Quantification of microbial community assembly processes during degradation on diverse plastispheres based on physicochemical characters and phylogenetic bin-based null model analysis

To understand the differences in degradation processes depending on the chemical properties of polymers, it is necessary to both quantify the microbiome composition and evaluate the process of microbial turnover (i.e., community assembly processes) in a variety of polymer materials. In this study, using a phylogenetic bin-based null model analysis (i.e., iCAMP), we evaluated community assembly processes from original estuary water to 37 types of polymers, which provide overwhelmingly diverse niches for microbes, in 14-day incubation experiments. First, we evaluated the polymer properties related to degradation rates. Polymers with higher adipic acid (AdA) monomer exhibited higher motility, hydrophilicity, and degradation rates, whereas those with higher aromatic monomer exhibited the opposite trends. Second, microbiome composition analysis was performed, and the microbiomes were significantly changed by the AdA or aromatic content. This was consistent with the polymer properties, suggesting that polymer motility and hydrophilicity attributable to the first-order structure modify the accessibility of the enzyme to the reaction site and hence the degradation rate, resulting in differences in microbiome community composition. Finally, we determined community assembly processes from estuary water to plastics using a phylogenetic bin-based null model analysis. The importance of heterogeneous selection was higher in mobile, hydrophilic, and fast-degrading polymers, while that of homogeneous selection was lower. This suggests that the environmental difference between before and after incubation becomes significant under rapid degradation, which select microbes adapted to biofilm environments. In addition, the more stochastic turnover prevailed, the more variation in the communities (i.e., β-diversity) increased. This suggests that turnover processes not dictated by the environment lead to instability in community compositions.

Connections between soil microbes, land use and European climate: Insights for management practices

Soil microbial biomass and activity strongly depend on land use, vegetation cover, climate, and soil physicochemical properties. In most cases, this dependence was assessed by one-to-one correlations while by employing network analysis, information about network robustness and the balance between stochasticity and determinism controlling connectivity, was revealed. In this study, we further elaborated on the hypothesis of Smith et al. (2021) that cropland soils depended more on climate variables and therefore are more vulnerable to climate change. We used the same dataset with that of Smith et al. (2021) that contains seasonal microbial, climate and soil variables collected from 881 soil points representing the main land uses in Europe: forests, grassland, cropland. We examined complete (both direct and indirect relationships) and incomplete networks (only direct relationships) and recorded higher robustness in the former. Partial Least Square results showed that on average more than 45% of microbial attributes' variability was predicted by climate and habitat drivers denoting medium to strong effect of habitat filtering. Network architecture slightly affected by season or land use type; it followed the core/periphery structure with positive and negative interactions and no hub nodes. Microbial attributes (biomass, activity and their ratio) mostly belong to core block together with Soil Organic Carbon (SOC), while climate and soil variables to periphery block with the exception of cropland networks, denoting the higher dependence between microbial and climate variables in these latter. All complete networks appeared robust except for cropland and forest in summer, a finding that disagrees with our initial hypothesis about cropland. Networks' connectivity was controlled stronger by stochasticity in forest than in croplands. The lack of human interventions in forest soils increase habitat homogeneity enhancing the influence of stochastic agents such as microbial unlimited dispersal and/or stochastic extinction. The increased stochasticity implies the necessity for proactive management actions.

Unraveling the forces shaping foraging dynamics in harvester ant colonies: Recruitment efficiency and environmental variability

The collective foraging behavior of ant colonies is a central focus in behavioral ecology. This paper enhances the classical model of foraging dynamics in harvester ant colonies by introducing a nonlinear recruitment rate and considering environmental variability. Initially, we analyze the existence and stability of steady states in the deterministic model. The results suggest that an increase in mean recruitment time can reduce the foraging threshold, leading to both forward and backward bifurcations. Furthermore, both average recruitment time and the interference intensity of recruiters impact the number of workers in each subgroup. Subsequently, we conduct an analysis of the long-term and transient dynamics of collective foraging in random environments, providing sufficient conditions for the colony to sustain foraging activity. The findings emphasize the scene-dependent impact of environmental stochasticity on foraging dynamics. When ant colonies deterministically cease foraging, environmental stochasticity may unexpectedly prolong the foraging state. Conversely, when colonies deterministically persist in foraging, environmental stochasticity may disrupt this continuity. Additionally, the effect of environmental stochasticity on foraging status varies with the initial worker size. Sizes near the boundary of the basin of attraction between non-foraging and foraging states exhibit greater sensitivity to environmental stochasticity, and sufficiently large stochasticity can impact foraging dynamics across a broader range of initial worker sizes. These findings underscore the intricate interplay between intrinsic factors (e.g., recruitment efficiency and interference intensity) and extrinsic factors (e.g., environmental stochasticity) in shaping the collective foraging dynamics of ant colonies.

Inferring stochastic group interactions within structured populations via coupled autoregression

The internal behaviour of a population is an important feature to take account of when modelling its dynamics. In line with kin selection theory, many social species tend to cluster into distinct groups in order to enhance their overall population fitness. Temporal interactions between populations are often modelled using classical mathematical models, but these sometimes fail to delve deeper into the, often uncertain, relationships within populations. Here, we introduce a stochastic framework that aims to capture the interactions of animal groups and an auxiliary population over time. We demonstrate the model's capabilities, from a Bayesian perspective, through simulation studies and by fitting it to predator-prey count time series data. We then derive an approximation to the group correlation structure within such a population, while also taking account of the effect of the auxiliary population. We finally discuss how this approximation can lead to ecologically realistic interpretations in a predator-prey context. This approximation also serves as verification to whether the population in question satisfies our various assumptions. Our modelling approach will be useful for empiricists for monitoring groups within a conservation framework and also theoreticians wanting to quantify interactions, to study cooperation and other phenomena within social populations.

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Life Is Uncertain: Inherent Variability Exhibited by Organisms, and at Higher Levels of Biological Organization

Organisms act stochastically. A not uncommon view in the ecological literature is that this is mainly due to the observer having insufficient information or a stochastic environment-and not partly because organisms themselves respond with inherent unpredictability. In this study, I compile the evidence that contradicts that view. Organisms generate uncertainty internally, which results in irreducible stochastic responses. I consider why: for instance, stochastic responses are associated with greater adaptability to changing environments and resource availability. Over longer timescales, biologically generated uncertainty influences behavior, evolution, and macroecological processes. Indeed, it could be stated that organisms are systems defined by the internal generation, magnification, and record-keeping of uncertainty as inputs to responses. Important practical implications arise if organisms can indeed be defined by an association with specific classes of inherent uncertainty: not least that isolating those signatures then provides a potential means for detecting life, for considering the forms that life could theoretically take, and for exploring the wider limits to how life might become distributed. These are all fundamental goals in astrobiology.

Depth significantly affects plastisphere microbial evenness, assembly and co-occurrence pattern but not richness and composition

Microplastics have become one of the hot concerns of global marine pollution. In recent years, diversity and abiotic influence factors of plastisphere microbial communities were well documented, but our knowledge of their assembly mechanisms and co-occurrence patterns remains unclear, especially the effects of depth on them. Here, we collected microorganisms on microplastics to investigate how ocean depth affects on microbial diversity, community composition, assembly processes and co-occurrence patterns. Our results indicated that there were similar microbial richness and community compositions but microbial evenness and unique microbes were obviously different in different ocean layers. Our findings also demonstrated that deterministic processes played dominant roles in the assembly of the mesopelagic plastisphere microbial communities, while the bathypelagic microbial community assembly was mainly shaped by stochastic processes. In addition, the co-occurrence networks suggested that the relationships between microorganisms in the mesopelagic layer were more complex and stable than those in the bathypelagic layer. Simultaneously, we also found that Proteobacteria and Actinobacteriota were the most abundant keystones which played important roles in microbial co-occurrence networks at both layers. This study enhanced our understanding of microbial diversity, assembly mechanism, and co-occurrence pattern on plastisphere surfaces, and provided useful insights into microorganisms capable of degrading plastics and microbial remediation.

De novo hematopoietic (stem) cell generation - A differentiation or stochastic process?

The hematopoietic system is one of the earliest tissues to develop. De novo generation of hematopoietic progenitor and stem cells occurs through a transdifferentiation of (hemogenic) endothelial cells to hematopoietic identity, resulting in the formation of intra-aortic hematopoietic cluster (IAHC) cells. Heterogeneity of IAHC cell phenotypes and functions has stymied the field in its search for the transcriptional program of emerging hematopoietic stem cells (HSCs), given that an individual IAHC cannot be simultaneously examined for function and transcriptome. Several models could account for this heterogeneity, including a novel model suggesting that the transcriptomes of individual emerging IAHC cells are in an unstable/metastable state, with pivotal hematopoietic transcription factors expressed dynamically due to transcriptional pulsing and combinatorial activities. The question remains - how is functional hematopoietic cell fate established - is the process stochastic? This article touches upon these important issues, which may be relevant to the field's inability to make HSCs ex vivo.

Modeling of oncolytic viruses in a heterogeneous cell population to predict spread into non-cancerous cells

New cancer treatment modalities that limit patient discomfort need to be developed. One possible new therapy is the use of oncolytic (cancer-killing) viruses. It is only recently that our ability to manipulate viral genomes has allowed us to consider deliberately infecting cancer patients with viruses. One key consideration is to ensure that the virus exclusively targets cancer cells and does not harm nearby non-cancerous cells. Here, we use a mathematical model of viral infection to determine the characteristics a virus would need to have in order to eradicate a tumor, but leave non-cancerous cells untouched. We conclude that the virus must differ in its ability to infect the two different cell types, with the infection rate of non-cancerous cells needing to be less than one hundredth of the infection rate of cancer cells. Differences in viral production rate or infectious cell death rate alone are not sufficient to protect non-cancerous cells.

Phenotypic heterogeneity in human genetic diseases: ultrasensitivity-mediated threshold effects as a unifying molecular mechanism

Phenotypic heterogeneity is very common in genetic systems and in human diseases and has important consequences for disease diagnosis and treatment. In addition to the many genetic and non-genetic (e.g., epigenetic, environmental) factors reported to account for part of the heterogeneity, we stress the importance of stochastic fluctuation and regulatory network topology in contributing to phenotypic heterogeneity. We argue that a threshold effect is a unifying principle to explain the phenomenon; that ultrasensitivity is the molecular mechanism for this threshold effect; and discuss the three conditions for phenotypic heterogeneity to occur. We suggest that threshold effects occur not only at the cellular level, but also at the organ level. We stress the importance of context-dependence and its relationship to pleiotropy and edgetic mutations. Based on this model, we provide practical strategies to study human genetic diseases. By understanding the network mechanism for ultrasensitivity and identifying the critical factor, we may manipulate the weak spot to gently nudge the system from an ultrasensitive state to a stable non-disease state. Our analysis provides a new insight into the prevention and treatment of genetic diseases.

Expecting the unexpected: a review of learning under uncertainty across development

Many of our decisions take place under uncertainty. To successfully navigate the environment, individuals need to estimate the degree of uncertainty and adapt their behaviors accordingly by learning from experiences. However, uncertainty is a broad construct and distinct types of uncertainty may differentially influence our learning. We provide a semi-systematic review to illustrate cognitive and neurobiological processes involved in learning under two types of uncertainty: learning in environments with stochastic outcomes, and with volatile outcomes. We specifically reviewed studies (N = 26 studies) that included an adolescent population, because adolescence is a period in life characterized by heightened exploration and learning, as well as heightened uncertainty due to experiencing many new, often social, environments. Until now, reviews have not comprehensively compared learning under distinct types of uncertainties in this age range. Our main findings show that although the overall developmental patterns were mixed, most studies indicate that learning from stochastic outcomes, as indicated by increased accuracy in performance, improved with age. We also found that adolescents tended to have an advantage compared with adults and children when learning from volatile outcomes. We discuss potential mechanisms explaining these age-related differences and conclude by outlining future research directions.

Revisiting and redefining return rate for determination of the precise growth status of a species

Growth curve models play an instrumental role in quantifying the growth of biological processes and have immense practical applications across all disciplines. The most popular growth metric to capture the species fitness is the "Relative Growth Rate" in this domain. The different growth laws, such as exponential, logistic, Gompertz, power, and generalized Gompertz or generalized logistic, can be characterized based on the monotonic behavior of the relative growth rate (RGR) to size or time. Thus, in this case, species fitness can be determined truly through RGR. However, in nature, RGR is often non-monotonic and specifically bell-shaped, especially in the situation when a species is adapting to a new environment [1]. In this case, species may experience with the same fitness (RGR) for two different time points. The species precise growth and maturity status cannot be determined from this RGR function. The instantaneous maturity rate (IMR), as proposed by [2], helps to determine the correct maturity status of the species. Nevertheless, the metric IMR suffers from severe drawbacks; (i) IMR is intractable for all non-integer values of a specific parameter. (ii) The measure depends on a model parameter. The mathematical expression of IMR possesses the term "carrying capacity" which is unknown to the experimenter. (iii) Note that for identifying the precise growth status of a species, it is also necessary to understand its response when the populations are deflected from their equilibrium position at carrying capacity. This is an established concept in population biology, popularly known as the return rate. However, IMR does not provide information on the species deflection rate at the steady state. Hence, we propose a new growth measure connected with the species return rate, termed the "reverse of relative of relative growth rate" (henceforth, RRRGR), which is treated as a proxy for the IMR, having similar mathematical properties. Finally, we introduce a stochastic RRRGR model for specifying precise species growth and status of maturity. We illustrate the model through numerical simulations and real fish data. We believe that this study would be helpful for fishery biologists in regulating the favorable conditions of growth so that the species can reach a steady state with optimum effort.

Ecological insights into the resilience of marine plastisphere throughout a storm disturbance

Among numerous research about marine plastisphere, the community living on the surface of plastic debris, little attention was given to the ecological mechanisms governing prokaryotes compared to eukaryotes, and even less focused on their resilience in a changing climate with more storm prevalence. Our current research recruited an integrated approach involving community succession across temporal dimension, ecological mechanisms that govern the assembly, and resilience to environmental perturbations to highlight the ecology of different kingdoms in the plastisphere. Towards this goal, we examined the succession of the prokaryotic and eukaryotic communities on artificial plastic nets in a sidestream of seawater from the Gulf of Aqaba over 35 days. A robust local storm enabled investigation of the alterations before, during, and after this disturbance, aiming at the community's potential to recover. Data from 16S and 18S rRNA sequencing and microscopic analyses decrypted the plastisphere diversity, community assembly, and stochasticity, followed by further analyses of functional and co-occurrence networks for the prokaryotic group. Prokaryotic and eukaryotic communities underwent exact opposite ecological mechanisms. While determinism driven by a robust environmental selection dictated the prokaryotic community assembly, stochasticity prevailed when this condition was relaxed. Interestingly, resilience against disturbance was observed in prokaryotes but not in eukaryotes. The decrease in compositional, functional diversity and network complexity in the prokaryotic community was reversed, presumably due to the niche specification process and high dispersal. Niche specification following perturbation was evident in some bacteria by selected functions associated with plastic degradation, stress response, and antibiotic resistance. On the contrary, eukaryotes decreased in diversity and were dominated by the commonly found Chlorophyta towards the later successional period. Novel findings on the ecology of marine plastisphere during perturbation encourage the integration of this aspect into prediction research.

Geographic Scale Influences the Interactivities Between Determinism and Stochasticity in the Assembly of Sedimentary Microbial Communities on the South China Sea Shelf

Determinism and stochasticity in microbial community composition decisions have attracted wide attention. However, there is no consensus on their interrelationships and relative importance, and the mechanism controlling the interaction between the two ecological processes remains to be revealed. The interaction of the two ecological processes on the continental shelf of the South China Sea was studied by performing 16S rRNA gene amplicon sequencing on 90 sediments at multiple depths in five sites. Three nearshore sites have higher microbial diversity than those two close to the shelf margin. Different microbial composition was observed between sites and microbial composition of nearshore sites was positively correlated with total nitrogen, total sulfur, total organic carbon, and dissolved oxygen, while that of offshore was positively correlated with total carbon, salinity, and photosynthetically active radiation. The null model test showed that the community composition among layers of the same site and between nearby sites was mainly dominated by the homogeneous selection, while that between distant sites was mainly affected by dispersal limitation, which indicates that geographic scale influences the interactivities of determinism and stochasticity. Our research indicates that the balance of these two ecological processes along the geographic scale is mainly determined by the dispersal ability of microbes and environmental heterogeneity between areas. The study provides new insights into how deterministic and stochastic processes shape microbial community composition on the continental shelf.

Contrasting archaeal and bacterial community assembly processes and the importance of rare taxa along a depth gradient in shallow coastal sediments

Marine microbial communities assemble along a sediment depth gradient and are responsible for processing organic matter. Composition of the microbial community along the depth is affected by various biotic and abiotic factors, e.g., the change of redox gradient, the availability of organic matter, and the interactions of different taxa. The community structure is also subjected to some random changes caused by stochastic processes of birth, death, immigration and emigration. However, the high-resolution shifts of microbial community and mechanisms of the vertical assembly processes in marine sediments remain poorly described. Archaeal and bacterial communities were analyzed based on 16S rRNA gene amplicon sequencing and metagenomes in the Bohai Sea sediment samples. The archaeal community was dominated by Thaumarchaeota with increased alpha diversity along depth. Proteobacteria was the dominant bacterial group with decreased alpha diversity as depth increased. Sampling sites and depths collectively affected the beta-diversity for both archaeal and bacterial communities. The dominant mechanism determining archaeal community assembly was determinism, which was mostly contributed by homogeneous selection, i.e., consistent selection pressures in different locations or depths. In contrast, bacterial community assembly was dominated by stochasticity. Co-occurrence networks among different taxa and key functional genes revealed a tight community with low modularity in the bottom sediment, and disproportionately more interactions among low abundant ASVs. This suggests a significant contribution to community stabilization by rare taxa, and suggests that the bottom layer, rather than surface sediments may represent a hotspot for benthic microbial interactions.

On geometric mean fitness: a reply to Takacs and Bourrat

In a recent paper, Takacs and Bourrat (Biol Philos 37:12, 2022) examine the use of geometric mean reproductive output as a measure of biological fitness. We welcome Takacs and Bourrat's scrutiny of a fitness definition that some philosophers have adopted uncritically. We also welcome Takacs and Bourrat's attempt to marry the philosophical literature on fitness with the biological literature on mathematical measures of fitness. However, some of the main claims made by Takacs and Bourrat are not correct, while others are correct but not for the reasons they give.

High biodiversity and distinct assembly patterns of microbial communities in groundwater compared with surface water

The differences in bacterial community assembly mechanism between surface water and groundwater, as well as the driving factors of environmental factors, are still unknown. Here we aimed to answer these questions by analyzing microbial community samples from surface water and groundwater. We observed a strong connection between microbial communities in surface water and groundwater and several human pathogens are shared between surface water and groundwater; however, the richness and diversity of groundwater microbial communities were greater than those of surface water, regardless of the season. Additionally, bacterial community compositions of surface water and groundwater differed significantly between seasons. Most importantly, the groundwater community exhibited a highly deterministic assembly process (56% contributed by deterministic process, with neutral community model R² = 0.277) compared with surface water (51% contributed by deterministic process, with R² = 0.526). This study provides a deep understanding of the effects of environmental factors on surface water and groundwater microbial communities, to better protect water resources.

Optimal management of stochastic invasion in a metapopulation with Allee effects

Invasive species account for incalculable damages worldwide, in both ecological and bioeconomic terms. The question of how a network of invasive populations can be optimally managed is one that deserves further exploration. A study accounting for partial observability and imperfect detection, in particular, could yield useful insights into species eradication efforts. Here, we generalized a simple model system that we developed in previous work. This model consists of three interacting populations with underlying strong Allee effects and stochastic dynamics, inhabiting distinct locations connected by dispersal, which can generate bistability. To explore the stochastic dynamics, we formulated an individual-based modeling approach. Next, using the theory of continuous-time Markov chains, we approximated the original high-dimensional model by a Markov chain with eight states, with each state corresponding to a combination of population thresholds. We then used the reduced model as the core for a powerful decision-making tool, referred to as a Partially Observable Markov Decision Process (POMDP). Analysis of this POMDP indicates when the system results in optimal management outcomes.

Distinct and Temporally Stable Assembly Mechanisms Shape Bacterial and Fungal Communities in Vineyard Soils

Microbial communities in agricultural soils are fundamental for plant growth and in vineyard ecosystems contribute to defining regional wine quality. Managing soil microbes towards beneficial outcomes requires knowledge of how community assembly processes vary across taxonomic groups, spatial scales, and through time. However, our understanding of microbial assembly remains limited. To quantify the contributions of stochastic and deterministic processes to bacterial and fungal assembly across spatial scales and through time, we used 16 s rRNA gene and ITS sequencing in the soil of an emblematic wine-growing region of Italy.Combining null- and neutral-modelling, we found that assembly processes were consistent through time, but bacteria and fungi were governed by different processes. At the within-vineyard scale, deterministic selection and homogenising dispersal dominated bacterial assembly, while neither selection nor dispersal had clear influence over fungal assembly. At the among-vineyard scale, the influence of dispersal limitation increased for both taxonomic groups, but its contribution was much larger for fungal communities. These null-model-based inferences were supported by neutral modelling, which estimated a dispersal rate almost two orders-of-magnitude lower for fungi than bacteria.This indicates that while stochastic processes are important for fungal assembly, bacteria were more influenced by deterministic selection imposed by the biotic and/or abiotic environment. Managing microbes in vineyard soils could thus benefit from strategies that account for dispersal limitation of fungi and the importance of environmental conditions for bacteria. Our results are consistent with theoretical expectations whereby larger individual size and smaller populations can lead to higher levels of stochasticity.

Stochastic dynamics of an SIS epidemic on networks

We derive a stochastic SIS pairwise model by considering the change of the variables of this system caused by an event. Based on approximations, we construct a low-dimensional deterministic system that can be used to describe the epidemic spread on a regular network. The mathematical treatment of the model yields explicit expressions for the variances of each variable at equilibrium. Then a comparison between the stochastic pairwise model and the stochastic mean-field SIS model is performed to indicate the effect of network structure. We find that the variances of the prevalence of infection for these two models are almost equal when the number of neighbors of every individual is large. Furthermore, approximations for the quasi-stationary distribution of the number of infected individuals and the expected time to extinction starting in quasi-stationary are derived. We analyze the approximations for the critical number of neighbors and the persistence threshold based on the stochastic model. The approximate performance is then examined by numerical and stochastic simulations. Moreover, during the early development phase, the temporal variance of the infection is also obtained. The simulations show that our analytical results are asymptotically accurate and reasonable.

Transcriptional repression in stochastic gene expression, patterning, and cell fate specification

Development is often driven by signaling and lineage-specific cues, yielding highly uniform and reproducible outcomes. Development also involves mechanisms that generate noise in gene expression and random patterns across tissues. Cells sometimes randomly choose between two or more cell fates in a mechanism called stochastic cell fate specification. This process diversifies cell types in otherwise homogenous tissues. Stochastic mechanisms have been extensively studied in prokaryotes where noisy gene activation plays a pivotal role in controlling cell fates. In eukaryotes, transcriptional repression stochastically limits gene expression to generate random patterns and specify cell fates. Here, we review our current understanding of repressive mechanisms that produce random patterns of gene expression and cell fates in flies, plants, mice, and humans.

A mathematical model which examines age-related stochastic fluctuations in DNA maintenance methylation

Due to its complexity and its ubiquitous nature the ageing process remains an enduring biological puzzle. Many molecular mechanisms and biochemical process have become synonymous with ageing. However, recent findings have pinpointed epigenetics as having a key role in ageing and healthspan. In particular age related changes to DNA methylation offer the possibility of monitoring the trajectory of biological ageing and could even be used to predict the onset of diseases such as cancer, Alzheimer's disease and cardiovascular disease. At the molecular level emerging evidence strongly suggests the regulatory processes which govern DNA methylation are subject to intracellular stochasticity. It is challenging to fully understand the impact of stochasticity on DNA methylation levels at the molecular level experimentally. An ideal solution is to use mathematical models to capture the essence of the stochasticity and its outcomes. In this paper we present a novel stochastic model which accounts for specific methylation levels within a gene promoter. Uncertainty of the eventual site-specific methylation levels for different values of methylation age, depending on the initial methylation levels were analysed. Our model predicts the observed bistable levels in CpG islands. In addition, simulations with various levels of noise indicate that uncertainty predominantly spreads through the hypermethylated region of stability, especially for large values of input noise. A key outcome of the model is that CpG islands with high to intermediate methylation levels tend to be more susceptible to dramatic DNA methylation changes due to increasing methylation age.

Tipping Cascades in a Multi-patch System with Noise and Spatial Coupling

Forecasting tipping points in spatially extended systems is a key area of interest to ecologists. A slowly declining spatially distributed population is an important example of an ecological system that could exhibit a cascade of tipping points. Here, we develop a spatial two-patch model with environmental stochasticity that is slowly forced through population collapse, in the presence of changing environmental conditions. We begin with a basic spatial model, then introduce a fast-slow version of the model using geometric singular perturbation theory, followed by the inclusion of stochasticity. Using the spectral density of the fluctuating subpopulation in each patch, we derive analytic expressions for candidate indicators of population extinction and evaluate their performance through a simulation study. We find that coupling and spatial heterogeneity decrease the magnitude of the proposed indicators in coupled populations relative to isolated populations. Moreover, the degree of coupling dictates the trends in summary statistics. We conclude that this theory may be applied to other contexts, including the control of invasive species.

Does Stochasticity Favour Complexity in a Prebiotic Peptide-Micelle System?

A primordial environment that hosted complex pre- or proto-biochemical activity would have been subject to random fluctuations. A relevant question is then: What might be the optimum variance of such fluctuations, such that net progress could be made towards a living system? Since lipid-based membrane encapsulation was undoubtedly a key step in chemical evolution, we used a peptide-micelle system in simulated experiments where simple micelles and peptide-stabilized micelles compete for the same amphiphilic lipid substrate. As cyclic thermal driver and energy source we used a thermochemical redox oscillator, to which the micelle reactions are coupled thermally through the activation energies. The long-time series averages taken for increasing values of the fluctuation variance show two distinct minima for simple micelles, but are smoothly increasing for complex micelles. This result suggests that the fluctuation variance is an important parameter in developing and perpetuating complexity. We hypothesize that such an environment may be self-selecting for a complex, evolving chemical system to outcompete simple or parasitic molecular structures.

Assessing excess mortality in times of pandemics based on principal component analysis of weekly mortality data-the case of COVID-19

The COVID-19 outbreak has called for renewed attention to the need for sound statistical analyses to monitor mortality patterns and trends over time. Excess mortality has been suggested as the most appropriate indicator to measure the overall burden of the pandemic in terms of mortality. As such, excess mortality has received considerable interest since the outbreak of COVID-19 began. Previous approaches to estimate excess mortality are somewhat limited, as they do not include sufficiently long-term trends, correlations among different demographic and geographic groups, or autocorrelations in the mortality time series. This might lead to biased estimates of excess mortality, as random mortality fluctuations may be misinterpreted as excess mortality. We propose a novel approach that overcomes the named limitations and draws a more realistic picture of excess mortality. Our approach is based on an established forecasting model that is used in demography, namely, the Lee-Carter model. We illustrate our approach by using the weekly age- and sex-specific mortality data for 19 countries and the current COVID-19 pandemic as a case study. Our findings show evidence of considerable excess mortality during 2020 in Europe, which affects different countries, age, and sex groups heterogeneously. Our proposed model can be applied to future pandemics as well as to monitor excess mortality from specific causes of death.

Elementary effects analysis of factors controlling COVID-19 infections in computational simulation reveals the importance of social distancing and mask usage

COVID-19 was declared a pandemic by the World Health Organisation (WHO) on March 11th, 2020. With half of the world's countries in lockdown as of April due to this pandemic, monitoring and understanding the spread of the virus and infection rates and how these factors relate to behavioural and societal parameters is crucial for developing control strategies. This paper aims to investigate the effectiveness of masks, social distancing, lockdown and self-isolation for reducing the spread of SARS-CoV-2 infections. Our findings from an agent-based simulation modelling showed that whilst requiring a lockdown is widely believed to be the most efficient method to quickly reduce infection numbers, the practice of social distancing and the usage of surgical masks can potentially be more effective than requiring a lockdown. Our multivariate analysis of simulation results using the Morris Elementary Effects Method suggests that if a sufficient proportion of the population uses surgical masks and follows social distancing regulations, then SARS-CoV-2 infections can be controlled without requiring a lockdown.

Developmental stochasticity and variation in floral phyllotaxis

Floral phyllotaxis is a relatively robust phenotype; trimerous and pentamerous arrangements are widely observed in monocots and core eudicots. Conversely, it also shows variability in some angiosperm clades such as 'ANA' grade (Amborellales, Nymphaeales, and Austrobaileyales), magnoliids, and Ranunculales. Regardless of the phylogenetic relationship, however, phyllotactic pattern formation appears to be a common process. What are the causes of the variability in floral phyllotaxis and how has the variation of floral phyllotaxis contributed to floral diversity? In this review, I summarize recent progress in studies on two related fields to develop answers to these questions. First, it is known that molecular and cellular stochasticity are inevitably found in biological systems, including plant development. Organisms deal with molecular stochasticity in several ways, such as dampening noise through gene networks or maintaining function through cellular redundancy. Recent studies on molecular and cellular stochasticity suggest that stochasticity is not always detrimental to plants and that it is also essential in development. Second, studies on vegetative and inflorescence phyllotaxis have shown that plants often exhibit variability and flexibility in phenotypes. Three types of phyllotaxis variations are observed, namely, fluctuation around the mean, transition between regular patterns, and a transient irregular organ arrangement called permutation. Computer models have demonstrated that stochasticity in the phyllotactic pattern formation plays a role in pattern transitions and irregularities. Variations are also found in the number and positioning of floral organs, although it is not known whether such variations provide any functional advantages. Two ways of diversification may be involved in angiosperm floral evolution: precise regulation of organ position and identity that leads to further specialization of organs and organ redundancy that leads to flexibility in floral phyllotaxis.

Noise effect on the temporal patterns of neural synchrony

Neural synchrony in the brain is often present in an intermittent fashion, i.e., there are intervals of synchronized activity interspersed with intervals of desynchronized activity. A series of experimental studies showed that this kind of temporal patterning of neural synchronization may be very specific and may be correlated with behaviour (even if the average synchrony strength is not changed). Prior studies showed that a network with many short desynchronized intervals may be functionally different from a network with few long desynchronized intervals as it may be more sensitive to synchronizing input signals. In this study, we investigated the effect of channel noise on the temporal patterns of neural synchronization. We employed a small network of conductance-based model neurons that were mutually connected via excitatory synapses. The resulting dynamics of the network was studied using the same time-series analysis methods as used in prior experimental and computational studies. While it is well known that synchrony strength generally degrades with noise, we found that noise also affects the temporal patterning of synchrony. Noise, at a sufficient intensity (yet too weak to substantially affect synchrony strength), promotes dynamics with predominantly short (although potentially very numerous) desynchronizations. Thus, channel noise may be one of the mechanisms contributing to the short desynchronization dynamics observed in multiple experimental studies.

Stochasticity versus determinism: Microbial community assembly patterns under specific conditions in petrochemical activated sludge

The pattern of microbial community assembly in petrochemical sludge is not well-explained. In this study, three kinds of petrochemical activated sludge (AS) from the same seed sludge were investigated to determine their microbial assembly pattern for long-term adaptation. Beta Nearest Taxon Index analysis revealed that the assembly strategies of the abundant and rare operational taxonomic unit (OTU) sub-communities are different for archaeal and bacterial communities. Abundant OTUs preferred deterministic processes, whereas rare OTUs randomly formed due to weak selection. Canonical correspondence analysis/variation partition analysis and Mantel testing results revealed that ammonium, petroleum, and chromium (Cr (VI)) mainly structured the abundant sub-communities. On the other hand, environmental variables, including ammonium, petroleum, and heavy metals, shaped the rare sub-communities. The PICRUSt2 tool was used to predict the functions. Results indicated a greater abundance of microbes harboring the hydrocarbon degradation pathway and heavy-metal-resistant enzymes. Cross-treatment experiments using one type of AS to treat the other two kinds of wastewater were conducted. The results of the cross-treatment experiments and qPCR both suggest the functional adaptation of the microbial community. We revealed selection strategies for the adaptation of bacteria and archaea in AS during environmental changes, providing a theoretical basis for petrochemical wastewater treatment.

The Role of Stochasticity in Noise-Induced Tipping Point Cascades: A Master Equation Approach

Tipping points have been shown to be ubiquitous, both in models and empirically in a range of physical and biological systems. The question of how tipping points cascade through systems has been less explored and is an important one. A study of noise-induced tipping, in particular, could provide key insights into tipping cascades. Here, we consider a specific example of a simple model system that could have cascading tipping points. This model consists of two interacting populations with underlying Allee effects and stochastic dynamics, in separate patches connected by dispersal, which can generate bistability. From an ecological standpoint, we look for rescue effects whereby one population can prevent the collapse of a second population. As a way to investigate the stochastic dynamics, we use an individual-based modeling approach rooted in chemical reaction network theory. Then, using continuous-time Markov chains and the theory of first passage times, we essentially approximate, or emulate, the original high-dimensional model by a Markov chain with just three states, where each state corresponds to a combination of population thresholds. Analysis of this reduced model shows when the system is likely to recover, as well as when tipping cascades through the whole system.

Embracing Noise in Chemical Reaction Networks

We provide a short review of stochastic modeling in chemical reaction networks for mathematical and quantitative biologists. We use as case studies two publications appearing in this issue of the Bulletin, on the modeling of quasi-steady-state approximations and cell polarity. Reasons for the relevance of stochastic modeling are described along with some common differences between stochastic and deterministic models.

Lake water-level fluctuation forecasting using machine learning models: a systematic review

Lake water-level fluctuation is a complex and dynamic process, characterized by high stochasticity and nonlinearity, and difficult to model and forecast. In recent years, applications of machine learning (ML) models have yielded substantial progress in forecasting lake water-level fluctuations. This paper presents a comprehensive review of the applications of ML models for modeling water-level dynamics in lakes. Among the many existing ML models, seven popular ML model types are reviewed: (1) artificial neural network (ANN); (2) support vector machine (SVM); (3) artificial neuro-fuzzy inference system (ANFIS); (4) hybrid models, such as hybrid wavelet-artificial neural network (WA-ANN) model, hybrid wavelet-artificial neuro-fuzzy inference system (WA-ANFIS) model, and hybrid wavelet-support vector machine (WA-SVM) model; (5) evolutionary models, such as gene expression programming (GEP) and genetic programming (GP); (6) extreme learning machine (ELM); and (7) deep learning (DL). Model inputs, data split, model performance criteria, and model inter-comparison as well as the associated issues are discussed. The advantages and limitations of the established ML models are also discussed. Some specific directions for future research are also offered. This review provides a new vision for hydrologists and water resources planners for sustainable management of lakes.

CGGBP1-regulated cytosine methylation at CTCF-binding motifs resists stochasticity

Background

The human CGGBP1 binds to GC-rich regions and interspersed repeats, maintains homeostasis of stochastic cytosine methylation and determines DNA-binding of CTCF. Interdependence between regulation of cytosine methylation and CTCF occupancy by CGGBP1 remains unknown.

Results

By analyzing methylated DNA-sequencing data obtained from CGGBP1-depleted cells, we report that some transcription factor-binding sites, including CTCF, resist stochastic changes in cytosine methylation. By analysing CTCF-binding sites we show that cytosine methylation changes at CTCF motifs caused by CGGBP1 depletion resist stochastic changes. These CTCF-binding sites are positioned at locations where the spread of cytosine methylation in cis depends on the levels of CGGBP1.

Conclusion

Our findings suggest that CTCF occupancy and functions are determined by CGGBP1-regulated cytosine methylation patterns.

Stochasticity and determinism in cell fate decisions

During development, cells need to make decisions about their fate in order to ensure that the correct numbers and types of cells are established at the correct time and place in the embryo. Such cell fate decisions are often classified as deterministic or stochastic. However, although these terms are clearly defined in a mathematical sense, they are sometimes used ambiguously in biological contexts. Here, we provide some suggestions on how to clarify the definitions and usage of the terms stochastic and deterministic in biological experiments. We discuss the frameworks within which such clear definitions make sense and highlight when certain ambiguity prevails. As an example, we examine how these terms are used in studies of neuronal cell fate decisions and point out areas in which definitions and interpretations have changed and matured over time. We hope that this Review will provide some clarification and inspire discussion on the use of terminology in relation to fate decisions.

Stochastic dynamics in a time-delayed model for autoimmunity

In this paper we study interactions between stochasticity and time delays in the dynamics of immune response to viral infections, with particular interest in the onset and development of autoimmune response. Starting with a deterministic time-delayed model of immune response to infection, which includes cytokines and T cells with different activation thresholds, we derive an exact delayed chemical master equation for the probability density. We use system size expansion and linear noise approximation to explore how variance and coherence of stochastic oscillations depend on parameters, and to show that stochastic oscillations become more regular when regulatory T cells become more effective at clearing autoreactive T cells. Reformulating the model as an Itô stochastic delay differential equation, we perform numerical simulations to illustrate the dynamics of the model and associated probability distributions in different parameter regimes. The results suggest that even in cases where the deterministic model has stable steady states, in individual stochastic realisations, the model can exhibit sustained stochastic oscillations, whose variance increases as one gets closer to the deterministic stability boundary. Furthermore, in the regime of bi-stability, whereas deterministically the system would approach one of the steady states (or periodic solutions) depending on the initial conditions, due to the presence of stochasticity, it is now possible for the system to reach both of those dynamical states with certain probability. Biological significance of this result lies in highlighting the fact that since normally in a laboratory or clinical setting one would observe a single individual realisation of the course of the disease, even for all parameters characterising the immune system and the strength of infection being the same, there is a proportion of cases where a spontaneous recovery can be observed, and similarly, where a disease can develop in a situation that otherwise would result in a normal disease clearance.

Improvement of the memory function of a mutual repression network in a stochastic environment by negative autoregulation

BACKGROUND

Cellular memory is a ubiquitous function of biological systems. By generating a sustained response to a transient inductive stimulus, often due to bistability, memory is central to the robust control of many important biological processes. However, our understanding of the origins of cellular memory remains incomplete. Stochastic fluctuations that are inherent to most biological systems have been shown to hamper memory function. Yet, how stochasticity changes the behavior of genetic circuits is generally not clear from a deterministic analysis of the network alone. Here, we apply deterministic rate equations, stochastic simulations, and theoretical analyses of Fokker-Planck equations to investigate how intrinsic noise affects the memory function in a mutual repression network.

RESULTS

We find that the addition of negative autoregulation improves the persistence of memory in a small gene regulatory network by reducing stochastic fluctuations. Our theoretical analyses reveal that this improved memory function stems from an increased stability of the steady states of the system. Moreover, we show how the tuning of critical network parameters can further enhance memory.

CONCLUSIONS

Our work illuminates the power of stochastic and theoretical approaches to understanding biological circuits, and the importance of considering stochasticity when designing synthetic circuits with memory function.

Untangling the assembly of macrophyte metacommunities by means of taxonomic, functional and phylogenetic beta diversity patterns

Metacommunity ecology has broadened considerably with the recognition that measuring beta diversity beyond the purely taxonomic viewpoint may improve our understanding of the dispersal- and niche-based mechanisms across biological communities. In that perspective, we applied a novel multidimensional approach including taxonomic, functional and phylogenetic data to enhance our basic understanding of macrophyte metacommunity dynamics. For each beta diversity metric, we calculated the mean overall value and tested whether the mean value was different from that expected by chance using null models. We also employed evolutionary and spatially constrained models to first identify the degree to which the studied functional traits showed a phylogenetic signal, and then to estimate the relative importance of spatial and environmental effects on metacommunity structure. We first found that most individual ponds were inhabited by species that were merely random draws from the taxonomic and phylogenetic species pool available in the study region. Contrary to our expectations, not all measured traits were conserved along the phylogeny. We also showed that trait and phylogenetic dimensions strongly increased the amount of variation in beta diversity that can be explained by degree of environmental filtering and dispersal limitation. This suggests that accounting for functional traits and phylogeny in metacommunity ecology helps to explain idiosyncratic patterns of variation in macrophyte species distribution. Importantly, phylogenetic and functional analyses identified the influence of underlying mechanisms that would otherwise be missed in an analysis of taxonomic turnover. Together, these results let us conclude that macrophyte species have labile functional traits adapted to dispersal-based processes and some evolutionary trade-offs that drive community assembly via species sorting. Overall, our exploration of different facets of beta diversity showed how functional and phylogenetic information may be used with species-level data to test community assembly hypotheses that are more ecologically meaningful than assessments of environmental patterns based on the purely taxonomic viewpoint.

Is the cell really a machine?

It has become customary to conceptualize the living cell as an intricate piece of machinery, different to a man-made machine only in terms of its superior complexity. This familiar understanding grounds the conviction that a cell's organization can be explained reductionistically, as well as the idea that its molecular pathways can be construed as deterministic circuits. The machine conception of the cell owes a great deal of its success to the methods traditionally used in molecular biology. However, the recent introduction of novel experimental techniques capable of tracking individual molecules within cells in real time is leading to the rapid accumulation of data that are inconsistent with an engineering view of the cell. This paper examines four major domains of current research in which the challenges to the machine conception of the cell are particularly pronounced: cellular architecture, protein complexes, intracellular transport, and cellular behaviour. It argues that a new theoretical understanding of the cell is emerging from the study of these phenomena which emphasizes the dynamic, self-organizing nature of its constitution, the fluidity and plasticity of its components, and the stochasticity and non-linearity of its underlying processes.

Cyclical and stochastic thermal variability affects survival and growth in brook trout

Directional changes in temperature have well-documented effects on ectotherms, yet few studies have explored how increased thermal variability (a concomitant of climate change) might affect individual fitness. Using a common-garden experimental protocol, we investigated how bidirectional temperature change can affect survival and growth of brook trout (Salvelinus fontinalis) and whether the survival and growth responses differ between two populations, using four thermal-variability treatments (mean: 10 °C; range: 7-13 °C): (i) constancy; (ii) cyclical fluctuations every two days; (iii) low stochasticity (random changes every 2 days); (iv) high stochasticity (random changes daily). Recently hatched individuals were monitored under thermal variability (6 weeks) and a subsequent one-month period of thermal constancy. We found that variability can positively influence survival, relative to thermal constancy, but negatively affect growth. The observations reported here can be interpreted within the context of Jensen's Inequality (performance at average conditions is unequal to average performance across a range of conditions). Projections of future population viability in the context of climate change would be strengthened by increased experimental attention to the fitness consequences of stochastic and non-stochastic thermal variability.

Is the cell really a machine?

It has become customary to conceptualize the living cell as an intricate piece of machinery, different to a man-made machine only in terms of its superior complexity. This familiar understanding grounds the conviction that a cell's organization can be explained reductionistically, as well as the idea that its molecular pathways can be construed as deterministic circuits. The machine conception of the cell owes a great deal of its success to the methods traditionally used in molecular biology. However, the recent introduction of novel experimental techniques capable of tracking individual molecules within cells in real time is leading to the rapid accumulation of data that are inconsistent with an engineering view of the cell. This paper examines four major domains of current research in which the challenges to the machine conception of the cell are particularly pronounced: cellular architecture, protein complexes, intracellular transport, and cellular behaviour. It argues that a new theoretical understanding of the cell is emerging from the study of these phenomena which emphasizes the dynamic, self-organizing nature of its constitution, the fluidity and plasticity of its components, and the stochasticity and non-linearity of its underlying processes.

Efficient simulation of thermally fluctuating biopolymers immersed in fluids on 1-micron, 1-second scales

The combination of fluid-structure interactions with stochasticity, due to thermal fluctuations, remains a challenging problem in computational fluid dynamics. We develop an efficient scheme based on the stochastic immersed boundary method, Stokeslets, and multiple timestepping. We test our method for spherical particles and filaments under purely thermal and deterministic forces and find good agreement with theoretical predictions for Brownian Motion of a particle and equilibrium thermal undulations of a semi-flexible filament. As an initial application, we simulate bio-filaments with the properties of F-actin. We specifically study the average time for two nearby parallel filaments to bundle together. Interestingly, we find a two-fold acceleration in this time between simulations that account for long-range hydrodynamics compared to those that do not, suggesting that our method will reveal significant hydrodynamic effects in biological phenomena.

Modeling Gene Networks to Understand Multistability in Stem Cells

Stem cells are unique in their ability to differentiate into diverse phenotypes capable of displaying radically different, yet stable, gene expression profiles. Understanding this multistable behavior is key to rationally influencing stem cell differentiation for both research and therapeutic purposes. To this end, mathematical paradigms have been adopted to simulate and explain the dynamics of complex gene networks. In this chapter, we introduce strategies for building deterministic and stochastic mathematical models of gene expression and demonstrate how analysis of these models can benefit our understanding of complex observed behaviors. Developing a mathematical understanding of biological processes is of utmost importance in understanding and controlling stem cell behavior.

Coding and decoding of oscillatory Ca2+ signals

About 30 years after their first observation, Ca²⁺ oscillations are now recognised as a universal mechanism of signal transduction. These oscillations are driven by periodic cycles of release and uptake of Ca²⁺ between the cytoplasm and the endoplasmic reticulum. Their frequency often increases with the level of stimulation, which can be decoded by some molecules. However, it is becoming increasingly evident that the widespread core oscillatory mechanism is modulated in many ways, depending on the cell type and on the physiological conditions. Interplay with inositol 1,4,5-trisphosphate metabolism and with other Ca²⁺ stores as the extracellular medium or mitochondria can much affect the properties of these oscillations. In many cases, these finely tuned characteristics of Ca²⁺ oscillations impact the physiological response that is triggered by the signal. Moreover, oscillations are intrinsically irregular. This randomness can also be exploited by the cell. In this review, we discuss evidences of these additional manifestations of the versatility of Ca²⁺ signalling.

Links between evolutionary processes and phenotypic robustness in microbes

The costs and benefits of random phenotypic heterogeneity in microbes have been vigorously debated and experimental tested for decades; yet, this conversation is largely independent from discussion of phenotypic robustness in other disciplines. In this review I connect microbial examples of stochasticity with studies on the ecological and population-genetic consequences of phenotypic variability. These topics illustrate the complexity of selection pressures on phenotypic robustness and provide inspiration that this complexity can be parsed with theoretical advances and the experimental power of microbial systems.

Model selection for integrated pest management with stochasticity

In Song and Xiang (2006), an integrated pest management model with periodically varying climatic conditions was introduced. In order to address a wider range of environmental effects, the authors here have embarked upon a series of studies resulting in a more flexible modeling approach. In Akman et al. (2013), the impact of randomly changing environmental conditions is examined by incorporating stochasticity into the birth pulse of the prey species. In Akman et al. (2014), the authors introduce a class of models via a mixture of two birth-pulse terms and determined conditions for the global and local asymptotic stability of the pest eradication solution. With this work, the authors unify the stochastic and mixture model components to create further flexibility in modeling the impacts of random environmental changes on an integrated pest management system. In particular, we first determine the conditions under which solutions of our deterministic mixture model are permanent. We then analyze the stochastic model to find the optimal value of the mixing parameter that minimizes the variance in the efficacy of the pesticide. Additionally, we perform a sensitivity analysis to show that the corresponding pesticide efficacy determined by this optimization technique is indeed robust. Through numerical simulations we show that permanence can be preserved in our stochastic model. Our study of the stochastic version of the model indicates that our results on the deterministic model provide informative conclusions about the behavior of the stochastic model.

Modeled auditory nerve responses to amplitude modulated cochlear implant stimulation

Cochlear implants encode speech information by stimulating the auditory nerve with amplitude-modulated pulse trains. A computer model of the auditory nerve's response to electrical stimulation can be used to evaluate different approaches to improving CI patients' perception. In this paper a computationally efficient stochastic and adaptive auditory nerve model was used to investigate full nerve responses to amplitude-modulated electrical pulse trains. The model was validated for nerve responses to AM pulse trains via comparison with animal data. The influence of different parameters, such as adaptation and stochasticity, on long-term adaptation and modulation-following behavior was investigated. Responses to pulse trains with different pulse amplitudes, amplitude modulation frequencies, and modulation depths were modeled. Rate responses as well as period histograms, Vector Strength and the fundamental frequency were characterized in different time bins. The response alterations, including frequency following behavior, observed over the stimulus duration were similar to those seen in animal experiments. The tested model can be used to predict complete nerve responses to arbitrary input, and thus to different sound coding strategies.

Brief temperature stress during reproductive stages alters meiotic recombination and somatic mutation rates in the progeny of Arabidopsis

BACKGROUND

Plants exposed to environmental stresses draw upon many genetic and epigenetic strategies, with the former sometimes modulated by the latter. This can help the plant, and its immediate progeny, at least, to better endure the stress. Some evidence has led to proposals that (epi) genetic changes can be both selective and sustainably heritable, while other evidence suggests that changes are effectively stochastic, and important only because they induce genetic variation. One type of stress with an arguably high level of stochasticity in its effects is temperature stress. Studies of how heat and cold affect the rates of meiotic recombination (MR) and somatic mutations (SMs, which are potentially heritable in plants) report increases, decreases, or no effect. Collectively, they do not point to any consistent patterns. Some of this variability, however, might arise from the stress being applied for such an extended time, typically days or weeks. Here, we adopted a targeted approach by (1) limiting exposure to one hour; and (2) timing it to coincide with (a) gamete, and early gametophyte, development, a period of high stress sensitivity; and (b) a late stage of vegetative development.

RESULTS

For plants (Arabidopsis thaliana) otherwise grown at 22 °C, we measured the effects of a 1 h exposure to cold (12 °C) or heat (32 °C) on the rates of MR, and four types of SMs (frameshift mutations; intrachromosomal recombination; base substitutions; transpositions) in the F1 progeny. One parent (wild type) was stressed, the other (unstressed) carried a genetic event detector. When rates were compared to those in progeny of control (both parents unstressed) two patterns emerged. In the progeny of younger plants (stressed at 36 days; pollinated at 40 days) heat and cold either had no effect (on MR) or (for SMs) had effects that were rare and stochastic. In the progeny of older plants (stressed at 41 days; pollinated at 45 days), while effects were also infrequent, those that were seen followed a consistent pattern: rates of all five genetic events were lowest at 12 °C and highest at 32 °C, i.e. they varied in a "dose-response" manner. This pattern was strongest (or, in the case of MR, only apparent) in progeny whose stressed parent was female.

CONCLUSION

While the infrequency of effects suggests the need for cautious inference, the consistency of responses in the progeny of older plants, indicate that in some circumstances the level of stochasticity in inherited genetic responses to heat or cold stress can be context-dependent, possibly reflecting life-cycle stages in the parental generation that are variably stress sensitive.

Distinct succession patterns of abundant and rare bacteria in temporal microcosms with pollutants

Elucidating the driving forces behind the temporal dynamics of abundant and rare microbes is essential for understanding the assembly and succession of microbial communities. Here, we explored the successional trajectories and mechanisms of abundant and rare bacteria via soil-enrichment subcultures in response to various pollutants (phenanthrene, n-octadecane, and CdCl₂) using time-series Illumina sequencing datasets. The results reveal different successional patterns of abundant and rare sub-communities in eighty pollutant-degrading consortia and two original soil samples. A temporal decrease in α-diversity and high turnover rate for β-diversity indicate that deterministic processes are the main drivers of the succession of the abundant sub-community; however, the high cumulative species richness indicates that stochastic processes drive the succession of the rare sub-community. A functional prediction showed that abundant bacteria contribute primary functions to the pollutant-degrading consortia, such as amino acid metabolism, cellular responses to stress, and hydrocarbon degradation. Meanwhile, rare bacteria contribute a substantial fraction of auxiliary functions, such as carbohydrate-active enzymes, fermentation, and homoacetogenesis, which indicates their roles as a source of functional diversity. Our study suggests that the temporal succession of microbes in polluted microcosms is mainly associated with abundant bacteria rather than the high proportion of rare taxa. The major forces (i.e., stochastic or deterministic processes) driving microbial succession could be dependent on the low- or high-abundance community members in temporal microcosms with pollutants.

Stochasticity of gene expression as a motor of epigenetics in bacteria: from individual to collective behaviors

Measuring gene expression at the single cell and single molecule level has recently made possible the quantitative measurement of stochasticity of gene expression. This enables identification of the probable sources and roles of noise. Gene expression noise can result in bacterial population heterogeneity, offering specific advantages for fitness and survival in various environments. This trait is therefore selected during the evolution of the species, and is consequently regulated by a specific genetic network architecture. Examples exist in stress-response mechanisms, as well as in infection and pathogenicity strategies, pointing to advantages for multicellularity of bacterial populations.

Allele-specific analysis of cell fusion-mediated pluripotent reprograming reveals distinct and predictive susceptibilities of human X-linked genes to reactivation

BACKGROUND

Inactivation of one X chromosome is established early in female mammalian development and can be reversed in vivo and in vitro when pluripotency factors are re-expressed. The extent of reactivation along the inactive X chromosome (Xi) and the determinants of locus susceptibility are, however, poorly understood. Here we use cell fusion-mediated pluripotent reprograming to study human Xi reactivation and allele-specific single nucleotide polymorphisms (SNPs) to identify reactivated loci.

RESULTS

We show that a subset of human Xi genes is rapidly reactivated upon re-expression of the pluripotency network. These genes lie within the most evolutionary recent segments of the human X chromosome that are depleted of LINE1 and enriched for SINE elements, predicted to impair XIST spreading. Interestingly, this cadre of genes displays stochastic Xi expression in human fibroblasts ahead of reprograming. This stochastic variability is evident between clones, by RNA-sequencing, and at the single-cell level, by RNA-FISH, and is not attributable to differences in repressive histone H3K9me3 or H3K27me3 levels. Treatment with the DNA demethylating agent 5-deoxy-azacytidine does not increase Xi expression ahead of reprograming, but instead reveals a second cadre of genes that only become susceptible to reactivation upon induction of pluripotency.

CONCLUSIONS

Collectively, these data not only underscore the multiple pathways that contribute to maintaining silencing along the human Xi chromosome but also suggest that transcriptional stochasticity among human cells could be useful for predicting and engineering epigenetic strategies to achieve locus-specific or domain-specific human Xi gene reactivation.