Emergent pattern formation in an interstitial biofilm

Abstract

Collective behavior of bacterial colonies plays critical roles in adaptability, survivability, biofilm expansion and infection. We employ an individual-based model of an interstitial biofilm to study emergent pattern formation based on the assumptions that rod-shaped bacteria furrow through a viscous environment, and excrete extracellular polymeric substances which bias their rate of motion. Because the bacteria furrow through their environment, the substratum stiffness is a key control parameter behind the formation of distinct morphological patterns. By systematically varying this property (which we quantify with a stiffness coefficient γ), we show that subtle changes in the substratum stiffness can give rise to a stable state characterized by a high degree of local order and long-range pattern formation. The ordered state exhibits characteristics typically associated with bacterial fitness advantages, even though it is induced by changes in environmental conditions rather than changes in biological parameters. Our findings are applicable to broad range of biofilms and provide insights into the relationship between bacterial movement and their environment, and basic mechanisms behind self-organization of biophysical systems.

Network patterns in exponentially growing two-dimensional biofilms

“…Without EPS, pilus attachment probability P s is uniform throughout the simulation space. In this case (for fixed motility parameters as described in the model description in our earlier work [14]) colony morphology depends only on γ, k U , and P s . We tested the model's behavior for γ = 1.5, 1.0, 0.5, 0.25, k U = 0.05, 0.001, and P s = 0.5, 0.25.…”

“…The model used here is similar to one we reported previously [14], except that it includes cell growth and division. We parameterized our model using experimental data that quantify single-cell dynamics [9], and our own measurements of individual growth rates [ Fig.…”

“…1(e)] at a critical length l max = 7 w into two cells each with length l min = 3 w ± δl, where δl represents asymmetric division and is randomly selected at the time of division from the uniform interval δl ∈ [0, 1] w. Cell-cell collisions produce force and torque away from the contact point. It is also possible for cells to pull on one another with their pili because we model the cell motility mechanism explicitly [14] by simulating the extension, binding, and retraction of T4P [15]. The equations for translational and rotational motion of rods and our repulsive interaction scheme are the same as those used in [2,14,16].…”

“…It is also possible for cells to pull on one another with their pili because we model the cell motility mechanism explicitly [14] by simulating the extension, binding, and retraction of T4P [15]. The equations for translational and rotational motion of rods and our repulsive interaction scheme are the same as those used in [2,14,16]. During T4P-mediated surface motility, P. aeruginosa cells spontaneously reverse polarity, this process is believed to be essential to chemotaxis phenomena, but polarity reversals occur stochastically even in the absence of attractant/repellant gradients [17].…”

“…However, our model explicitly simulates the stochastic process of pilus binding and retraction [14], instead of making a mean-field approximation based on the assumption of a large number of T4P per cell. Based on available literature, such an assumption may not be valid for P. aeruginosa [9].…”