Bacteriophages (phages) play a critical role in controlling bacterial populations, both in nature and as potential therapeutic agents. Their ability to replicate, compete against each other, and eradicate target cell populations is usually understood through a number of 'life history parameters', traditionally measured by population-level assays, which implicitly average the parameter's value across a large number of infection events. Recent experiments suggest that bacteriophage life history parameters are subject to considerable stochasticity, raising the question of whether experimental and modelling efforts that do not account for this variability may overlook important factors in phage's behaviour, competitive fitness or therapeutic viability. Here, using agent-based simulations, we investigate the importance of stochasticity in lysis time and burst size of lytic bacteriophages in two common laboratory competition experiments: serial passage of well-mixed populations and plaque expansion across a bacterial lawn. We find that a phage's analytic growth rate in isolation can be a poor predictor of its fitness advantage in simulated competition experiments. Specifically, when lysis times are tightly distributed, we identify a novel effect we name "population resonance", through which a bacteriophage can display a significant fitness advantage over a competitor with a much greater growth rate in isolation. Our simulations also show that both serial passage and plaque expansion reward variability in lysis time more than expected, by increasing the phage resilience when resources are scarce.

In microbial populations, fitness, which is essential to understand and predict evolution, is often defined and measured as the net growth rate of a population in isolation. Applying the same definition to viruses is challenging, both because viral replication involves a host infection process, which is determined by several parameters that are context-dependent, and because viruses compete heavily for resources (susceptible cells). These challenges are particularly exacerbated in spatial range expansions, where multiplicity of infection is often high and resource availability varies in time and space. To assess different fitness definitions and their generalizability, we investigate a model of coupled partial differential equations for phage plaque expansion in one and two dimensions. We find that two commonly used metrics for phage fitness in plaque expansions, i.e., steady state phage densities and front expansion speed in isolation, are unable to reliably predict the winner in one- and two-dimensional direct competitions. More generally, we find that optimal phage traits depend on the dimensionality of the system and the make-up of the phage population, leading to unexpected behaviours, e.g., rock-paper-scissor dynamics and, in high dimensions, enhanced phage density due to the nearby presence of a competitor. We show that the phenomenon stems from the interplay between resource consumption and replication and thus may apply more broadly to any population competing for shared resources.

Biofilms are structured microbial communities, known for their electron transfer properties, which are essential for metabolic processes and microbial survival. Here, we investigated the electrogenic properties of Bacillus subtilis, a bacterial producer of electron-donating biofilms. Interdigitated gold electrodes were utilized to continuously measure the electrochemical activity of biofilm-forming B. subtilis cells and genetic mutants unable to create them (biofilm-deficient). The formation of extracellular polymeric substances (EPS) and filamentous appendages was monitored via scanning electron microscopy (SEM). Chronoamperometry was used to assess electrochemical activity, which showed fluctuations in electrical current at specific time points in biofilm-forming cells. Cyclic voltammetry (CV) revealed significant differences between the voltammograms of biofilm-forming and biofilm-deficient cells, hypothesized to be a result of the reduction of secreted flavodoxin. Electrochemical impedance spectroscopy (EIS) was also performed at various intervals and analyzed using an equivalent circuit. We identified the presence of a charge transfer resistance (Rct) exclusively in biofilm which correlated to the time of increased electrochemical activity measured using chronoamperometry. Finally, through confocal microscopy, we found that the expression of a gene involved in biofilm matrix formation, tasA, was correlated with the time when charge transfer was measured. These results indicate that electrochemical activity is primarily present in biofilm-forming cells rather than in biofilm-deficient mutants. By combining electrochemical and microscopic methods, we developed a methodology to continuously monitor the stages of biofilm formation and showed that electrochemical activities within biofilms vary over time and there is a temporal relationship between these processes and the expression of genes responsible for biofilm development.

Wrinkled patterns in biofilms arise from buckling instabilities triggered by stresses that accumulate as growth is constrained by a stationary substrate. While nutrient availability, friction, and adhesion each influence wrinkling, their combined effects remain poorly understood. Here, we address this gap using a lattice-network model of biofilm morphogenesis. Under constant nutrient supply, wrinkles initiate at the center, where stresses are highest and isotropic, regardless of the level of friction or adhesion. Stronger adhesion delays wrinkling and decouples the length scale governing the buckling instability from the overall biofilm size. Heterogeneous adhesion lowers the critical stress by triggering wrinkles in weakly adhered regions, with the effect modulated by friction and the average adhesion. Under nonuniform nutrient supply, our model predicts that wrinkle initiation shifts from the center to the edge as initial nutrient availability decreases, a transition we experimentally validate using E. coli biofilms.

Wrinkling is a striking emergent behavior that occurs in microbial biofilms across many species. The phenomenon originates from an intricate interplay between environmental factors, cell-to-cell phenotypic heterogeneity, and mechanical forces, thus requiring insights from multiple disciplines, from biology through chemistry to physics, to be fully understood. We critically review current knowledge about wrinkle formation in biofilms, starting with an analysis of the shared and distinct features that characterize this morphology across different species and the potential evolutionary advantages associated with it. Leveraging the vast literature on Bacillus subtilis, we then focus on its biofilms to discuss in detail the molecular mechanisms and regulation of wrinkle formation, along with the environmental factors that impact this phenotype. We follow by summarizing the insights gained from theoretical and modeling work on the mechanical origin of wrinkle formation, and the related experimental studies that have attempted to measure the material properties of different biofilms. We conclude with a synthesis of the many physical, chemical, and biological factors at play and a discussion of the remaining open questions around complex architectures in biofilms.

When a lytic bacteriophage infects a cell, it hijacks the cell’s resources to replicate itself, ultimately causing cell lysis and the release of new virions. As phages function as obligate parasites, each stage of the infection process depends on the physiological parameters of the host cell. Given the inherent variability of bacterial physiology, we ask how the phage infection dynamic reflects such heterogeneity.Here, we introduce a pioneering assay for investigating the kinetics of individual infection steps by a single T7 phage on a single bacterium. The high-throughput, time-resolved nature of the assay allows us to monitor the infection progression simultaneously in multiple cells, revealing substantial heterogeneity in each step and correlations between the infection dynamics and the infected cell’s properties. Simulations of competing phage populations with distinct lysis time distributions indicate that this heterogeneity can have considerable impact on phage fitness, recognizing variability in infection kinetics as a potential evolutionary driver of phage-bacteria interactions.### Competing Interest StatementThe authors have declared no competing interest.

Bacterial communities undergoing spatial expansions, such as colonies and biofilms, typically exhibit a severe loss of genetic diversity due to gene surfing, a phenomenon by which only cells close to the expanding edge have a chance of contributing to the next generation. As a result, mutants that emerge behind the front will remain trapped in the bulk of the population, even if they carry a significant growth advantage. While these findings are applicable to smooth, uniformly dense colonies, where cell spatial re-arrangement is limited, it is unclear whether they hold in bacterial communities that display complex three-dimensional morphologies, such as wrinkly biofilms.We employ an experimental procedure that enables a rapid switch of the chemical environment with minimal physical manipulation of the cell community to investigate the fate of trapped clones carrying a selective advantage in wrinkly B. subtilis biofilms. We find that these clones are able to use the channels underneath the wrinkles to traverse long distances and take over the expansion front. Moreover, we find that active motility is required to accomplish this task. Collectively, our findings reveal an intriguing novel role of wrinkles, as they enable potentially advantageous mutants to propel themselves towards the leading edge of the expansion and rescue the population upon environmental changes. Our results also point at motility, which has traditionally received little attention in bacterial communities growing on hard substrates, as a key mechanism for population rescue in biofilms that display complex morphologies.### Competing Interest StatementThe authors have declared no competing interest.

Crowding effects critically impact the self-organization of densely packed cellular assemblies, such as biofilms, solid tumors, and developing tissues. When cells grow and divide, they push each other apart, remodeling the structure and extent of the population’s range. Recent work has shown that crowding has a strong impact on the strength of natural selection. However, the impact of crowding on neutral processes, which controls the fate of new variants as long as they are rare, remains unclear. Here, we quantify the genetic diversity of expanding microbial colonies and uncover signatures of crowding in the site frequency spectrum. By combining Luria–Delbrück fluctuation tests, lineage tracing in a novel microfluidic incubator, cell-based simulations, and theoretical modeling, we find that the majority of mutations arise behind the expanding frontier, giving rise to clones that are mechanically “pushed out” of the growing region by the proliferating cells in front. These excluded-volume interactions result in a clone-size distribution that solely depends on where the mutation first arose relative to the front and is characterized by a simple power law for low-frequency clones. Our model predicts that the distribution depends on a single parameter—the characteristic growth layer thickness—and hence allows estimation of the mutation rate in a variety of crowded cellular populations. Combined with previous studies on high-frequency mutations, our finding provides a unified picture of the genetic diversity in expanding populations over the whole frequency range and suggests a practical method to assess growth dynamics by sequencing populations across spatial scales.

Spatial models where growth is limited to the population edge have been instrumental to understanding the population dynamics and the clone size distribution in growing cellular populations, such as microbial colonies and avascular tumours. A complete characterization of the coalescence process generated by spatial growth is still lacking, limiting our ability to apply classic population genetics inference to spatially growing populations. Here, we start filling this gap by investigating the statistical properties of the cell lineages generated by the two dimensional Eden model, leveraging their physical analogy with directed polymers. Our analysis provides quantitative estimates for population measurements that can easily be assessed via sequencing, such as the average number of segregating sites and the clone size distribution of a subsample of the population. Our results not only reveal remarkable features of the genealogies generated during growth, but also highlight new properties that can be misinterpreted as signs of selection if non-spatial models are inappropriately applied.

Viral superinfection occurs when multiple viral particles subsequently infect the same host. In nature, several viral species are found to have evolved diverse mechanisms to prevent superinfection (superinfection exclusion) but how this strategic choice impacts the fate of mutations in the viral population remains unclear. Using stochastic simulations, we find that genetic drift is suppressed when superinfection occurs, thus facilitating the fixation of beneficial mutations and the removal of deleterious ones. Interestingly, we also find that the competitive (dis)advantage associated with variations in life history parameters is not necessarily captured by the viral growth rate for either infection strategy. Putting these together, we then show that a mutant with superinfection exclusion will easily overtake a superinfecting population even if the latter has a much higher growth rate. Our findings suggest that while superinfection exclusion can negatively impact the long-term adaptation of a viral population, in the short-term it is ultimately a winning strategy.

Reaction-diffusion waves have long been used to describe the growth and spread of populations undergoing a spatial range expansion. Such waves are generally classed as either pulled, where the dynamics are driven by the very tip of the front and stochastic fluctuations are high, or pushed, where cooperation in growth or dispersal results in a bulk-driven wave in which fluctuations are suppressed. These concepts have been well studied experimentally in populations where the cooperation leads to a density-dependent growth rate. By contrast, relatively little is known about experimental populations that exhibit density-dependent dispersal. Using bacteriophage T7 as a test organism, we present novel experimental measurements that demonstrate that the diffusion of phage T7, in a lawn of host E. coli , is hindered by steric interactions with host bacteria cells. The coupling between host density, phage dispersal and cell lysis caused by viral infection results in an effective density-dependent diffusion coefficient akin to cooperative behavior. Using a system of reaction-diffusion equations, we show that this effect can result in a transition from a pulled to pushed expansion. Moreover, we find that a second, independent density-dependent effect on phage dispersal spontaneously emerges as a result of the viral incubation period, during which phage is trapped inside the host unable to disperse. Additional stochastic agent-based simulations reveal that lysis time dramatically affects the rate of diversity loss in viral expansions. Taken together, our results indicate both that bacteriophage can be used as a controllable laboratory population to investigate the impact of density-dependent dispersal on evolution, and that the genetic diversity and adaptability of expanding viral populations could be much greater than is currently assumed.

Increasingly, predicting and even controlling evolutionary processes is a sought after goal in fields ranging from agriculture, artificial intelligence, synthetic biology, oncology, and infectious diseases. However, our ability to predict evolution and plan such interventions in real populations is limited in part by our understanding of how spatial structure modulates evolutionary dynamics. Among current clinical assays applied to predict drug response in infectious diseases, for instance, many do not explicitly consider spatial structure and its influence on phenotypic heterogeneity, despite it being an inextricable characteristic of real populations. As spatially structured populations are subject to increased interference of beneficial mutants compared to their well-mixed counter-parts, among other effects, this population heterogeneity and structure may non-trivially impact drug response. In spatially-structured populations, the extent of this mutant interference is density dependent and thus varies with relative position within a meta-population in a manner modulated by mutant frequency, selection strength, migration speed, and habitat length, among other factors. In this study, we examine beneficial mutant fixation dynamics along the front of an asexual population expanding its range. We observe that multiple distinct evolutionary regimes of beneficial mutant origin-fixation dynamics are maintained at characteristic length scales along the front of the population expansion. Using an agent-based simulation of range expansion with mutation and selection in one dimension, we measure these length scales across a range of population sizes, selection strengths, and mutation rates. Furthermore, using simple scaling arguments to adapt theory from well-mixed populations, we find that the length scale at the tip of the front within which ‘local’ mutant fixation occurs in a successive mode decreases with increasing mutation rate, as well as population size in a manner predicted by our derived analytic expression. Finally, we discuss the relevance of our findings to real cellular populations, arguing that this conserved region of successive mutant fixation dynamics at the wave tip can be exploited by emerging evolutionary control strategies.

Water occupies typically 50% of a protein crystal and thus significantly contributes to the diffraction signal in crystallography experiments. Separating its contribution from that of the protein is, however, challenging because most water molecules are not localized and are thus difficult to assign to specific density peaks. The intricateness of the protein-water interface compounds this difficulty. This information has, therefore, not often been used to study biomolecular solvation. Here, we develop a methodology to surmount in part this difficulty. More specifically, we compare the solvent structure obtained from diffraction data for which experimental phasing is available to that obtained from constrained molecular dynamics (MD) simulations. The resulting spatial density maps show that commonly used MD water models are only partially successful at reproducing the structural features of biomolecular solvation. The radial distribution of water is captured with only slightly higher accuracy than its angular distribution, and only a fraction of the water molecules assigned with high reliability to the crystal structure is recovered. These differences are likely due to shortcomings of both the water models and the protein force fields. Despite these limitations, we manage to infer protonation states of some of the side chains utilizing MD-derived densities.

In spatially constrained growing populations, such as tumors or biofilms, cells at the edge of expanding front have significant advantages because of the high accessibility to nutrients and spaces. Therefore, mutations that happen at the front have a large probability to surf at the edge of the population. A previous population sequencing study has shown that this effect leaves characteristic features in the mutation frequency spectrum. However, sequencing experiments cannot capture the behavior of low-frequency variants. Here, we constructed a microfluidic device to track mutations which happened at the front of a population growth. Combining with agent-based simulations and theoretical studies, we found that low-frequency mutant clones have broadly distributed shapes and sizes, which result from the combination of growth and excluded volume interactions. These results could be relevant to the emergence of …

Many cellular populations are tightly packed, such as microbial colonies and biofilms, or tissues and tumours in multicellular organisms. The movement of one cell in these crowded assemblages requires motion of others, so that cell displacements are correlated over many cell diameters. Whenever movement is important for survival or growth, these correlated rearrangements could couple the evolutionary fate of different lineages. However, little is known about the interplay between mechanical forces and evolution in dense cellular populations. Here, by tracking slower-growing clones at the expanding edge of yeast colonies, we show that the collective motion of cells prevents costly mutations from being weeded out rapidly. Joint pushing by neighbouring cells generates correlated movements that suppress the differential displacements required for selection to act. This mechanical screening of fitness differences allows slower-growing mutants to leave more descendants than expected under non-mechanical models, thereby increasing their chance for evolutionary rescue. Our work suggests that, in crowded populations, cells cooperate with surrounding neighbours through inevitable mechanical interactions. This effect has to be considered when predicting evolutionary outcomes, such as the emergence of drug resistance or cancer evolution.

Water occupies typically 50% of a protein crystal, and thus significantly contributes to diffraction signals from crystallography experiments. Separating its contribution from that of the protein is challenging mainly because almost water molecules are mostly delocalized as a result of the probabilistic nature of solvation, and are thus are difficult to assign to specific density peaks. The intricate protein-water interface compounds this difficulty. Here, we compare the solvent structure obtained from diffraction data for which experimental phasing is available to that obtained from constrained molecular dynamics (MD) simulations. The resulting spatial density maps show that commonly used MD water models are only partially successful at capturing biomolecular solvation. In general, their radial distribution is captured with only slightly higher accuracy than their angular distribution, and only a fraction of the water molecules assigned with high reliability to the crystal structure are recovered. These differences are likely due to shortcomings of both the water models and the protein force fields. Despite this difficulty, we nevertheless attempt to infer protonation states of side chains