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Biofouling of marine surfaces such as ship hulls is a major industrial problem. Antifouling (AF) paints delay the onset of biofouling by releasing biocidal chemicals. We present a computational model for microbial colonization of a biocide-releasing AF surface. Our model accounts for random arrival from the ocean of microorganisms with different biocide resistance levels, biocide-dependent proliferation or killing, and a transition to a biofilm state. Our computer simulations support a picture in which biocide-resistant microorganisms initially form a loosely attached layer that eventually transitions to a growing biofilm. Once the growing biofilm is established, immigrating microorganisms are shielded from the biocide, allowing more biocide-susceptible strains to proliferate. In our model, colonization of the AF surface is highly stochastic. The waiting time before the biofilm establishes is exponentially distributed, suggesting a Poisson process. The waiting time depends exponentially on both the concentration of biocide at the surface and the rate of arrival of resistant microorganisms from the ocean. Taken together our results suggest that biofouling of AF surfaces may be intrinsically stochastic and hence unpredictable, but immigration of more biocide-resistant species, as well as the biological transition to biofilm physiology, may be important factors controlling the time to biofilm establishment. [ABSTRACT FROM AUTHOR]

Microbial biofilms show high phenotypic and genetic diversity, yet the mechanisms underlying diversity generation andmaintenance remain unclear. Here, we investigate how spatial patterns of growth activity within a biofilm lead to spatial patterns of genetic diversity. Using individual-based computer simulations, we show that the active layer of growing cells at the biofilm interface controls the distribution of lineages within the biofilm, and therefore the patterns of standing and de novo diversity. Comparing biofilms of equal size, those with a thick active layer retain more standing diversity, while de novo diversity is more evenly distributed within the biofilm. In contrast, equal-sized biofilms with a thin active layer retain less standing diversity, and their de novo diversity is concentrated at the top of the biofilm, and in fewer lineages. In the context of antimicrobial resistance, biofilms with a thin active layer may be more prone to generate lineages with multiple resistance mutations, and to seed new resistant biofilms via sloughing of resistant cells from the upper layers. Our study reveals fundamental "baseline" mechanisms underlying the patterning of diversity within biofilms. [ABSTRACT FROM AUTHOR]

Laboratory assays such as MIC tests assume that antibiotic molecules are stable in the chosen growth medium—but rapid degradation has been observed for antibiotics including β-lactams under some conditions in aqueous solution. Degradation rates in bacterial growth medium are less well known. Here, we develop a 'delay time bioassay' that provides a simple way to estimate antibiotic stability in bacterial growth media, using only a plate reader and without the need to measure the antibiotic concentration directly. We use the bioassay to measure degradation half-lives of the β-lactam antibiotics mecillinam, aztreonam and cefotaxime in widely-used bacterial growth media based on MOPS and Luria-Bertani (LB) broth. We find that mecillinam degradation can occur rapidly, with a half-life as short as 2 hours in MOPS medium at 37°C and pH 7.4, and 4-5 hours in LB, but that adjusting the pH and temperature can increase its stability to a half-life around 6 hours without excessively perturbing growth. Aztreonam and cefotaxime were found to have half-lives longer than 6 hours in MOPS medium at 37°C and pH 7.4, but still shorter than the timescale of a typical minimum inhibitory concentration (MIC) assay. Taken together, our results suggest that care is needed in interpreting MIC tests and other laboratory growth assays for β-lactam antibiotics, since there may be significant degradation of the antibiotic during the assay. [ABSTRACT FROM AUTHOR]

Urinary catheters are used extensively throughout healthcare for various reasons including management of urinary tract dysfunction. The purpose of this study was to simultaneously explore both catheter user experience and staff perception of catheter services within community urinary catheter care. A questionnaire was conducted to investigate the views of community nursing staff. During the same time period, patients were interviewed about (a) catheter‐care standards and adherence to guidelines, (b) patients' feelings towards their catheter and (c) potential improvements to catheter practices and design. Sixty‐nine staff were surveyed. Although 97% of staff indicated they used local guidelines, in up to 62% of cases findings suggested practices in sending urine samples for culture did not comply with guidelines. Then, 75% of staff were satisfied with catheter care, but weaknesses were identified in handover processes, communication between staff and patients, and excessive documentation. Staff results were compared with the findings from interviews of 29 long‐term urinary catheter users, demonstrating a higher level of satisfaction with catheter care among patients (86%). Patients and staff agreed that generally the impacts of their catheter on personal hygiene, sense of independence, sense of dignity and of patient happiness, were neutral (neither positive nor negative). However, regarding improvements to catheter practices and catheter design, 73% of staff but only 45% of patients suggested improvements in service, while 76% of patients but only 49% of staff suggested improvement in design. The study reveals general satisfaction with community catheter care, but indicates areas of potential improvements regarding communication, documentation and catheter design. When compared to patient responses, staff overall had a less positive view of patients perception of their relationship with their catheter. [ABSTRACT FROM AUTHOR]

Phenotypic delay—the time delay between genetic mutation and expression of the corresponding phenotype—is generally neglected in evolutionary models, yet recent work suggests that it may be more common than previously assumed. Here, we use computer simulations and theory to investigate the significance of phenotypic delay for the evolution of bacterial resistance to antibiotics. We consider three mechanisms which could potentially cause phenotypic delay: effective polyploidy, dilution of antibiotic-sensitive molecules and accumulation of resistance-enhancing molecules. We find that the accumulation of resistant molecules is relevant only within a narrow parameter range, but both the dilution of sensitive molecules and effective polyploidy can cause phenotypic delay over a wide range of parameters. We further investigate whether these mechanisms could affect population survival under drug treatment and thereby explain observed discrepancies in mutation rates estimated by Luria-Delbrück fluctuation tests. While the effective polyploidy mechanism does not affect population survival, the dilution of sensitive molecules leads both to decreased probability of survival under drug treatment and underestimation of mutation rates in fluctuation tests. The dilution mechanism also changes the shape of the Luria-Delbrück distribution of mutant numbers, and we show that this modified distribution provides an improved fit to previously published experimental data. Author summary: Understanding precisely how some bacteria survive exposure to antibiotics is a major research focus. Specific mutations in the bacterial genome are known to provide protection. However, it remains unclear how much time passes between a bacterium acquiring the genetic change and being able to tolerate antibiotics—termed the phenotypic delay—and what controls this delay. Here, using computer simulations and mathematical arguments we discuss three biologically plausible mechanisms of phenotypic delay. We investigate how each mechanism would affect the outcome of laboratory experiments often used to study the evolution of antibiotic resistance, and we highlight how the delay might be detected in such experiments. We also show that the existence of the delay could explain an observed discrepancy in the measurement of mutation rates, and demonstrate that one of our models provides a superior fit to experimental data. Our work exposes how molecular details at the intracellular level can have a direct effect on evolution at the population level. [ABSTRACT FROM AUTHOR]

Osmosis is one of the most important physical phenomena in living and soft matter systems. While the thermodynamics of osmosis is well understood, the underlying microscopic dynamical mechanisms remain the subject of discussion. Unravelling these mechanisms is a prerequisite for understanding osmosis in non-equilibrium systems. Here, we investigate the microscopic basis of osmosis, in a system at equilibrium, using molecular dynamics simulations of a minimal model in which repulsive solute and solvent particles differ only in their interactions with an external potential. For this system, we can derive a simple virial-like relation for the osmotic pressure. Our simulations support an intuitive picture in which the solvent concentration gradient, at osmotic equilibrium, arises from the balance between an outward force, caused by the increased total density in the solution, and an inward diffusive flux caused by the decreased solvent density in the solution. While more complex effects may occur in other osmotic systems, our results suggest that they are not required for a minimal picture of the dynamic mechanisms underlying osmosis. [ABSTRACT FROM AUTHOR]

Parameter sensitivity analysis is a powerful tool in the building and analysis of biochemical network models. For stochastic simulations, parameter sensitivity analysis can be computationally expensive, requiring multiple simulations for perturbed values of the parameters. Here, we use trajectory reweighting to derive a method for computing sensitivity coefficients in stochastic simulations without explicitly perturbing the parameter values, avoiding the need for repeated simulations. The method allows the simultaneous computation of multiple sensitivity coefficients. Our approach recovers results originally obtained by application of the Girsanov measure transform in the general theory of stochastic processes [A. Plyasunov and A. P. Arkin, J. Comput. Phys. 221, 724 (2007)]. We build on these results to show how the method can be used to compute steady-state sensitivity coefficients from a single simulation run, and we present various efficiency improvements. For models of biochemical signaling networks, the method has a particularly simple implementation. We demonstrate its application to a signaling network showing stochastic focussing and to a bistable genetic switch, and present exact results for models with linear propensity functions. [ABSTRACT FROM AUTHOR]

We compute rates and pathways for nucleation in a sheared two-dimensional Ising model with Metropolis spin flip dynamics using forward flux sampling (FFS). We find a peak in the nucleation rate at intermediate shear rate. We analyze the origin of this peak using modified shear algorithms and committor analysis. We find that the peak arises from an interplay between three shear-mediated effects: Shear-enhanced cluster growth, cluster coalescence, and cluster breakup. Our results show that complex nucleation behavior can be found even in a simple driven model system. This work also demonstrates the use of FFS for simulating rare events, including nucleation, in nonequilibrium systems. [ABSTRACT FROM AUTHOR]

In many stochastic simulations of biochemical reaction networks, it is desirable to “coarse grain” the reaction set, removing fast reactions while retaining the correct system dynamics. Various coarse-graining methods have been proposed, but it remains unclear which methods are reliable and which reactions can safely be eliminated. We address these issues for a model gene regulatory network that is particularly sensitive to dynamical fluctuations: a bistable genetic switch. We remove protein-DNA and/or protein-protein association-dissociation reactions from the reaction set using various coarse-graining strategies. We determine the effects on the steady-state probability distribution function and on the rate of fluctuation-driven switch flipping transitions. We find that protein-protein interactions may be safely eliminated from the reaction set, but protein-DNA interactions may not. We also find that it is important to use the chemical master equation rather than macroscopic rate equations to compute effective propensity functions for the coarse-grained reactions. [ABSTRACT FROM AUTHOR]

We present a method for computing stationary distributions for activated processes in equilibrium and nonequilibrium systems using forward flux sampling. In this method, the stationary distributions are obtained directly from the rate constant calculations for the forward and backward reactions; there is no need to perform separate calculations for the stationary distribution and the rate constant. We apply the method to the nonequilibrium rare event problem proposed by Maier and Stein, to nucleation in a 2-dimensional Ising system, and to the flipping of a genetic switch. [ABSTRACT FROM AUTHOR]

We analyze the efficiency of several simulation methods which we have recently proposed for calculating rate constants for rare events in stochastic dynamical systems in or out of equilibrium. We derive analytical expressions for the computational cost of using these methods and for the statistical error in the final estimate of the rate constant for a given computational cost. These expressions can be used to determine which method to use for a given problem, to optimize the choice of parameters, and to evaluate the significance of the results obtained. We apply the expressions to the two-dimensional nonequilibrium rare event problem proposed by Maier and Stein [Phys. Rev. E 48, 931 (1993)]. For this problem, our analysis gives accurate quantitative predictions for the computational efficiency of the three methods. [ABSTRACT FROM AUTHOR]

We present three algorithms for calculating rate constants and sampling transition paths for rare events in simulations with stochastic dynamics. The methods do not require a priori knowledge of the phase-space density and are suitable for equilibrium or nonequilibrium systems in stationary state. All the methods use a series of interfaces in phase space, between the initial and final states, to generate transition paths as chains of connected partial paths, in a ratchetlike manner. No assumptions are made about the distribution of paths at the interfaces. The three methods differ in the way that the transition path ensemble is generated. We apply the algorithms to kinetic Monte Carlo simulations of a genetic switch and to Langevin dynamics simulations of intermittently driven polymer translocation through a pore. We find that the three methods are all of comparable efficiency, and that all the methods are much more efficient than brute-force simulation. [ABSTRACT FROM AUTHOR]

Although regime shifts are known from various ecosystems, the involvement of microbial communities is poorly understood. Here we show that gradual environmental changes induced by, for example, eutrophication or global warming can induce major oxic-anoxic regime shifts. We first investigate a mathematical model describing interactions between microbial communities and biogeochemical oxidation-reduction reactions. In response to gradual changes in oxygen influx, this model abruptly transitions between an oxic state dominated by cyanobacteria and an anoxic state with sulfate-reducing bacteria and phototrophic sulfur bacteria. The model predictions are consistent with observations from a seasonally stratified lake, which shows hysteresis in the transition between oxic and anoxic states with similar changes in microbial community composition. Our results suggest that hysteresis loops and tipping points are a common feature of oxic-anoxic transitions, causing rapid drops in oxygen levels that are not easily reversed, at scales ranging from small ponds to global oceanic anoxic events. [ABSTRACT FROM AUTHOR]

We studied in detail the reproducibility of community development in replicate nutrient-cycling microbial microcosms that were set up identically and allowed to develop under the same environmental conditions. Multiple replicate closed microcosms were constructed using pond sediment and water, enriched with cellulose and sulphate, and allowed to develop over several months under constant environmental conditions, after which their microbial communities were characterized using 16S rRNA gene sequencing. Our results show that initially similar microbial communities can follow alternative - yet stable - trajectories, diverging in time in a system size-dependent manner. The divergence between replicate communities increased in time and decreased with larger system size. In particular, notable differences emerged in the heterotrophic degrader communities in our microcosms; one group of steady state communities was enriched with Firmicutes, while the other was enriched with Bacteroidetes. The communities dominated by these two phyla also contained distinct populations of sulphate-reducing bacteria. This biomodality in community composition appeared to arise during recovery from a low-diversity state that followed initial cellulose degradation and sulphate reduction. [ABSTRACT FROM AUTHOR]

Bacterial biofilms are usually assumed to originate from individual cells deposited on a surface. However, many biofilm-forming bacteria tend to aggregate in the planktonic phase so that it is possible that many natural and infectious biofilms originate wholly or partially from pre-formed cell aggregates. Here, we use agent-based computer simulations to investigate the role of pre-formed aggregates in biofilm development. Focusing on the initial shape the aggregate forms on the surface, we find that the degree of spreading of an aggregate on a surface can play an important role in determining its eventual fate during biofilm development. Specifically, initially spread aggregates perform better when competition with surrounding unaggregated bacterial cells is low, while initially rounded aggregates perform better when competition with surrounding unaggregated cells is high. These contrasting outcomes are governed by a trade-off between aggregate surface area and height. Our results provide new insight into biofilm formation and development, and reveal new factors that may be at play in the social evolution of biofilm communities. [ABSTRACT FROM AUTHOR]

Range expansions are a ubiquitous phenomenon, leading to the spatial spread of genetic, ecological, and cultural traits. While some of these traits are advantageous (and hence selected), other, nonselected traits can also spread by hitchhiking on the wave of population expansion. This requires us to understand how the spread of a hitchhiking trait is coupled to the wave of advance of its host population. Here, we use a system of coupled Fisher-Kolmogorov-Petrovsky-Piskunov (F-KPP) equations to describe the spread of a horizontally transmitted hitchhiking trait within a population as it expands. We extend F-KPP wave theory to the system of coupled equations to predict how the hitchhiking trait spreads as a wave within the expanding population. We show that the speed of this trait wave is controlled by an intricate coupling between the tip of the population and trait waves. Our analysis yields a new speed selection mechanism for coupled waves of advance and reveals the existence of previously unexpected speed transitions. [ABSTRACT FROM AUTHOR]

Malliavin weight sampling (MWS) is a stochastic calculus technique for computing the derivatives of averaged system properties with respect to parameters in stochastic simulations, without perturbing the system's dynamics. It applies to systems in or out of equilibrium, in steady state or time-dependent situations, and has applications in the calculation of response coefficients, parameter sensitivities and Jacobian matrices for gradient-based parameter optimisation algorithms. The implementation of MWS has been described in the specific contexts of kinetic Monte Carlo and Brownian dynamics simulation algorithms. Here, we present a general theoretical framework for deriving the appropriate MWS update rule for any stochastic simulation algorithm. We also provide pedagogical information on its practical implementation. [ABSTRACT FROM AUTHOR]

Microbial communities mediate crucial biogeochemical, biomedical and biotechnological processes, yet our understanding of their assembly, and our ability to control its outcome, remain poor. Existing evidence presents conflicting views on whether microbial ecosystem assembly is predictable, or inherently unpredictable. We address this issue using a well-controlled laboratory model system, in which source microbial communities colonize a pristine environment to form complex, nutrient-cycling ecosystems. When the source communities colonize a novel environment, final community composition and function (as measured by redox potential) are unpredictable, although a signature of the community's previous history is maintained. However, when the source communities are pre-conditioned to their new habitat, community development is more reproducible. This situation contrasts with some studies of communities of macro-organisms, where strong selection under novel environmental conditions leads to reproducible community structure, whereas communities under weaker selection show more variability. Our results suggest that the microbial rare biosphere may have an important role in the predictability of microbial community development, and that pre-conditioning may help to reduce unpredictability in the design of microbial communities for biotechnological applications. [ABSTRACT FROM AUTHOR]

Despite the fact that much of the global microbial biosphere is believed to exist in high pressure environments, the effects of hydrostatic pressure on microbial physiology remain poorly understood. We use a genome-wide screening approach, combined with a novel high-throughput high-pressure cell culture method, to investigate the effects of hydrostatic pressure on microbial physiology in vivo. The Keio collection of single-gene deletion mutants in Escherichia coli K-12 was screened for growth at a range of pressures from 0.1 MPa to 60 MPa. This led to the identification of 6 genes, rodZ, holC, priA, dnaT, dedD and tatC, whose products were required for growth at 30 MPa and a further 3 genes, tolB, rffT and iscS, whose products were required for growth at 40 MPa. Our results support the view that the effects of pressure on cell physiology are pleiotropic, with DNA replication, cell division, the cytoskeleton and cell envelope physiology all being potential failure points for cell physiology during growth at elevated pressure. [ABSTRACT FROM AUTHOR]

Direct microscopic fluorescence imaging of single molecules can provide a wealth of mechanistic information, but up to now, it has not been possible under high pressure conditions, due to limitations in microscope pressure cell design. We describe a pressure cell window design that makes it possible to image directly single molecules at high hydrostatic pressure. We demonstrate our design by imaging single molecules of Alexa Fluor 647 dye bound to DNA, at 120 and 210 bar, and following their fluorescence photodynamics. We further show that the failure pressure of this type of pressure cell window can be in excess of 1 kbar. [ABSTRACT FROM AUTHOR]

We present a model for host-parasite dynamics which incorporates both vertical and horizontal transmission as well as spatial structure. Our model consists of stacked contact processes (CP), where the dynamics of the host is a simple CP on a lattice while the dynamics of the parasite is a secondary CP which sits on top of the host-occupied sites. In the simplest case, where infection does not incur any cost, we uncover a novel effect: a non-monotonic dependence of parasite prevalence on host turnover. Inspired by natural examples of hyperparasitism, we extend our model to multiple levels of parasites and identify a transition between the maintenance of a finite and infinite number of levels, which we conjecture is connected to a roughening transition in models of surface growth. [ABSTRACT FROM AUTHOR]

We present a method for computing parameter sensitivities and response coefficients in Brownian dynamics simulations. The method involves tracking auxiliary variables (Malliavin weights) in addition to the usual particle positions, in an unperturbed simulation. The Malliavin weights sample the derivatives of the probability density with respect to the parameters of interest and are also interesting dynamical objects in themselves. Malliavin weight sampling is simple to implement, applies to equilibrium or nonequilibrium, steady state or time-dependent systems, and scales more efficiently than standard finite difference methods. [ABSTRACT FROM AUTHOR]

We have developed a modular system for high-resolution microscopy at high hydrostatic pressure. The system consists of a pressurized cell of volume ∼100 μl, a temperature controlled holder, a ram, and a piston. We have made each of these components in several versions which can be interchanged to allow a wide range of applications. Here, we report two pressure cells with pressure ranges 0.1–700 MPa and 0.1–100 MPa, which can be combined with hollow or solid rams and pistons. Our system is designed to work with fluorescent samples (using a confocal or epifluorescence microscope), but also allows for transmitted light microscopy via the hollow ram and piston. The system allows precise control of pressure and temperature (-20 to 70 °C), as well as rapid pressure quenching. We demonstrate its performance and versatility with two applications: time-resolved imaging of colloidal phase transitions caused by pressure changes between 0.1 and 100 MPa, and imaging the growth of Escherichia coli bacteria at 50 MPa. We also show that the isotropic-nematic phase transition of pentyl-cyanobiphenyl (5CB) liquid crystal provides a simple, convenient, and accurate method for calibrating pressure in the range 0.1–200 MPa. [ABSTRACT FROM AUTHOR]

Forward flux sampling (FFS) provides a convenient and efficient way to simulate rare events in equilibrium or non-equilibrium systems. FFS ratchets the system from an initial state to a final state via a series of interfaces in phase space. The efficiency of FFS depends sensitively on the positions of the interfaces. We present two alternative methods for placing interfaces automatically and adaptively in their optimal locations, on-the-fly as an FFS simulation progresses, without prior knowledge or user intervention. These methods allow the FFS simulation to advance efficiently through bottlenecks in phase space by placing more interfaces where the probability of advancement is lower. The methods are demonstrated both for a single-particle test problem and for the crystallization of Yukawa particles. By removing the need for manual interface placement, our methods both facilitate the setting up of FFS simulations and improve their performance, especially for rare events which involve complex trajectories through phase space, with many bottlenecks. [ABSTRACT FROM AUTHOR]

We present a method, Non-Stationary Forward Flux Sampling, that allows efficient simulation of rare events in both stationary and non-stationary stochastic systems. The method uses stochastic branching and pruning to achieve uniform sampling of trajectories in phase space and time, leading to accurate estimates for time-dependent switching propensities and time-dependent phase space probability densities. It is suitable for equilibrium or non-equilibrium systems, in or out of stationary state, including non-Markovian or externally driven systems. We demonstrate the validity of the technique by applying it to a one-dimensional barrier crossing problem that can be solved exactly, and show its usefulness by applying it to the time-dependent switching of a genetic toggle switch. [ABSTRACT FROM AUTHOR]

A correction to the article "Eliminating fast reactions in stochastic simulations of biochemical networks: A bistable genetic switch" that was published in 2008 is presented.

Despite much current interest in active matter, little is known about osmosis in active systems. Using molecular dynamics simulations, we investigate how active solutes perturb osmotic steady states. We find that solute activity increases the osmotic pressure, and can also expel solvent from the solution —i.e. cause reverse osmosis. The latter effect cannot be described by an effective temperature, but can be reproduced by mapping the active solution onto a passive one with the same degree of local structuring as the passive solvent component. Our results provide a basic framework for understanding active osmosis, and suggest that activity-induced structuring of the passive component may play a key role in the physics of active-passive mixtures. [ABSTRACT FROM AUTHOR]

Amyloid fibrils are self-assembled protein aggregates implicated in a number of human diseases. Fragmentation-dominated models for the self-assembly of amyloid fibrils have had important successes in explaining the kinetics of amyloid fibril formation but predict fibril length distributions that do not match experiments. Here we resolve this inconsistency using a combination of experimental kinetic measurements and computer simulations. We provide evidence for a structural transition that occurs at a critical fibril mass concentration, or CFC, above which fragmentation of fibrils is suppressed. Our simulations predict the formation of distinct fibril length distributions above and below the CFC, which we confirm by electron microscopy. These results point to a new picture of amyloid fibril growth in which structural transitions that occur during self-assembly have strong effects on the final population of aggregate species with small, and potentially cytotoxic, oligomers dominating for long periods of time at protein concentrations below the CFC. [ABSTRACT FROM AUTHOR]

Microalgae that form phytoplankton live and die in a complex microbial consortium in which they co-exist with bacteria and other microorganisms. The dynamics of species succession in the plankton depends on the interplay of these partners. Bacteria utilize substrates produced by the phototrophic algae, while algal growth can be supported by bacterial exudates. Bacteria might also use chemical mediators with algicidal properties to attack algae. To elucidate whether specific bacteria play universal or context-specific roles in the interaction with phytoplankton, we investigated the effect of cocultured bacteria on the growth of 8 microalgae. An interaction matrix revealed that the function of a given bacterium is highly dependent on the cocultured partner. We observed no universally algicidal or universally growth-promoting bacteria. The activity of bacteria can even change during the aging of an algal culture from inhibitory to stimulatory or vice versa. We further established a synthetic phytoplankton/bacteria community with the centric diatom, Coscinodiscus radiatus, and 4 phylogenetically distinctive bacterial isolates, Mameliella sp., Roseovarius sp., Croceibacter sp., and Marinobacter sp. Supported by a LotkaVolterra model, we show that interactions within the consortium are specific and that the sum of the pairwise interactions can explain algal and bacterial growth in the community. No synergistic effects between bacteria in the presence of the diatom was observed. Our survey documents highly species-specific interactions that are dependent on algal fitness, bacterial metabolism, and community composition. This species specificity may underly the high complexity of the multi-species plankton communities observed in nature. IMPORTANCE The marine food web is fueled by phototrophic phytoplankton. These algae are central primary producers responsible for the fixation of ca. 40% of the global CO2. Phytoplankton always co-occur with a diverse bacterial community in nature. This diversity suggests the existence of ecological niches for the associated bacteria. We show that the interaction between algae and bacteria is highly species-specific. Furthermore, both, the fitness stage of the algae and the community composition are relevant in determining the effect of bacteria on algal growth. We conclude that bacteria should not be sorted into algicidal or growth supporting categories; instead, a context-specific function of the bacteria in the plankton must be considered. This functional diversity of single players within a consortium may underly the observed diversity in the plankton. [ABSTRACT FROM AUTHOR]

Active Brownian particles (ABPs), when subject to purely repulsive interactions, are known to undergo activity-induced phase separation broadly resembling an equilibrium (attraction-induced) gas-liquid coexistence. Here we present an accurate continuum theory for the dynamics of phase-separating ABPs, derived by direct coarse graining, capturing leading-order density gradient terms alongside an effective bulk free energy. Such gradient terms do not obey detailed balance; yet we find coarsening dynamics closely resembling that of equilibrium phase separation. Our continuum theory is numerically compared to large-scale direct simulations of ABPs and accurately accounts for domain growth kinetics, domain topologies, and coexistence densities. [ABSTRACT FROM AUTHOR]

Abstract: Predicting and controlling the behaviour of microbial ecosystems demands a fundamental understanding of the factors controlling their dynamics. In the natural environment microbes typically live in small local populations with limited and unpredictable nutrient supply and high death rates. Here, we show that these conditions can produce oscillations in microbial population dynamics, even for a single population. For a large population, with deterministic growth dynamics, our model predicts transient (damped) oscillations. For a small population, demographic noise causes these oscillations to be sustained indefinitely. We show that the same mechanism can produce sustained stochastic oscillations in a two-species, nutrient-cycling microbial ecosystem. Our results suggest that oscillatory population dynamics may be a common feature of small microbial populations in the natural environment, even in the absence of complex interspecies interactions or spatial structuring. [Copyright &y& Elsevier]