μm, and nutrient diffusivity in agarose gels of D=5×10-10m2/s (Zhang et al., 2010) implies that β17, β20.01, β30.02, β41 and β52. Consequently, within the context of our model, it is evident that osmotic stresses, fluid influx and biomass growth are the dominant forces that drive colony expansion. Moreover, in the nutrient limited regime, our model predicts the transient maximum biofilm expansion velocity to scale as,

(10) V=C3(DG)12

whereas, the width of the propagating fronts of EPS production experimentally observed by Srinivasan et al. (2018) is predicted to scale according to,

(11) W=C4(DG)12

where C3 and C4 are once again dimensionless prefactors that require a detailed numerical calculation, as discussed later.

Transient biofilm solutions

With the above scaling assumptions, Equations (1–3) now reduces to the following partial differential equations that describe biofilm colony expansion,

(12) (h^ϕ^)t^β13ϕ0(h^3(ϕ^3)x^)x^+β23ϕ0(h^3h^x^x^x^)x^=c^h^ϕ^(1h^ϕ^)K1+c^,
(13) (h^(1ϕ0ϕ^))t^β13(κ(ϕ^)h^3(ϕ^3)x^)x^+β23(κ(ϕ^)h^3h^x^x^x^)x^+β3(h^(1ϕ0ϕ^)κ(ϕ^)h^x^x^x^)x^=β4(1ϕ0ϕ^)(ϕ^31),
(14) c^t^-c^x^x^=-β5h^ϕ^c^K1+c^.

where κ(ϕ^)=(1-ϕ0ϕ^)/(ϕ0ϕ^) is a volume fraction dependent permeability term. The eight boundary conditions associated with Equations (12)–(14) are the symmetry boundary conditions h^x^(0)=h^x^(RP)=0, h^x^x^x^(0)=h^x^x^x^(RP)=0, ϕ^x^(0)=ϕ^x^(RP)=0, ϕ^x^x^x^(0)=ϕ^x^x^x^(RP)=0 and c^x^(0)=c^x^(RP)=0, where the dimensionless petri-dish size is chosen as RP=16 to match the size of typical 35 mm diameter petri dishes used in experiments (Srinivasan et al., 2018). In Figure 4A, we plot the time evolution of the shape and nutrient concentration field for a biofilm colony of initial radius R^0=0.5 and thickness h^in=0.06.

Dynamics of EPS production and biofilm expansion obtained by solving (12 - 14) with β1=6.7, β2=0.01, β3=0.02, β4=1 and β5=1.7.

(A) On the top are thickness profiles h^(x^,t^) of an expanding biofilm colony, at time intervals of t^ = 1, 5, 20, 50 and 90. The nutrient field c(x,t) at corresponding time intervals is plotted at the bottom. The dotted red line indicates profiles at t^c = 8, the transition point between the bulk and edge expansion regimes. (B) Variation of the vertical fluid uptake profile within the swarm calculated from Equation (9). The light blue lines correspond to the bulk growth regime for t^ = 1, 5 while the red lines correspond to t^ = 20, 50 and 90 in the edge growth regime. (C) Plots of normalized EPS production activity within the biofilm, where g1 is evaluated using the expression in Table 1. The data points are spatial measurements of tapA gene activity in B. subtilis biofilms reproduced from Srinivasan et al. (2018), with distances scaled by L = 550 μm. (D) Solid lines indicate transient colony edge expansion velocities for β4=1,10 and 100 respectively, and with other parameter values fixed as listed above. The experimental data is reproduced from Srinivasan et al. (2018) and indicates median expansion velocities (filled circles), the 25th to 75th percentile velocities (filled box), and extreme values (vertical lines), where the data has been scaled by U=0.5μms-1 and G=1/40 min-1.

https://doi.org/10.7554/eLife.42697.007

Unlike in the case of swarms, the solutions to Equations (12)–(14) are transient, and exhibit two distinct expansion regimes: initial acceleration phase until t^c=5, followed by a decelerating phase beyond. For t^<t^c, colony expansion arises as the microbes rapidly consumes locally available nutrient at the interior and synthesize fresh EPS matrix, generating spatial gradients in nutrient availability (see Figure 4A). In Figure 4B, we show that the newly synthesized EPS generates a large osmotic pressure differential between the biofilm and the substrate, and osmotic fluid influx gradually relaxes the biofilm matrix to a swollen configuration. For t>tc, the localized zone of EPS production near the film front propagates with a fixed shape as shown in Figure 4C, consistent with the observed spatial localization in tapA gene activity (see Figure 1J and Srinivasan et al., 2018). Moreover, the radial colony expansion profile in Figure 4D is also consistent with the non-monotonic front speed observed experimentally (Srinivasan et al., 2018). For the specific experimental conditions we consider, our detailed theory allows us to estimate the prefactors in the scaling laws Equations (10)–(11) so that C30.2 and C41.8.

These results are hallmarks of a transition from a bulk to an edge biofilm growth mode, triggered by nutrient limitation (Pirt, 1967). In the deceleration regime, diffusive transport of nutrients from a region external to the colony continues to sustain EPS production at the biofilm periphery, analogous to Stefan-like problems in solidification. Our generalized multiphase model is thus able to quantitatively rationalize the expansion curves, transition time and localized biological activity observed experimentally, and demonstrates that nutrient availability and diffusive transport governs the dynamics of Bacillus subtilis macrocolonies grown on agar.

Discussion

Analysis of collective microbial expansion in thin film geometries often prioritizes biological mechanisms, such as genetic regulation, developmental programs and cellular signaling/competition, over the role of the heterogeneous physical micro-environments. Here we have presented a multi-phase theory that quantitatively describes the expansion dynamics of microbial swarms and biofilms and considers variations in the colony thickness, an aspect of colony expansion that has often been overlooked in many theories (Korolev et al., 2012; Ghosh et al., 2015; Wang et al., 2017). The resulting unified description of both steady-state swarms and transient biofilm spreading leads to simple estimates and scaling laws for the colony expansion rate that are validated via comparison with experimental measurements for different systems. In swarms, exudation of water from the permeable substrate via bacterial osmolyte secretion facilitates steady state colony expansion. Numerical solutions of our model demonstrate that the shape of the swarm front is determined by capillarity, and its expansion speed by cell-division and growth, leading to scaling laws validated by comparison with previous experiments. In contrast, transient biofilm macrocolony expansion on agar is driven by osmotic polymer stresses generated via EPS matrix production in a spatially localized zone at the periphery. Nutrient transport and depletion leads to the formation of these heterogenous zones, and results in two regimes in biofilm expansion.

However our depth-integrated theory also has certain limitations. For example, we are unable to capture discrete thickness variations of the order of a few cells, which might require an agent-based approach. For bacterial swarms, our model is unable to quantitatively account for the region of enhanced thickness (i.e., the multilayer region in Figure 1C and E), likely because the multilayer width is difficult to experimentally ascertain, owing to the large tail distribution seen in the mean intensity trace in Figure 1E, and the arbitrariness in the choice of threshold in Appendix 3—figure 5. Similarly, in the context of biofilm colony expansion, our model does not account for sliding and frictional contact between the cells/EPS matrix and the substrate (Farrell et al., 2013). More generally, our mean-field picture neglects fluctuation-driven effects during colony expansion, such as the formation multicellular raft structures (Kearns, 2010) and synchronized long-range interactions (Chen et al., 2017).

Natural next steps of our approach include (i) adding three-dimensional effects by allowing for spatial variations in the mechanical stresses, flows and nutrient fields in the vertical direction, (ii) accounting for orientational order in the bacterial swarms and films, and (iii) accounting for interfacial tension on the stability of the growing swarm/biofilm-fluid interface, especially in the context of fingering instabilities in microbial colonies Trinschek et al. (2018).

A rigorous multi-phase approach may also be relevant in revisiting pattern formation phenomena in microbial colony expansion (Matsushita et al., 1999), that so far been addressed primarily using various non-linear diffusion models (Golding et al., 1998; Allen and Waclaw, 2019) that ignore the third dimension. Finally, from an experimental and theoretical perspective, our results naturally raise the question of controlling biofilm and swarm expansion by manipulating water and nutrient availability, complementing the better studied approaches of manipulating colonies by the genetic regulation of EPS production, cell division, and chemical signaling in microbial colonies.

Appendix 1

Experiment

Strains

In this study, we used two B. subtilis strains, MTC822 and MTC832, that were both previously constructed from a wild-type NCIB 3610 B. subtilis strain using a standard transformation protocol (Sinha, 2013). The MTC822 strain is used for fluorescence visualization in the swarming experiment, where the mkate2 red fluorescent protein reports on the activity of the constitutive hyperspank promotor via the amyE::Phyperspank-mkate2 construct. The MTC832 strain was used in the biofilm experiments in order to visualize localized matrix production activity and harbors the amyE::PtapA-cfp construct. In the MTC832 strain, the cfp cyan fluorescent protein reports on the activity of the tapA gene that is associated with exopolysaccharide production activity.

Materials and methods

Swarm plates were prepared using 0.5 wt% agarose gel (A1296, Sigma) infused with 25 ml of Luria-Bertani (Miller) medium (i.e. 10 g/L Tryptone 10 g/L NaCl 5 g/L Yeast Extract, Sigma) and 25 μg/ml Chloramphenicol. Biofilm plates were prepared using 1.5 wt% agarose gel (A1296, Sigma) infused with the standard MSgg biofilm-inducing growth medium (Branda et al., 2001) (i.e. 50 μM MnCl2, 5 mM KH2PO4, 1 μM ZnCl2, 50 μM FeCl3, 2 mM MgCl2, 700 μM CaCl2, 50 μg/ml threonine, 50 μg/ml tryptophan, 50 μg/ml phenylalanine, 0.5 wt% glutamate, 0.5 wt% glycerol, 2 μM thiamine and 100 mM MOPS (pH 7)) and 50 μg/ml Spectinomycin. Note that all plates underwent an identical drying protocol prior to use. Freshly poured plates were initially dried with the lid open under a laminar flow hood for 15 min. Subsequently, the lid was closed and the dish was cooled at 25 C overnight for a period of 10 hr. All strains were initially grown in fresh Luria-Bertani (Miller) broth medium (Sigma) until mid-exponential phase in a shaker/incubator at 37C. The cultures were diluted to OD650=0.1 and ~ 1 μl drop was deposited onto the corresponding swarm (for MTC822) or biofilm (for MTC832) plates. The petri-plates were transfered to a 30C incubator chamber during growth. Fluorescence imaging was performed using a Zeiss Axiozoom.V16 microscope with a PlanNeoFluar Z 1.0x objective (NA 0.25), with a Zeiss 63 HE filter to image the red mkate2 protein, and a Zeiss 47 HE filter to image the cyan cfp protein. For swarm profile measurements, images of the advancing swarm front were captured every 10 s over a period of 10 min. For biofilm colonies, expansion velocities were measured every 10 min over a period of 72 hr following the protocol described in Srinivasan et al. (2018).

https://doi.org/10.7554/eLife.42697.011

Appendix 2

Theory

Consider a quasi-2D expanding swarm or biofilm colony where, x denotes the horizontal direction and z denotes the vertical (thickness) direction. The macroscopic colony is described by (i) the thickness field h(x,t), (ii) the volume fraction of the active phase (i.e. swarmer cells or EPS matrix ϕ(x,t) and (iii) the averaged concentration field in the substrate c(x,t).

Nutrient transport

We assume that c~(x,z,t) is the nutrient concentration in the substrate denoted by region d<z<0, where z=0 is the substrate/colony interface and d is the substrate thickness. The time evolution of c~(x,z,t) is governed by the diffusion equation.

(A1) c~t-D2c~=0

where D is the diffusivity of the nutrient in the agarose gel. Integrating the diffusion equation through the substrate thickness results in,

(A2) ct-D2cx2=-Γhϕdc(K+c)

where c(x,t)=(1/d)-d0c~𝑑z is the mean depth-averaged nutrient concentration in the substrate, Γ is the specific nutrient consumption rate, and K is the half velocity constant at which the specific growth rate is one half the maximum value. In obtaining Equation (A2), we have balanced nutrient efflux from the substrate with nutrient consumption within the colony as D(c~/z)|-d=-Γhϕc/(K+c). Equation (A2) describes nutrient transport within the permeable tissue/gel substrate underneath the colony, and holds when the diffusion time scale is much smaller than the flow time scale within the low-aspect-ratio substrate. Upon non-dimensionalizing Equation (A2), the effective nutrient consumption rate S used in Table 1 in the main text is given by S=Γh*ϕeq/(c0d), where h* is the colony thickness scale, ϕeq is the active phase volume fraction scale and c0 is the initial nutrient concentration in the substrate.

Appendix 2—table 1
List of the symbols, descriptions, and numerical value for each of the parameters used. 
https://doi.org/10.7554/eLife.42697.013
VariableDescriptionNumerical value
Hvertical length scaleswarms - 0.5 μm biofilms - 400⁢ μm ⁢
Lhorizontal length scaleswarms - 100 ⁢μ⁢m biofilms - 1100⁢ μ⁢m
Uhorizontal velocity scaleswarms - 1.3 ⁢μ⁢m/s biofilms - 0.5 ⁢μ⁢m/s
ϕ0equilibrium volume fraction of active phaseswarms - 0.5
biofilms - 0.04
Q0vertical fluid velocity scaleswarms - 10–2 ⁢μ⁢m/s
biofilms - 0.04
g0effective swarm cell growth rate0.005 — 0.2 s-1
GEPS production rate1/40 min-1
μ1EPS matrix viscosity105 Pa.s
μ2fluid viscosity10–3 Pa.s
ζfriction coefficient per unit cell volume10–2 pN/(μm s-1)
γfluid surface tension10-2 N/m
Ψ0osmotic pressure scale2100 Pa
Dimensionless parameters: Bacterial swarm
α1γHζL3U0.2
α2Q0Hg00.1 — 4.3
Dimensionless parameters: Bacterial biofilm
β1Ψ0H2μ1UL6.7
β2γH3μ1UL30.01
β3γHζμ1L30.02
β4Q0HG1
β5SG2

Continuity

For a volume element within the microbial colony, the averaged conservation of mass (Ishii and Hibiki, 2011) for the active phase and the aqueous phase is expressed as,

(A3) ϕt+(ϕu1)=g1(h,ϕ,c),
(A4) (1ϕ)t+((1ϕ)u2)=0,

where ϕ is the volume fraction of the active phase (i.e. swarmer cells or biomass), u1=(u1,w1) is the averaged velocity of the active phase, u2=(u2,w2) is the averaged velocity of the fluid phase along the x (horizontal) and z (vertical) directions respectively. Integrating Equations (A3) and (A4) through the colony thickness leads to,

(A5) hϕt+0hx(ϕu1)+(ϕw1)|0h=hg1(h,ϕ,c),
(A6) h(1ϕ)t+0hx((1ϕ)u2)+((1ϕ)w2)|0h=0.

The boundary conditions in Equations (A5) and (A6) for the vertical velocities w1 and w2 are,

(A7) w1|z=0=0andw1|z=h=ht+u1hx,
(A8) w2|z=0=V0(x)andw2|z=h=ht+u2hx,

where the no-flux condition is applied to active phase at z=0. For the aqueous phase, at z=0, there is a spatial fluid influx described by V0(x) due to the osmotic pressure difference between the substrate and the microbial colony. Both phases at the upper interface at z=h obey the kinematic boundary condition. In Equations (A2), (A5) and (A6), the five unknown fields are h, ϕ, c, u1 and u2. The remaining two equations for closure are obtained from an averaged momentum balance.

Momentum balance

The averaged momentum balances for each phase can be expressed as (Ishii and Hibiki, 2011; Drew, 1983),

(A9) (ϕσ1)+M=0,
(A10) ((1ϕ)σ2)M=0,

where 𝝈𝟏 is the averaged stress tensor acting on a volume element of the swarm cell/EPS matrix phase, 𝝈𝟐 is the averaged stress tensor in a volume element of the fluid phase and 𝑴 denotes the total momentum transfer between the swarm cell/EPS matrix and the fluid phase. The interfacial momentum transfer term is expressed as (Ishii and Hibiki, 2011; Drew, 1983),

(A11) 𝑴=pfϕ-ζϕ(𝒖𝟏-𝒖𝟐).

The first term in Equation (A11) denotes the force pfϕ due to the averaged interfacial pressure (Drew, 1983; Ishii and Hibiki, 2011) on the cells/EPS matrix by the surrounding fluid. Note that the momentum transfer terms in Equations (A9)-(A10) balance each other as the averaged pressures at the two-phase interface are equal, leading to a vanishing net buoyancy. The second term corresponds to the net viscous stokes drag, where ζ is a friction coefficient. While Equations (A9–A11) are generally applicable in describing both swarms and films, particular expressions that describe 𝝈𝟏,𝝈𝟐 and ζ are unique to swarming and biofilm expansion and are discussed below for each case.

Bacterial swarms

Within microbial swarms, the fluid phase is modeled as a Newtonian liquid with viscosity μ2 whereas the swarmer cells are treated as inviscid with an isotropic stress equal to the surrounding fluid pressure pf. The averaged constitutive laws for the swarmer cell phase and the fluid phase are expressed as,

(A12) σ1=pfI,
(A13) σ2=pfI+μ2(u2+u2T).

Substituting Equations (A12) and (A13) in Equations (A9–A11), we obtain

(A14) ϕpfζϕ(u1u2)=0,
(A15) ((1ϕ)τ2)(1ϕ)pf+ζϕ(u1u2)=0,

where τ2=μ2(u2+u2T) is the deviatoric stress tensor. In bacterial swarms, the friction coefficient is ζ=ζc/Vc18μ2/a2 where a is the diameter of the cell, ζc3πμ2a is the friction coefficient of a single swarmer cell from Stokes’s law, Vc=πa3/6 is the cell volume.

Thin-film lubrication limit for swarms

We consider the limit of hR, where R is the colony radius. In the thin-film lubrication limit, combining Equations (A14) and (A15) results in the equation governing the mean horizontal fluid velocity to leading order,

(A16) μ2z((1-ϕ)u2z)=dpfdx,

where the fluid pressure is assumed to vary only in the horizontal direction, and is set by the local curvature of the swarm colony and the fluid surface tension as,

(A17) pf=p0-γ2hx2,

where p0 is a constant (atmospheric) pressure. Integrating Equation (A16) twice and using the boundary conditions u2(0)=0 and (du2/dz)z=h=0 lead to the expression for the averaged horizontal fluid phase velocity profile as,

(A18) u2=1(1-ϕ)μ2dpfdx(z22-hz).

The mean horizontal swarmer cell velocity is determined from the Darcy-type equation in Equation (A14) as,

(A19) u1=u2-1ζdpfdx.

Growth rate of bacterial swarms

We note that bacterial swarming is typically associated with nutrient rich environments, where the initial concentration level c0Γ, the specific nutrient consumption rate. Paradoxically, although the rate of bacterial cell division and volume expansion are exponential, the rate of swarm expansion is constant. Therefore, despite abundant nutrient availability, cell division must be halted at the swarm interior through non-nutrient meditated regulatory/signaling mechanisms, such that only a subpopulation of swarmer cells undergo cell division. In our study, we account for this effect by considering a simple logistic model for the growth term of the form,

(A20) g1(h,ϕ,c)=g0ϕ(1-hϕHϕ0),

where H is the swarm colony thickness at the interior, and g0 is an effective growth rate that accounts for spatial localization in cell division. More specifically, if L(x) describes the spatial profile of cell growth within a swarm colony, then g0=0RL(x)𝑑x/0Rϕ[1-(hϕ/(Hϕ0))]𝑑x, where R is the radius of the swarm colony. Measurements of the spatial distribution of cell growth rates within the colony during swarming remain lacking. Consequently, in our model, we determine the value of g0 by fitting it to the experimental data. We use a nonlinear least-squares solver to match steady-state expansion speeds obtained from solving Equations (A21)-(A22) to the experimental data for steady-state B. subtilis swarms (see Figure 1D in main text).

Swarm equations

Combining Equations (A5)-(A8) with Equations (A17)-(A19) results in the dimensional thickness averaged equations for swarm colonies,

(A21) t(hϕ)+γ3μ2x(h3ϕ1ϕh3x3)+γζx(hϕ3hx3),=g0hϕ(1hϕHϕ0)
(A22) t(h(1-ϕ))+γ3μ2x(h3h3x3)=(1-ϕ)V0(x).

The five boundary conditions are (h/x)x=0=(h/x)x=0, (3h/x3)x=0=(3h/x3)x=0, and ϕ(0)=ϕ0, where ϕ0 is the volume fraction of the swarmer cells at the interior where there is no flow. As discussed in the main text, we impose the far-field boundary condition by choosing a domain much larger than the colony size.

Bacterial films

In our model, the EPS matrix constitutes an active viscous hydrogel network with a polymer volume fraction ϕ(x,t), while the fluid phase is considered as a freely moving solvent of volume fraction 1-ϕ(x,t). The averaged constitutive laws for the EPS matrix phase and the fluid phase are expressed as,

(A23) σ1=(Ψ+pf)I+μ1(u1+u1T),
(A24) σ2=pfI+μ2(u2+u2T),

where μ1 is the viscosity of the EPS matrix, μ2 is the aqueous phase viscosity, Ψ represents an effective swelling pressure in the biofilm EPS hydrogel and pf is the fluid pressure. Substituting Equations (A23) and (A24) in Equations (A9–A11), we obtain

(A25) (ϕτ1)(ϕΨ)ϕpfζϕ(u1u2)=0
(A26) ((1ϕ)τ2)(1ϕ)pf+ζϕ(u1u2)=0,

where τ1=μ1(u1+u1T) and τ2=μ2(u2+u2T) are the deviatoric stresses in the EPS phase and aqueous phase, respectively. In our model, we assume that the contribution of stress from the viscosity of the aqueous phase is negligible compared to the frictional drag due to flow between water and EPS polymer chain network. Consequently, Equation (A26) reduces to a Darcy-type law of the form,

(A27) u1u2=1ϕζϕpf,

where κ(ϕ)=(1-ϕ)/ϕ is a volume fraction dependent permeability and the fricton coefficient is ζ=μ2/ξ2, provided that ξ50 nm is the polymer mesh network length scale. Substituting Equation (A27) in Equation (A25) results in,

(A28) (ϕτ1)=(ϕΨ+pf).

Thin-film lubrication limit for bacterial films

In the thin-film lubrication limit for biofilms when hR, Equation (A28) reduces to

(A29) μ1z(ϕu1z)=dΠdx,

where Π=ϕΨ+pf is treated as an effective EPS phase pressure, as it is dΠ/dx that drives the viscous EPS flow. In Equation (A29), the effective pressure is assumed to vary only in the horizontal direction, and the fluid pressure is once again set by the curvature and fluid surface tension according to Equation (A17). Moreover, the swelling pressure is expressed as Ψ=ψ(ϕ)/ϕ, where ψ(ϕ)=ψ0×ϕ3 is the osmotic pressure of the biofilm EPS matrix using the Flory-Huggins model for a polymer network in a θ-solvent (Cogan and Guy, 2010; Winstanley et al., 2011). The osmotic pressure scale is ψ0=kT/b3, where b0.5 nm is the approximate size of the monomer unit in the EPS matrix. Note that although we refer to Π as an effective pressure, in Equation (A23) the mechanical pressure acting on the EPS phase is instead ψ(ϕ)/ϕ+pf. Integrating Equation (A29) twice using the boundary conditions u1(0)=0 and (du1/dz)z=h=0 results in,

(A30) u1=1ϕμ1dΠdx(z22-hz).

The mean horizontal fluid velocity is determined from the Darcy-type equation in Equation (A27) as,

(A31) u2=u1-1-ϕζϕdpfdx.

Growth rate of bacterial films

In biofilms, the growth term g1(h,ϕ,c) is expressed by the modified logistic term,

(A32) g1(h,ϕ,c)=GϕcK+c(1hϕHϕ0),

where G is the specific EPS production rate, H is the maximum overall biofilm colony thickness and ϕ0 is the volume fraction of the EPS matrix in the fully swollen state. Note that, in contrast to microbial swarming, the growth rate term in expanding biofilms is strongly coupled to the local nutrient concentration field via a Monod-type term in Equation (A32).

Biofilm equations

Combining Equations (A5)-(A8) with Equations (A30)-(A31) results in the dimensional thin-film governing equations for biofilm colonies,

(A33) t(hϕ)-13μ1x(h3Πx)=GhϕcK+c(1-hϕHϕ0),
(A34) t(h(1ϕ))13μ1x(h31ϕϕΠx)+γζx(h(1ϕ)2ϕ3hx3)=(1ϕ)V0(x),
(A35) ct-D2cx2=-ΓhϕdcK+c.

The eight boundary conditions are the symmetry conditions (h/x)x=0=(h/x)x=R=0, (h3/x3)x=0=(3h/x3)x=R=0, (ϕ/x)x=0=(ϕ/x)x=R=0, and (c/x)x=0=(c/x)x=R=0 where R is now the size of the petri dish that is infused with nutrients.

Numerical computation

Numerical solutions to Equations (A21)-(A22) and Equations (A33)-(A35) were implemented using the COMSOL 5.0 finite element package and have been provided as source code files. We use a fixed 1D domain of size x[0,L1] where L1 = 16 and 150 for the biofilm and swarm simulations respectively. We use quintic Lagrange basis functions with element sizes below 0.04 and use the general form PDE solver. To handle the moving contact line, we introduce a precursor film of thickness hp/H = 0.0125, where H is the vertical length scale (see Appendix 3—figure 1). We follow the regularization described in Trinschek et al. (2016) to introduce a minimum threshold for growth and a stable fixed point in the precursor film. Specifically, the growth terms in (A2), (A20) and (A32) are multiplied by a factor F=[1-exp(5(h^p-h^ϕ^))]×(1-h^p/(h^ϕ^)), where F1 everywhere except near the precursor film where F=0.

Experimental data

Appendix 2—table 2
Summary of the comparision between the experimental data and model.

The experimentally measured quantities are the colony expansion speed V=dR/dt and multilayer region thickness W. The value of α2 is determined by fitting Equations (7)-(8) to the expansion velocity, leading to estimates of the effective growth rate g0, the horizontal length scale L and the capillary number Ca

https://doi.org/10.7554/eLife.42697.014
NoReferenceV=dRdt
μms-1
(exp)
W
μm
(exp)
α2

(model)
g0
s-1
(model)
L
μm
(model)
Ca
× 10-7
(model)
1B. subtilis this work - (data set I)0.561781.490.013981.32
2B. subtilis this work - (data set II)0.263684.330.0051280.59
3B. subtilis this work - (data set III)0.601321.350.015961.42
4E. coli Wu and Berg, 20121.71540.300.07664.38
5E. coli Darnton et al., 20103.8-
https://doi.org/10.7554/eLife.42697.012

Appendix 3

Figures

Appendix 3—figure 1
Precursor film.

(A) Numerical solution to Equations (5)-(6) in the main text that represents the swarm profile h^(x^). The solid black line indicates the swarm profile in the interior region domain x^[0,x1]. The dashed line beyond x1 denotes the precursor-film region. The solid red line represents a linear extrapolation of the swarm profile in the region x^[x1,R^], where R^ is the swarm radius. Inset: Magnified view of the transition region, where hp is the precursor film of thickness. (B) The onset of the precursor film is defined at the point of the numerical profile where the slope is a minimum.

https://doi.org/10.7554/eLife.42697.016
Appendix 3—figure 2
Steady-state swarm solutions.

(A) The evolution of the numerical swarm thickness h^(x^,t^) plotted in the laboratory frame at fixed time intervals. (B) Plot of the swarm radius as a function of time indicating steady-state solutions.

https://doi.org/10.7554/eLife.42697.017
Appendix 3—figure 3
Top: Image of edge of swarm colony.

B. subtilis swarm colony showing the interior monolayer region, the multi-layer front, and the monolayer at the very edge of the colony. Bottom: Horizontal flows in swarms. Steady-state profile of the dimensionless net horizontal velocity J^=0h^u^dz^ in the swarmer cell phase (red) and fluid phase (blue). Expression for J^1 and J^2 are obtained from Equations (A18) and (A19) as J1=-11-ϕdp^dx^h^33-α1dp^dx^h^ and J2=11ϕdp^dx^h^33, where α1=0.2 as discussed in the main text. The step function represents experimental flow speeds as a function of the distance form the swarm edge as measured by Wu and Berg (2012), and where the horizontal distance has been scaled by L=15μm.

https://doi.org/10.7554/eLife.42697.018
Appendix 3—figure 4
Early and late flows in biofilms.

Profile of the dimensionless net horizontal velocity J^=0h^u^dz^ in the EPS phase (red) and fluid phase (blue). Expression for J^1 and J^2 are obtained from Equations (A30) and (A31) as J1=β1ϕdΠ^dx^h^33 and J2=-β1ϕdΠ^dx^h^33-β31-ϕϕdp^dx^h^, where β1=6.7 and β3=0.02 as discussed in the main text.

https://doi.org/10.7554/eLife.42697.019
Appendix 3—figure 5
Variation of the experimentally measured multilayer swarm width W with Ca.

For B. subtilis swarm experiments, the value of W was determined by considering the width of the region where the mean constitutive fluorophore intensity I>0.7max(I) (See Figure 1E). The multilater width in the E. coli swarm was reported as 154 μm ± 27 μm by Wu and Berg (2012). The dashed line corresponds to the predicted scaling from Equation (6).

https://doi.org/10.7554/eLife.42697.020
https://doi.org/10.7554/eLife.42697.015

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Decision letter

  1. Raymond E Goldstein
    Reviewing Editor; University of Cambridge, United Kingdom
  2. Arup K Chakraborty
    Senior Editor; Massachusetts Institute of Technology, United States
  3. Alexandre Persat
    Reviewer; EPFL, Switzerland

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Dynamics of spreading microbial swarms and films" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Arup Chakraborty as the Senior Editor. The following individual involved in review of your submission has agreed to reveal his identity: Alex Persat (Reviewer #1).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

Srinivasan and coauthors propose a generalized model to describe the expansion of bacterial communities growing on agar plates in two distinct modes: swarms and biofilms. They demonstrate that the interplay between biological mechanisms, such as osmolyte secretion, extracellular matrix production and spatial localization in cell growth, and physics of the microbial environment, such as hydrodynamics, mechanics, osmotic flux and nutrient diffusion, controls the expansion dynamics of microbial swarms and biofilms. They show that swarming colonies undergo steady state expansion, while biofilms expand transiently, going from an initial acceleration phase to a decelerating one. These observations are in agreement with a few experiments. In the end, they prove that despite the differences in biological mechanisms of films and swarms, a unified two-phase model of a spreading colony can describe both systems.

The reviewers found the paper well-written and the theoretical and numerical analysis was performed very carefully. The phase-field theory developed in the paper goes substantially beyond earlier models as recently reviewed by Allen and Waclaw, 2018. The detailed parameter list in the Appendix can become a valuable reference for future studies. The manuscript thus offers a significant theoretical advance.

Title:

The reviewers thought that the current title sounds like an area of research rather than the title of a paper. Perhaps adding the term "model" or "theory" or any other more specific/descriptive statements would more clearly reflect the content of the paper.

Essential revisions:

The reviewers found that the manuscript lacks a bit of clarity with respect to conclusions and agreement with experimental data:

1) There is a lack of comparison between scaling and experiments. I think the beauty of this work is to highlight scaling of the front velocity and width of the front. However, the authors limit their validation as fitting a single data point to obtain prefactors (e.g. subsection “Steady state swarms”, last paragraph). I would expect that the authors would make an effort to measure velocities for a few capillary numbers for example, and verify the -1/3 exponent behavior. It looks like the authors can actually do these experiments since they already have some data shown in Figure 1. This would absolutely strengthen the paper and the model (independently of whether the model fits the data).

2) The aim of the model is to describe both swarming and biofilm expansion and weigh in on their physical differences. However, describing swarming and biofilm separately makes it difficult to have a direct comparison between the two. It would be useful to directly compare the two (in concomitance with Table 1), explaining, for example, the choices of different active-passive phases, osmotic flux, growth term and nutrient uptake. For example, it would be useful to know why in swarmer cells nutrient uptake is not considered (is it related to nutrient diffusion in different agar substrate?). Why isn't surface tension considered in the biofilm model? This lack of discussion weakens the argument of a unifying model.

3) In biofilms, it is not clear to me why the active phase consists of only the EPS and cell growth is not considered as a driver of expansion.

4) I would imagine that one important factor limiting growth particularly in the biofilm case is the friction between the colony and the agar substrate. This is pointed out in Farell, 2013, but it seems to be vastly overlooked by the authors. Can they incorporate this in their fluidic model?

5) They state that their model reveals that bacterial swarm colonization corresponds to a fluid regulated limit, whereas the dynamics of biofilm expansion is governed by nutrient transport (subsection “Conclusions”). However, these are actually the assumptions used to describe the system, so it is not so surprising. Can the authors comment on this point?

One reviewer identified himself as "eviewer A" of the previous submission to PNAS for this manuscript (i.e. the positive reviewer), and stated that the authors had responded to his comments in their "response to reviewers" document, which they attached to the eLife submission.

This reviewer did note the following:

6) It is still slightly worrisome that 2 out of the 4 figures are introductory/motivational, as pointed out in my major comment 1 of the PNAS report.

7) Regarding my previous comment 2, about limitations of the model. I appreciate that the authors have added the sentence about shortcomings of the model after I pointed out one issue. Given that eLife does not have any length restrictions, I think it would be appropriate to add a new section to the main text where general limitations of the model are discussed, and where the authors believe the model is failing. I think such a section would strengthen the manuscript, provide additional context, and it would be helpful to the readers.

https://doi.org/10.7554/eLife.42697.022

Author response

As per the reviewers’ recommendation, we have changed the title to “A multiphase theory for spreading microbial swarms and films”.

Essential revisions:

1) There is a lack of comparison between scaling and experiments. I think the beauty of this work is to highlight scaling of the front velocity and width of the front. However, the authors limit their validation as fitting a single data point to obtain prefactors (e.g. subsection “Steady state swarms”, last paragraph). I would expect that the authors would make an effort to measure velocities for a few capillary numbers for example, and verify the -1/3 exponent behavior. It looks like the authors can actually do these experiments since they already have some data shown in Figure 1. This would absolutely strengthen the paper and the model (independently of whether the model fits the data).

We thank the reviewers for their comment. Following the reviewers’ suggestion, we have now included additional comparisons with experiments that verify some of the predictions of our model. Specifically, we now fit our model and compare the scaling behavior using five independent experimental measurements of swarm expansion velocities. These include two new experimental measurements of swarm expansion in B. subtilis, and two existing measurements in E. coli previously reported by Darton et al., 2010, and Wu and Berg, 2012. The comparison between the various experiments and fitting to our model is described in the main text in Appendix 2—table 2 and Figure 3D.

As seen in the figure for the velocity scaling, the measured swarm expansion velocities (dR/dt) indeed follows a -1/3 exponent with the Capillary number. We have introduced a new figure in the main text (i.e., Figure 3D) to emphasize the verification of the -1/3 scaling. We have rewritten the introduction of the time and length scales, that clarifies the estimation of the various parameters.

“There are two key dimensionless parameters that describes swarm colony expansion. […] A complete set of parameters for three experimental measurements of swarm expansion in B. subtilis, and two existing measurements in E. coli previously reported by Darton et al., 2010, and Wu and Berg, 2012, are summarized in Appendix [3].”

Finally, upon considering the new dataset, the width of the swarm front does not follow the scaling we had originally proposed, as seen in Appendix 3—figure A5.

For each experiment, we fit our model using the effective growth rate g0 as the fitting parameter. We have also included a discussion of this fitting, and the overall comparison between model and theory with the new experimental measurements:

“Furthermore, we corroborate our scaling law in Equation 5 by fitting our model to five independent experimental measurements of swarm expansion velocities, as shown in Figure 3D. [...] From an experimental point of view, the width of the multilayer region is not sharply defined in Figure 1E, and will depend on the choice of threshold.”

As the reviewer indicates, we think these results strengthen the paper and our model, especially the agreement of the scaling law with the experimental velocities. We are once again grateful and thank the reviewer for their valuable insight towards improving the manuscript.

2) The aim of the model is to describe both swarming and biofilm expansion and weigh in on their physical differences. However, describing swarming and biofilm separately makes it difficult to have a direct comparison between the two. It would be useful to directly compare the two (in concomitance with Table 1), explaining, for example, the choices of different active-passive phases, osmotic flux, growth term and nutrient uptake. For example, it would be useful to know why in swarmer cells nutrient uptake is not considered (is it related to nutrient diffusion in different agar substrate?). Why isn't surface tension considered in the biofilm model? This lack of discussion weakens the argument of a unifying model.

We thank the reviewers for their comment. As per the reviewers’ suggestion, we have now added a number of paragraphs (immediately after Table 1), that directly compares the various terms in our unifying model across swarms and biofilms. We have now described the origin of the differences in the nutrient uptake, growth term and the active and passive fluxes in parallel with Table 1.

Our Discussion directly addresses some of the questions the reviewers raise, viz.

1) The overall rate of change of nutrient concentration in the substrate depends on nutrient diffusion and uptake as described by Appendix 2—equation A2, i.e.

∂c∂t-D∂2c∂x2=-ΓhϕdcK+c

where, c(x,t) is the mean depth-averaged nutrient concentration in the substrate, Γis the specific nutrient consumption rate per unit concentration, k is the half velocity constant at which the specific growth rate is one half the maximum value and d is the substrate thickness. When the substrate concentration is scaled by the initial concentrationc0, the depletion term on the RHS of Equation 18 depends on Γ/c0, the ratio of the specific nutrient consumption rate to the initial concentration.

Bacterial swarming is typically associated with nutrient rich conditions, wherec0≫Γ. As a result, the nutrient uptake term can be neglected in bacterial swarming asg2→0, and the concentration c≈c0 throughout swarm expansion. In contrast, biofilm growth occurs under nutrient limited conditions whereΓc0∼O1, resulting in a corresponding uptake term as shown in Table 1.

2) Surface tension is indeed included in the biofilm model, and sets the capillary fluid pressure aspf∼-γ∂2h/∂x2. Furthermore, as shown in Appendix 2—equations A28 and A2944-45, the gradient of the effective EPS phase pressure Π drives the horizontal fluxes Q1(x) and Q2(x) of the EPS matrix and fluid phase in Table 1. Note that Π is a sum of the Flory-Huggins osmotic pressure and the capillary fluid pressure. In this manner, surface tension is indeed considered in the biofilm model.

3) In biofilms, it is not clear to me why the active phase consists of only the EPS and cell growth is not considered as a driver of expansion.

Our model considers the growth of mature, macroscopic biofilm colonies. In these colonies, the biomass almost entirely consists of the EPS matrix. For example, as stated in Flemming and Wingender, 2010.

“In most biofilms, the microorganisms account for less than 10% of the dry mass, whereas the matrix can account for over 90%. The matrix is the extracellular material, mostly produced by the organisms themselves, in which the biofilm cells are embedded.”

Moreover, the total biomass itself constitutes only ~5% of the biofilm by mass (with the fluid being the predominant phase). Therefore, we treat the biofilm as a highly viscous and hydrated gel. Our model considers the osmotic stress and swelling pressure in freshly produced EPS matrix as the major contributor to biofilm expansion.

It only at the very early stages of biofilm colony growth (when EPS production is minimal) where cell-cell contact is the major contributor to growth pressure. An example of the latter scenario is microcolonies that consist of only ~100 – 1000s of cells. Models of microcolonies do indeed consider stress contributions from growth, for example, as in the work of Farrell, 2013.

4) I would imagine that one important factor limiting growth particularly in the biofilm case is the friction between the colony and the agar substrate. This is pointed out in Farell, 2013, but it seems to be vastly overlooked by the authors. Can they incorporate this in their fluidic model?

In our model, we consider the both the active EPS matrix phase, and the passive aqueous phase as Newtonian fluids. Consequently, friction between the colony and the agar substrate is accounted for in our model by the no-slip boundary condition, u1z=0=0 and u2z=0=0, where u1 and u2 are the horizontal components of the EPS phase and fluid phase velocities.

Therefore, our assumption of a no-slip boundary condition corresponds to the limit of an infinite friction coefficient in the discrete cell 2D model of Farrell, 2013. There are two key factors that differentiates our model from that of Farrell, 2013: (i) the presence of the EPS matrix, and (ii) the consideration of a third (i.e., vertical thickness) dimension.

Variations in the biofilm thickness allow the EPS matrix to sustain shear gradients in velocity. As a result, the biofilm colony “flows” over the agar substrate as a fluid in our model, while individual bacterial cells expand by “sliding” as a 2D dimensional colony by sliding in the model of Farrell et al. Similarly, in our model EPS production and growth in the third dimension generates the osmotic stresses and swelling pressure that drives colony expansion.

Note also that Farrell et al. consider the very early micro-colony stage of biofilms (< 2hrs), where cells are present as a monolayer and both EPS production and variations in the third dimension are absent.

One possibility towards incorporating the effect of frictional sliding in macroscopic biofilms would be the introduction of a Navier slip-length parameter in our model, where the slip-length is tuned to account for sliding of EPS matrix on the agar substrate. We have added this point to the Discussion, where we discuss limitations and scope of our model, by the following line:

“Similarly, during biofilm colony expansion, we do not account for sliding and frictional contact between the cells/EPS matrix and the substrate (Farrell, 2013).”

5) They state that their model reveals that bacterial swarm colonization corresponds to a fluid regulated limit, whereas the dynamics of biofilm expansion is governed by nutrient transport (subsection “Conclusions”). However, these are actually the assumptions used to describe the system, so it is not so surprising. Can the authors comment on this point?

We thank the reviewer for this observation. We agree, our model does not reveal this statement and is indeed an assumption used to describe the system. We have rephrased the sentence to emphasize that our model merely “rationalizes the assumption”, rather than reveals the statement.

One reviewer identified himself as "reviewer A" of the previous submission to PNAS for this manuscript (i.e. the positive reviewer), and stated that the authors had responded to his comments in their "response to reviewers" document, which they attached to the eLife submission.

This reviewer did note the following:

6) It is still slightly worrisome that 2 out of the 4 figures are introductory/motivational, as pointed out in my major comment 1 of the PNAS report.

We thank the reviewer for the comment. We have now included an additional subpanel in Figure 3D to show the scaling of the velocity with the capillary number. We also note that despite having 2 figures, each figure has a number of sub-panels that could easily be expanded as separate figures. In the interest of brevity and condensing the results of the data and model in one location for both swarms and biofilms, we have opted to use the subfigure approach, rather than expanding each panel into separate figures.

7) Regarding my previous comment 2, about limitations of the model. I appreciate that the authors have added the sentence about shortcomings of the model after I pointed out one issue. Given that eLife does not have any length restrictions, I think it would be appropriate to add a new section to the main text where general limitations of the model are discussed, and where the authors believe the model is failing. I think such a section would strengthen the manuscript, provide additional context, and it would be helpful to the readers.

As the reviewer has suggested, we have now added paragraphs under the conclusions section where we discuss the limitations of our model and scope for expansion:

“Analysis of collective microbial expansion in thin film geometries often prioritizes biological mechanisms, such as genetic regulation, developmental programs and cellular signaling/competition, over the role of the heterogeneous physical micro-environments. […] Finally, from an experimental and theoretical perspective, our results naturally raise the question of controlling biofilm and swarm expansion by manipulating water and nutrient availability, complementing the better studied approaches of manipulating colonies by the genetic regulation of EPS production, cell division, and chemical signaling in microbial colonies.”

https://doi.org/10.7554/eLife.42697.023

Article and author information

Author details

  1. Siddarth Srinivasan

    John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States
    Contribution
    Conceptualization, Formal analysis, Software, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
  2. C Nadir Kaplan

    1. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States
    2. Kavli Institute for Bionano Science and Technology, Harvard University, Cambridge, United States
    Contribution
    Software, Formal analysis, Validation, Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  3. L Mahadevan

    1. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, United States
    2. Kavli Institute for Bionano Science and Technology, Harvard University, Cambridge, United States
    3. Department of Physics, Harvard University, Cambridge, United States
    4. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    lmahadev@g.harvard.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5114-0519

Funding

National Science Foundation (DMR 1420570)

  • L Mahadevan

MacArthur Foundation

  • L Mahadevan

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank M Cabeen, R Losick for providing strains, S Rubinstein for access to the fluorescent imaging and microbial culture facility, and H Berg, L Ping and A Pahlavan for helpful discussions. This work was supported by the Harvard MRSEC DMR 1420570 and the MacArthur Foundation (LM).

Senior Editor

  1. Arup K Chakraborty, Massachusetts Institute of Technology, United States

Reviewing Editor

  1. Raymond E Goldstein, University of Cambridge, United Kingdom

Reviewer

  1. Alexandre Persat, EPFL, Switzerland

Publication history

  1. Received: October 9, 2018
  2. Accepted: March 14, 2019
  3. Version of Record published: April 30, 2019 (version 1)

Copyright

© 2019, Srinivasan et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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