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Nucleosomes and their modifications often facilitate gene regulation in eukaryotes. Certain genomic regions may obtain alternate epigenetic states through enzymatic reactions forming positive feedback between nucleosome states. How a system of nucleosome states maintains confinement is an open question. Here we explore a family of stochastic dynamic models with combinations of readwrite enzymes. We find that a larger number of intermediate nucleosome states increases both the robustness of linear spreading in models with only local recruitment processes and the degree of bi-stability under conditions with at least one non-local recruitment. Further, supplementing the positive feedback with one negative feedback acting over long distances along the genome enables effective confinement of epigenetic, bistable regions. Our study emphasizes the importance of determining whether each particular read-write enzyme acts only locally or between distant nucleosomes.

The rapid succession of new variants of SARS-CoV-2 emphasizes the need to understand the factors driving pathogen evolution. Here, we investigate a possible tradeoff between the rate of progression of a disease and its reproductive number. Using an SEIR framework, we show that in the exponential growth phase of an epidemic, there is an optimal disease duration that balances the advantage of a fast disease progression with that of causing many secondary infections. This result offers one possible explanation for the ever shorter generation times of novel variants of SARS-CoV-2, as it progressed from the original strain to the Alpha, Delta, and, from late 2021 onwards, to several Omicron variant subtypes. In the endemic state, the optimum disappears and longer disease duration becomes advantageous for the pathogen. However, selection pressures depend on context: mitigation strategies such as quarantine of infected individuals may slow down the evolution towards longer-lasting, more infectious variants. This work then suggests that, in the future, the trend towards shorter generation times may reverse, and SARS-CoV-2 may instead evolve towards longer-lasting variants.

1AbstractRestriction-modification (RM) systems are the most ubiquitous bacterial defense system against bacteriophages and an important part of controlling phage predation. Using genomic sequence data, we show that RM systems are often shared among bacterial strains in a structured way. Examining the network of interconnections between bacterial strains within each genus, we find that in many genera strains share more RM systems than expected from a random network. We also find that many genera have a larger than expected number of bacterial strains with unique RM systems. We use population dynamics models of closed and open phage-bacteria ecosystems to qualitatively understand the selection pressures that could lead to these non-random network structures with enhanced overlap or uniqueness. In our models we find that the phages impose a pressure that favours bacteria with more RM systems, and more overlap of RM systems with other strains, but in bacteria dominated states this is opposed by the increased cost to the growth rate of these bacteria. Similar to what we observe in the genome data, we find that two distinct bacterial strategies emerge – strains either have a larger overlap than expected, or they have more unique RM systems than one expects from a null model. The former strategy appears to dominate when the repertoire of available RM systems is smaller but the average number of RM systems per strain is larger.

Coronaviruses, including SARS-CoV, MERS-CoV, and SARS-CoV-2 cause respiratory diseases with remarkably heterogeneous progression. This in part reflects the viral ability to influence the cytokine secretion and thereby the innate immune system. Especially the viral interference of IFN-I signaling and the subsequent deficiency of innate immune response in the early phase have been associated with rapid virus replication and later excessive immune responses. We propose a mathematical framework to analyze IFN-I signaling and its impact on the interaction motif between virus, NK cells and macrophages. The model recapture divergent dynamics of coronavirus infections including the possibility for elevated secretion of IL-6 and IFN-γas a consequence of exacerbated macrophage activation. Dysfunction of NK cells recruitment increase disease severity by leading to a higher viral load peak, the possibility for excessive macrophage activation, and an elevated risk of the cytokine storm. Thus the model predicts that delayed IFN-I signaling could lead to pathogenicity in the latter stage of an infection. Reversely, in case of strong NK recruitment from infected cells we predict a possible chronic disease state with moderate and potentially oscillating virus/cytokine levels.

The differentiation of ICM cells into epiblast (EPI) and primitive endoderm (PE) is central in embryonic development. It is known that FGF4 signaling is important in this process, but it remains unclear how cells can be correctly partitioned. Here we model the NANOG-GATA6-FGF4 network, and test all 64 logical regulatory combinations for their ability to partition a group of cells. We found that nearly all the logic combinations allowed for correct partitioning, including a minimal network where self-activation of NANOG and GATA6 was inactivated. However such self-activation increased the robustness of the system. Furthermore, the model also captured the reported changes in cell proportions in response to FGF perturbations. This constrains the possible regulatory logic and predicts the presence of an “OR” gate in cell-cell communication. We repeatedly found that FGF4 coordinated the decision in two phases: A convergence and a bifurcation phase. First FGF4 negative feedback drives the cells to a balanced “battle” state where most cells have intermediate levels of both regulators, thus being double positive. Subsequent bifurcation happens at constant FGF4 level. Together our results suggest that the frequently observed state of multipotency during differentiation may be an emergent phenomenon resulting from inter-cellular negative feedbacks.

Epidemics are regularly associated with reports of superspreading: single individuals infecting many others. How do we determine if such events are due to people inherently being biological superspreaders or simply due to random chance? We present an analytically solvable model for airborne diseases which reveal the spreading statistics of epidemics in socio-spatial heterogeneous spaces and provide a baseline to which data may be compared. In contrast to classical SIR models, we explicitly model social events where airborne pathogen transmission allows a single individual to infect many simultaneously, a key feature that generates distinctive output statistics. We find that diseases that have a short duration of high infectiousness can give extreme statistics such as 20 % infecting more than 80 %, depending on the socio-spatial heterogeneity. Quantifying this by a distribution over sizes of social gatherings, tracking data of social proximity for university students suggest that this can be a approximated by a power law. Finally, we study mitigation efforts applied to our model. We find that the effect of banning large gatherings works equally well for diseases with any duration of infectiousness, but depends strongly on socio-spatial heterogeneity.

Although coronavirus disease 2019 (COVID-19) has caused severe suffering in many countries around the world, the efficacy of non-pharmaceutical interventions such as policies of social distancing has been greater than models have predicted. Meanwhile, evidence is mounting that the pandemic is characterized by superspreading, where a small fraction account for the majority of infections. Capturing this phenomenon theoretically requires modeling at the scale of individuals. Using a mathematical model, we show that superspreading represents an Achilles’ heel of COVID-19, and drastically improves the efficacy of mitigations which reduce the personal contact number, even when this is done without changing the average social contact time.

So far, the COVID-19 pandemic has been characterised by an initial rapid rise in new cases followed by a peak and a more erratic behaviour that varies between regions. This is not easy to reproduce with traditional SIR models, which predict a more symmetric epidemic. Here, we argue that superspreaders and population heterogeneity are the core factors explaining this discrepancy. We do so through an agent-based lattice model of a disease spreading in a heterogeneous population. We predict that an epidemic driven by superspreaders will spread rapidly in cities, but not in the countryside where the sparse population limits the maximal number of secondary infections. This suggests that mitigation strategies should include restrictions on venues where people meet a large number of strangers. Furthermore, mitigating the epidemic in cities and in the countryside may require different levels of restrictions.

SUMMARYThe Ca2+ modulated pulsatile secretion of glucagon and insulin by pancreatic α and β cells plays a key role in glucose homeostasis. However, how α and β cells coordinate via paracrine interaction to produce various Ca2+ oscillation patterns is still elusive. Using a microfluidic device and transgenic mice in which α and β cells were labeled with different colors, we were able to record islet Ca2+ signals at single cell level for long times. Upon glucose stimulation, we observed heterogeneous Ca2+ oscillation patterns intrinsic to each islet. After a transient period, the oscillations of α and β cells were globally phase-locked, i.e., the two types of cells in an islet each oscillate synchronously but with a phase shift between the two. While the activation of α cells displayed a fixed time delay of ~20 s to that of β cells, β cells activated with a tunable delay after the α cells. As a result, the tunable phase shift between α and β cells set the islet oscillation period and pattern. Furthermore, we demonstrated that the phase shift can be modulated by glucagon. A mathematical model of islet Ca2+ oscillation taking into consideration of the paracrine interaction was constructed, which quantitatively agreed with the experimental data. Our study highlights the importance of cell-cell interaction to generate stable but tunable islet oscillation patterns.

Bacteria often arrange themselves in various spatial configurations which changes how they interact with their surroundings. In this paper, we investigate in silico how the structure of the bacterial arrangements influences the adsorption of bacteriophages. We quantify how the adsorption rate scales with the number of bacteria in the arrangement, and show that the adsorption rates for microcolonies (increasing with exponent ∼1/3) and bacterial chains (increasing with exponent ∼0.5 − 0.8) are substantially lower than for well-mixed bacteria (increasing with exponent 1). We further show that, after infection, the spatially clustered arrangements reduce the effective burst size by more than 50 % and cause substantial superinfections in a very short time interval after phage lysis.SIGNIFICANCEWhen bacteria forms clusters they substantially change their exposure to invading phages and other external agents from the well-mixed scenario. Despite this fact, much research has focused on and is focusing on using well-mixed bacteria. Understanding the kinetics of the spatial structures is paramount to developing robust analyses and theories of experimental results. We carefully investigate how the clusters lower the adsorption rate of external phages and how the clustering modifies the hit probabilities for the bacteria.

BackgroundThe draconian measures used to control COVID-19 dissemination have been highly effective but only at enormous socioeconomic cost. Evidence suggests that “superspreaders” who transmit the virus to a large number of people, play a substantial role in transmission; recent estimates suggest that about 1-20% of people with the virus are the source for about 80% of infections. We used an agent-based model to explore the interplay between social structure, mitigation and superspreading.MethodsWe developed an agent-based model with a subset of “superspreader” agents that transmit disease far more efficiently. These agents act in a social network that allows transmission during contacts in three sectors: “home,” “work/school” and “other”. We simulated the effect of various mitigation strategies that limit contacts in each of these sectors, and used the model to fit COVID-19 mortality data from Sweden.FindingsReducing contacts in the “other” sector had a far greater impact on epidemic trajectory than did reducing “home” or “work/school” contacts; this effect was substantially enhanced when the infectivity of children was reduced relative to that of adults. The model fit Swedish hospitalization data with reasonable assumptions about the effect of Sweden’s mitigation policies on contacts in the different sectors.InterpretationOur results suggest COVID-19 could be controlled by limiting large gatherings and other opportunities for contacts between people in restaurants, sporting events, concerts and worship services) while still allowing regular contacts in the home or at work and school.Research in contextEvidence before this studySuperspreading events have long been known to be important in the epidemiology of many infectious diseases, including tuberculosis, measles, Ebola and SARS. Since the emergence of SARS-CoV-2, epidemiologic analyses have inferred substantial individual-level variation in transmissibility, with an estimated 1% to 20% of infected persons causing about 80% of all COVID-19 cases.Added value of this studyWe developed an agent-based socially structured model to simulate the effect of superspreaders in COVID-19 transmission in the context of country-wide “lockdown” policies. These simulations indicate that COVID-19 can be effectively mitigated by limiting contacts between people who otherwise rarely meet, while allowing home and most work/school contacts to continue.Implications of all available evidenceIt is crucial to include heterogeneity in individual infectiousness when modeling the impact of mitigation strategies on observed COVID-19 epidemic patterns. Reducing opportunities for superspreading by limiting random contacts outside home and work could be the most effective way to control COVID-19. Our findings suggest why the epidemic has continued to decline following re-opening of work and school in European countries. The superspreader phenomenon may also explain the variability in COVID-19 incidence in rural and urban areas within a country.

Background: The international community has been put in an unprecedented situation by the COVID-19 pandemic. Creating models to describe and quantify alternative mitigation strategies becomes increasingly urgent. Methods: In this study, we propose an agent-based model of disease transmission in a society divided into closely connected families, workplaces, and social groups. This allows us to discuss mitigation strategies, including targeted quarantine measures. Results: We find that workplace and more diffuse social contacts are roughly equally important to disease spread, and that an effective lockdown must target both. We examine the cost-benefit of replacing a lockdown with tracing and quarantining contacts of the infected. Quarantine can contribute substantially to mitigation, even if it has short duration and is done within households. When reopening society, testing and quarantining is a strategy that is much cheaper in terms of lost workdays than a long lockdown of workplaces. Conclusions: A targeted quarantine strategy is quite efficient with only 5 days of quarantine, and its relative effect increases when supplemented with other measures that reduce disease transmission.

The competition between bacteria often involves both nutrients and phage predators and may give rise to abrupt regime shifts between the alternative stable states characterized by different species compositions. While such transitions have been previously studied in the context of competition for nutrients, the case of phage-induced bistability between competing bacterial species has not been considered yet. Here we demonstrate a possibility of regime shifts in well-mixed phage-bacterial ecosystems. In one of the bistable states the fast-growing bacteria competitively exclude the slow-growing ones by depleting their common nutrient. Conversely, in the second state the slow-growing bacteria with a large burst size generate such a large phage population that the other species cannot survive. This type of bistability can be realized as the competition between a strain of bacteria protected from phage by abortive infection and another strain with partial resistance to phage. It is often desirable to reliably control the state of microbial ecosystems, yet bistability significantly complicates this task. We discuss successes and limitations of one control strategy in which one adds short pulses to populations of individual species. Our study proposes a new type of phage therapy, where introduction of the phage is supplemented by addition of a partially resistant host bacteria.IMPORTANCEPhage-microbial communities play an important role in human health as well as natural and industrial environments. Here we show that these communities can assume several alternative species compositions separated by abrupt regime shifts. Our model predicts these regime shifts in the competition between bacterial strains protected by two different phage defense mechanisms: abortive infection/CRISPR and partial resistance. The history dependence caused by regime shifts greatly complicates the task of manipulation and control of a community. We propose and study a successful control strategy via short population pulses aimed at inducing the desired regime shifts. In particular, we predict that a fast-growing pathogen could be eliminated by a combination of its phage and a slower-growing susceptible host.

Summary How do flat sheets of cells form gut and neural tubes? Across systems, several mechanisms are at play: cells wedge, form actomyosin cables, or intercalate. As a result, the cell sheet bends, and the tube elongates. It is unclear to what extent each mechanism can drive tube formation on its own. To address this question, we computationally probe if one mechanism, either cell wedging or intercalation, may suffice for the entire sheet-to-tube transition. Using a physical model with epithelial cells represented by polarized point particles, we show that either cell intercalation or wedging alone can be sufficient and that each can both bend the sheet and extend the tube. When working in parallel, the two mechanisms increase the robustness of the tube formation. The successful simulations of the key features in Drosophila salivary gland budding, sea urchin gastrulation, and mammalian neurulation support the generality of our results., Graphical Abstract, Highlights • Cell wedging and intercalation are modeled using a polarized point-particle approach • Cell intercalation is sufficient for tube budding • Tube budding is more robust when intercalation is complemented by wedging • Wedging and differential proliferation are sufficient for mammalian neurulation, Developmental Biology; Experimental Models in Systems Biology

Natural bacterial populations are subject to constant predation pressure by phages. Bacteria use a variety of well-studied molecular mechanisms to defend themselves from phage predation. However, since phage are non-motile, perhaps the simplest defense against phage would be for bacteria to outrun their predators. In particular, chemotaxis, the active migration of bacteria up attractant gradients, may help the bacteria escape slowly diffusing phages. Here we study phage infection dynamics in migrating bacterial populations driven by chemotaxis through low viscosity agar plates. We find that expanding phage-bacteria populations support two migrating fronts, an outermost “bacterial” front driven by nutrient uptake and chemotaxis and an inner “phage” front at which bacterial population collapses due to phage predation. We show that with increasing adsorption rate and initial phage population, the rate of migration of the phage front increases, eventually overtaking the bacterial front and driving the system across a “phage transition” from a regime where bacteria outrun a phage infection to one where they must evolve phage resistance to survive. We confirm experimentally that this process requires phages to “surf” the bacterial front by repeatedly reinfecting the fastest moving bacteria. A deterministic model recapitulates the transition. Macroscopic fluctuations in bacterial densities at the phage front suggest that a feedback mechanism, possibly due to growth rate dependent phage infection rates, drives millimeter scale spatial structure in phage-bacteria populations. Our work opens a new, spatiotemporal, line of investigation into the eco-evolutionary struggle between bacteria and their phage predators.Significance StatementThe infection of bacteria by phage requires physical contact. This fact means that motile bacteria may avoid non-motile phage by simply running away. By this mechanism bacterial chemotaxis may help bacteria to escape phages. Here we show that when phage infect bacteria moving in soft agar plates, high phage populations or infectivity rates result in phages stopping and killing all bacteria. Conversely, when initial phage numbers or infectivity rates are low, bacteria are able to migrate away from phage successfully, despite phage ability to “surf” bacterial fronts for more than 24 hours. Between these regimes we document a “phage transition” where bacterial physiology and contingency in phage infection manifest through large-scale fluctuations in spatio-temporal dynamics.

Bacteria form colonies and secrete extracellular polymeric substances that surround the individual cells. These spatial structures are often associated with collaboration and quorum sensing between the bacteria. Here we investigate the mutual protection provided by spherical growth of a monoclonal colony during exposure to phages that proliferate on its surface. As a proof of concept we exposed growing colonies ofEscherichia colito a virulent mutant of phage P1. When the colony consists of less than ~ 50000 members it is eliminated, while larger initial colonies allow long-term survival because the growth of bacteria throughout the spherical colony exceeds the killing of bacteria on the surface. A mathematical model pinpoints how this critical colony size depends on key parameters in the phage infection cycle. Surprisingly, we predict that a higher phage adsorption rate would allow substantially smaller colonies to survive a virulent phage.Significance StatementBacteria are repeatedly exposed to an excess of phages, and carry evidence of this in terms of multiple defense mechanisms encoded in their genome. In addition to molecular mechanisms, bacteria may exploit the defense of spatial refuges. Here we demonstrate how bacteria can limit the impact of a virulent phage attack by growing as a colony which only exposes its surface to phages. We identify a critical size of the initial colony, below which the phages entirely eliminates the colony, and above which the colony continues to grow despite the presence of phages. Our study suggests that coexistence of phages and bacteria is strongly influenced by the spatial composition of microcolonies of susceptible bacteria.