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Introduction: Genetic improvement in rice increased yield potential and improved varieties for farmers over the last decades. However, the demand for rice is growing while its cultivation faces challenges posed by climate change. To address these challenges, rice breeding programs need to adopt efficient breeding strategies to provide a steady increase in the rate of genetic gain for major traits. The International Rice Research Institute (IRRI) breeding program has evolved over time to implement faster and more efficient breeding techniques such as rapid generation advance (RGA) and genomic selection (GS). Simulation experiments support data-driven optimization of the breeding program toward the desired rate of genetic gain for key traits. Methods: This study used stochastic simulations to compare breeding schemes with different cycle times. The objective was to assess the impact of different genomic selection strategies on medium- and long-term genetic gain. Four genomic selection schemes were simulated, representing the past approaches (5 years recycling), current schemes (3 years recycling), and two options for the future schemes (both with 2 years recycling). Results: The 2-Year within-cohort prediction scheme showed a significant increase in genetic gain in the medium-term horizon. Specifically, it resulted in a 22%, 24%, and 27% increase over the current scheme in the zero, intermediate, and high genotype-by-environment interaction (GEI) contexts, respectively. On the other hand, the 2-Year scheme based on between-cohort prediction was more efficient in the long term, but only in the absence of GEI. Consistent with our expectations, the shortest breeding schemes showed an increase in genetic gain and faster depletion of genetic variance compared to the current scheme. Discussion: These results suggest that higher rates of genetic gain are achievable in the breeding program by further reducing the cycle time and adjusting the target population of environments. However, more attention is needed regarding the crossing strategy to use genetic variance optimally. [ABSTRACT FROM AUTHOR]

The population dynamics of early replicators has revealed numerous puzzles, highlighting the difficulty of transitioning from simple template-directed replicating molecules to complex biological systems. The resolution of these puzzles has set the research agenda on prebiotic evolution since the seminal works of Manfred Eigen in the 1970s. Here, we study the effects of demographic noise on the population dynamics of template-directed (non-enzymatic) and protein-mediated (enzymatic) replicators. We borrow stochastic algorithms from evolutionary game theory to simulate finite populations of two types of replicators. These algorithms recover the replicator equation framework in the infinite population limit. For large but finite populations, we use finite-size scaling to determine the probability of fixation and the mean time to fixation near a threshold that delimits the regions of dominance of each replicator type. Since enzyme-producing replicators cannot evolve in a well-mixed population containing replicators that benefit from the enzyme but do not encode it, we study the evolution of enzyme-producing replicators in a finite population structured in temporarily formed random groups of fixed size n. We argue that this problem is identical to the weak-altruism version of the n-player prisoner's dilemma, and show that the threshold is given by the condition that the reward for altruistic behavior is equal to its cost. [ABSTRACT FROM AUTHOR]

IntroductionGenetic improvement in rice increased yield potential and improved varieties for farmers over the last decades. However, the demand for rice is growing while its cultivation faces challenges posed by climate change. To address these challenges, rice breeding programs need to adopt efficient breeding strategies to provide a steady increase in the rate of genetic gain for major traits. The International Rice Research Institute (IRRI) breeding program has evolved over time to implement faster and more efficient breeding techniques such as rapid generation advance (RGA) and genomic selection (GS). Simulation experiments support data-driven optimization of the breeding program toward the desired rate of genetic gain for key traits. MethodsThis study used stochastic simulations to compare breeding schemes with different cycle times. The objective was to assess the impact of different genomic selection strategies on medium- and long-term genetic gain. Four genomic selection schemes were simulated, representing the past approaches (5 years recycling), current schemes (3 years recycling), and two options for the future schemes (both with 2 years recycling). ResultsThe 2-Year within-cohort prediction scheme showed a significant increase in genetic gain in the medium-term horizon. Specifically, it resulted in a 22%, 24%, and 27% increase over the current scheme in the zero, intermediate, and high genotype-by-environment interaction (GEI) contexts, respectively. On the other hand, the 2-Year scheme based on between-cohort prediction was more efficient in the long term, but only in the absence of GEI. Consistent with our expectations, the shortest breeding schemes showed an increase in genetic gain and faster depletion of genetic variance compared to the current scheme. DiscussionThese results suggest that higher rates of genetic gain are achievable in the breeding program by further reducing the cycle time and adjusting the target population of environments. However, more attention is needed regarding the crossing strategy to use genetic variance optimally.

Conditional Spectrum (CS), characterized by a conditional mean and conditional standard deviation, serves as a target spectrum linking seismic hazard information to ground motion selection for seismic demand analysis. Current Ground Motion Selection and Modification (GMSM) methods aim to align with this target spectrum but face challenges due to the limited availability of recorded ground motions and potential modifications, leading to difficulties in ensuring selected ground motions accurately reflect site characteristics. Addressing this issue, the study presents an algorithm to select hazard-consistent CS-based records utilizing a stochastic ground motion model with a dual objective: (1) alleviate the need for scaling during the selection process, and (2) select records consistent with the target hazard and the contributing causal scenarios. To achieve both objectives, the study generates a database of hazard-targeted ground motions utilizing the kriging surrogate with scenarios sampled from the disaggregation matrix. Then, a postprocessing step explicitly considers causative parameters in the selection process. The algorithm's potential to select site-specific ground motions is demonstrated using sites in the Western United States, providing insights into the computational cost and accuracy. Additionally, statistical comparisons are conducted to explore circumstances where similar hazard consistency can be achieved without the postprocessing step, reducing the overall computational cost. Finally, the selected ground motions are favorably compared to those selected through traditional conditional spectrum-based approaches based on their spectral shape and ground motion intensity measures. [ABSTRACT FROM AUTHOR]

Localized deformation and randomly shaped imperfections are salient features of buckling-type instabilities in thin-walled load-bearing structures. However, it is generally agreed that their complex interactions in response to mechanical loading are not yet sufficiently understood, as evidenced by buckling-induced catastrophic failures which continue to today. This study investigates how the intimate coupling between localization mechanisms and geometric imperfections combine to determine the statistics of the pressure required to buckle (the illustrative example of) a hemispherical shell. The geometric imperfections, in the form of a surface, are defined by a random field generated over the nominally hemispherical shell geometry, and the probability distribution of the buckling pressure is computed via stochastic finite element analysis. Monte-Carlo simulations are performed for a wide range of the shell's radius to thickness ratio, as well as the correlation length of the spatial distribution of the imperfection. The results show that over this range, the buckling pressure is captured by the Weibull distribution. In addition, the analyses of the deformation patterns observed during the simulations provide insights into the effects of certain characteristic lengths on the local buckling that triggers global instability. In light of the simulation results, a probabilistic model is developed for the statistics of the buckling load that reveals how the dimensionless radius plays a dual role which remained hidden in previous deterministic analyses. The implications of the present model for reliability-based design of shell structures are discussed. [ABSTRACT FROM AUTHOR]

Dengue is a mosquito-borne viral infection that triggers a series of intracellular events in the host immune system, which may result in an invasion of the virus into the host and cause illness with a spectrum of severity. Depending on the degree of the infection, mild to severe clinical symptoms appear when the T-cell and B-cell-initiated immune responses fail to eradicate the virus particles and subsequently become compromised. Here, we propose a mathematically tractable simple model that exhibits important biological features of dengue infection. Dynamical analysis of our model explores the factors influencing viral persistence in the body over an extended period. To investigate plausible variability in viral dynamics in different hosts, we perform stochastic simulations of our model using Gillespie's algorithm. Our simulation results recapitulate the distribution of the intrinsic incubation period, daily viral load, and the day of peak viremia. In addition, we observe that the invasion probability of the virus into the host is correlated with the initial virus population injected by the mosquito. However, considering the biting behavior of Aedes mosquitoes, a lower initial virus injection could end up increasing the epidemic potential of the virus. [ABSTRACT FROM AUTHOR]

Understanding the mechanistic basis of epigenetic memory has proven to be a difficult task due to the underlying complexity of the systems involved in its establishment and maintenance. Here, we review the role of computational modeling in helping to unlock this complexity, allowing the dissection of intricate feedback dynamics. We focus on three forms of epigenetic memory encoded in gene regulatory networks, DNA methylation, and histone modifications and discuss the important advantages offered by plant systems in their dissection. We summarize the main modeling approaches involved and highlight the principal conceptual advances that the modeling has enabled through iterative cycles of predictive modeling and experiments. Lastly, we discuss remaining gaps in our understanding and how intertwined theory and experimental approaches might help in their resolution. [ABSTRACT FROM AUTHOR]

The estimation of unknown parameters in simulations, also known as calibration, is crucial for practical management of epidemics and prediction of pandemic risk. A simple yet widely used approach is to estimate the parameters by minimising the sum of the squared distances between actual observations and simulation outputs. It is shown in this paper that this method is inefficient, particularly when the epidemic models are developed based on certain simplifications of reality, also known as imperfect models which are commonly used in practice. To address this issue, a new estimator is introduced that is asymptotically consistent, has a smaller estimation variance than the least-squares estimator, and achieves the semiparametric efficiency. Numerical studies are performed to examine the finite sample performance. The proposed method is applied to the analysis of the COVID-19 pandemic for 20 countries based on the susceptible-exposed-infectious-recovered model with both deterministic and stochastic simulations. The estimation of the parameters, including the basic reproduction number and the average incubation period, reveal the risk of disease outbreaks in each country and provide insights to the design of public health interventions. [ABSTRACT FROM AUTHOR]

The population dynamics of early replicators has revealed numerous puzzles, highlighting the difficulty of transitioning from simple template-directed replicating molecules to complex biological systems. The resolution of these puzzles has set the research agenda on prebiotic evolution since the seminal works of Manfred Eigen in the 1970s. Here, we study the effects of demographic noise on the population dynamics of template-directed (non-enzymatic) and protein-mediated (enzymatic) replicators. We borrow stochastic algorithms from evolutionary game theory to simulate finite populations of two types of replicators. These algorithms recover the replicator equation framework in the infinite population limit. For large but finite populations, we use finite-size scaling to determine the probability of fixation and the mean time to fixation near a threshold that delimits the regions of dominance of each replicator type. Since enzyme-producing replicators cannot evolve in a well-mixed population containing replicators that benefit from the enzyme but do not encode it, we study the evolution of enzyme-producing replicators in a finite population structured in temporarily formed random groups of fixed size n. We argue that this problem is identical to the weak-altruism version of the n-player prisoner’s dilemma, and show that the threshold is given by the condition that the reward for altruistic behavior is equal to its cost.

Spontaneous lipid vesiculation and related size distribution are traditionally studied in the framework of equilibrium thermodynamics and continuum mechanics, overlooking the kinetic aspects of the process. In the scenario of liposomes consisting of different lipid molecules dispersed in the same medium - a non-equilibrium situation -, the system evolves driven by lipid monomer transfer among the different liposomes. This process encompasses time-dependent changes in liposome size and size distribution, thus predicting size and composition at a given time would entail the control of the size of liposomes by kinetic means, an asset in the framework of diagnostics and synthetic biology. We introduce a direct transfer model, based on the fact that monomers are highly reactive species and apply it to saturated phospholipid molecules differing in hydrophobic chain length. Considering a well-defined gamma-type liposome size distribution, we demonstrate a clear liposome size-composition correlation and are able to predict liposome size and size distribution at any time in the transfer process. The size-composition correlation opens up new prospects for the control of the self-assembling properties of lipids and thereby the control of the liposome size. [ABSTRACT FROM AUTHOR]

ABSTRACT The paper evaluates the influence of size series, percentage of censored data, and coefficients of variation used to generate synthetic series on the estimation of means, standard deviations, coefficients of variation, and medians in series with censored data. Seven techniques were applied to treat censored data in synthetic series with 180 scenarios (four size series, nine censoring percentages and five coefficients of variation): values proportional to the DL: zero, DL/2, DL/20.5 and DL - and parametric (MLE), robust (ROS) and Kaplan-Meier methods. Predictions were analyzed with four performance metrics (MPE, MAPE, KGE, and RMSE). It is found that the percentage of censored data and the coefficient of variation significantly alter forecast quality. It is also found that substitution by DL/2, by DL/20.5 and ROS are the most appropriate techniques for estimating the variables described, emphasizing ROS when estimating parametric variables and substitution by DL/20.5 for medians.

We propose a machine learning framework for the data-driven discovery of macroscopic chemotactic Partial Differential Equations (PDEs)—and the closures that lead to them- from high-fidelity, individual-based stochastic simulations of Escherichia coli bacterial motility. The fine scale, chemomechanical, hybrid (continuum—Monte Carlo) simulation model embodies the underlying biophysics, and its parameters are informed from experimental observations of individual cells. Using a parsimonious set of collective observables, we learn effective, coarse-grained “Keller–Segel class” chemotactic PDEs using machine learning regressors: (a) (shallow) feedforward neural networks and (b) Gaussian Processes. The learned laws can be black-box (when no prior knowledge about the PDE law structure is assumed) or gray-box when parts of the equation (e.g. the pure diffusion part) is known and “hardwired” in the regression process. More importantly, we discuss data-driven corrections (both additive and functional), to analytically known, approximate closures. [ABSTRACT FROM AUTHOR]

The aim of the article is to verify the conceptual model of integrated optimization of a bank's value, which enables the integration of the risk management process with business processes while maintaining compromise between the safety (stability) of a bank's operations and striving to maximize its value. The model is an attempt at a comprehensive solution to such dilemmas as shaping a bank's value ex ante, not ex post. Verification of the model has shown that the model works in accordance with the adopted assumptions and leads to the achievement of the basic goal for which it was constructed. In practice, it means the possibility of ensuring a compromise between the safety and effectiveness of a bank's operations, which, in the context of ongoing changes in its environment, allows for a long-term competitive advantage. [ABSTRACT FROM AUTHOR]

Abstract Stochastic population dynamics simulations are essential for many ecological and epidemiological studies to generate time series and genealogies that capture the relatedness between individuals. Many software packages allow one to simulate phylogenetic trees but these tend to suffer from one or two major limitations. First, the underlying population dynamics model is often simplistic (e.g. constant population size or exponential growth). Second, the software packages are not appropriate to simulate a large number of trees. We introduce TiPS, an R package to generate trajectories and phylogenetic trees associated with a compartmental model. Trajectories are simulated using Gillespie's exact or approximate stochastic simulation algorithm, or a newly proposed mixed version of the two. Phylogenetic trees are simulated from a trajectory under a backwards‐in‐time approach (i.e. coalescent). TiPS is based on the Rcpp package, allowing to combine the flexibility of R for model definition and the speed of C++ for simulations execution. The model is defined in R with a set of reactions, which allow capturing heterogeneity in life cycles or any sort of population structure. TiPS converts the model into C++ code and compiles it into a simulator that is interfaced in R via a function. TiPS is flexible, easy‐to‐use and available on the CRAN at https://cran.r‐project.org/package=TiPS. Plus, benchmarking analyses show that TiPS is faster than existing packages. This package is particularly well suited for population genetics and phylodynamics studies that need to generate a large number of phylogenies used for population dynamics studies.

Owing to stochastic fluctuations arising from finite population size, known as genetic drift, the ability of a population to explore a rugged fitness landscape depends on its size. In the weak mutation regime, while the mean steady-state fitness increases with population size, we find that the height of the first fitness peak encountered when starting from a random genotype displays various behaviours versus population size, even among small and simple rugged landscapes. We show that the accessibility of the different fitness peaks is key to determining whether this height overall increases or decreases with population size. Furthermore, there is often a finite population size that maximizes the height of the first fitness peak encountered when starting from a random genotype. This holds across various classes of model rugged landscapes with sparse peaks, and in some experimental and experimentally inspired ones. Thus, early adaptation in rugged fitness landscapes can be more efficient and predictable for relatively small population sizes than in the large-size limit. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'. [ABSTRACT FROM AUTHOR]

Lindenmayer systems (L-systems) are a grammar system that consists of string rewriting rules. The rules replace every symbol in a string in parallel with a successor to produce the next string, and this procedure iterates. In a stochastic context-free L-system (S0L-system), every symbol may have one or more rewriting rule, each with an associated probability of selection. Properly constructed rewriting rules have been found to be useful for modeling and simulating some natural and human engineered processes where each derived string describes a step in the simulation. Typically, processes are modeled by experts who meticulously construct the rules based on measurements or domain knowledge of the process. This paper presents an automated approach to finding stochastic L-systems, given a set of string sequences as input. The implemented tool is called the Plant Model Inference Tool for S0L-systems or PMIT-S0L. PMIT-S0L is evaluated using 960 procedurally generated S0L-systems in a test suite, which are each used to generate input strings, and PMIT-S0L is then used to infer the system from only the sequences. The evaluation shows that PMIT-S0L infers S0L-systems with up to 9 rewriting rules each in under 12 hours. Additionally, it is found that 3 sequences of strings are sufficient to find the correct original rewriting rules in 100 % of the cases in the test suite, and 6 sequences of strings reduce the difference in the associated probabilities to approximately 1 % or less. [ABSTRACT FROM AUTHOR]

The underlying market trends that drive stock price fluctuations are often referred to in terms of bull and bear markets. Optimal stock portfolio selection methods need to take into account these market trends; however, the bull and bear market states tend to be unobserved and can only be assigned retrospectively. We fit a linked hidden Markov model (LHMM) to relative stock price changes for S&P 500 stocks from 2011–2016 based on weekly closing values. The LHMM consists of a multivariate state process whose individual components correspond to HMMs for each of the 12 sectors of the S&P 500 stocks. The state processes are linked using a Gaussian copula so that the states of the component chains are correlated at any given time point. The LHMM allows us to capture more heterogeneity in the underlying market dynamics for each sector. In this study, stock performances are evaluated in terms of capital gains using the LHMM by utilizing historical stock price data. Based on the fitted LHMM, optimal stock portfolios are constructed to maximize capital gain while balancing reward and risk. Under out-of-sample testing, the annual capital gain for the portfolios for 2016–2017 are calculated. Portfolios constructed using the LHMM are able to generate returns comparable to the S&P 500 index. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of high greenhouse gas emissions from oil and gas platforms in Norway. We look at the potential of integrating an energy system composed of wind turbines and battery systems to unload the electrical power generated by gas turbines being the main source of emissions today. We propose a simulation model of the energy system, the power demand, the available wind speed, and different control strategies. By putting the models together, we evaluate the performance of various compositions of the system and determine their impact on emissions and battery lifetime. The numerical results show that changing today's practices has great potential to reduce greenhouse gases, with amounts varying between 30% and 80% compared with today's level. [ABSTRACT FROM AUTHOR]

Dengue annually infects millions of people from a regionally and seasonally varying dengue virus population circulating as four distinct serotypes. Effective protection against dengue infection and disease requires tetravalent vaccine formulations to stimulate a balanced protective immune response to all four serotypes. However, this has been a challenge to achieve, and several clinical trials with different leading vaccine candidates have demonstrated unbalanced replication and interference of interindividual serotype components, leading to low efficacy and enhanced disease severity for dengue-naïve populations. Production of serotype-specific neutralizing antibodies is largely viewed as a correlate of protection against severe dengue disease. However, the underlying mechanisms that lead to these protective immune responses are not clearly elucidated. In this work, using a stochastic model of B cell affinity maturation, we tested different live-attenuated vaccine constructs with varied viral replication rates and contrasted the initiation and progress of adaptive immune responses during tetravalent vaccination and after dengue virus challenge. Comparison of our model simulations across different disease-severity levels suggested that individual production of high levels of serotype-specific antibodies together with a lower cross-reactive antibody are better correlates for protection. Furthermore, evolution of these serotype-specific antibodies was dependent on the percent of viral attenuation in the vaccine, and production of initial B cell and T cell populations pre- and post-secondary dengue infection was crucial in providing protective immunity for dengue-naïve populations. Furthermore, contrasting disease severity with respect to different dengue serotypes, our model simulations showed that tetravalent vaccines fare better against DENV-4 serotype when compared to other serotypes.

Although genetic mutations that alter organisms' average lifespans have been identified in aging research, our understanding of the dynamic changes during aging remains limited. Here, we integrate single-cell imaging, microfluidics, and computational modeling to investigate phenotypic divergence and cellular heterogeneity during replicative aging of single S. cerevisiae cells. Specifically, we find that isogenic cells diverge early in life toward one of two aging paths, which are characterized by distinct age-associated phenotypes. We captured the dynamics of single cells along the paths with a stochastic discrete-state model, which accurately predicts both the measured heterogeneity and the lifespan of cells on each path within a cell population. Our analysis suggests that genetic and environmental factors influence both a cell's choice of paths and the kinetics of paths themselves. Given that these factors are highly conserved throughout eukaryotes, divergent aging might represent a general scheme in cellular aging of other organisms.

Abstract Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub. Graphical abstract

Stochastic population dynamics simulations are essential for many ecological and epidemiological studies to generate time series and genealogies that capture the relatedness between individuals. Many software packages allow one to simulate phylogenetic trees but these tend to suffer from one or two major limitations. First, the underlying population dynamics model is often simplistic (e.g. constant population size or exponential growth). Second, the software packages are not appropriate to simulate a large number of trees.We introduce TiPS, an R package to generate trajectories and phylogenetic trees associated with a compartmental model. Trajectories are simulated using Gillespie's exact or approximate stochastic simulation algorithm, or a newly proposed mixed version of the two. Phylogenetic trees are simulated from a trajectory under a backwards‐in‐time approach (i.e. coalescent). TiPS is based on the Rcpp package, allowing to combine the flexibility of R for model definition and the speed of C++ for simulations execution. The model is defined in R with a set of reactions, which allow capturing heterogeneity in life cycles or any sort of population structure. TiPS converts the model into C++ code and compiles it into a simulator that is interfaced in R via a function.TiPS is flexible, easy‐to‐use and available on the CRAN at https://cran.r‐project.org/package=TiPS. Plus, benchmarking analyses show that TiPS is faster than existing packages.This package is particularly well suited for population genetics and phylodynamics studies that need to generate a large number of phylogenies used for population dynamics studies. [ABSTRACT FROM AUTHOR]

This article brings up an introduction to computational physics and scientific programming through an application, the radioactive decay problem. This problem is solved computationally through two different approaches: numerical integration and stochastic simulation. The numerical integration is realised via the Runge-Kutta method, used to obtain numerical solutions of the ordinary differential equation describing the model dynamics, while the stochastic simulations are implemented via Monte Carlo simulations, where random numbers are used to guide decision making. A comparicion of the advantages of each method and its convenience is made and some examples of implementation of the algorithms presented in this work are also exhibited in different programming languages. [ABSTRACT FROM AUTHOR]

To curb the rising threat of antimicrobial resistance, we need to understand the routes to antimicrobial treatment failure. Bacteria can survive treatment by using both genetic and phenotypic mechanisms to diminish the effect of antimicrobials. We assemble empirical data showing that, for example, Pseudomonas aeruginosa infections frequently contain persisters, transiently non-growing cells unaffected by antibiotics (AB) and hyper-mutators, mutants with elevated mutation rates, and thus higher probability of genetic resistance emergence. Resistance, persistence and hyper-mutation dynamics are difficult to disentangle experimentally. Hence, we use stochastic population modelling and deterministic fitness calculations to investigate the relative importance of genetic and phenotypic mechanisms for immediate treatment failure and establishment of prolonged, chronic infections. We find that persistence causes 'hidden' treatment failure with very low cell numbers if antimicrobial concentrations prevent growth of genetically resistant cells. Persister cells can regrow after treatment is discontinued and allow for resistance evolution in the absence of AB. This leads to different mutational routes during treatment and relapse of an infection. By contrast, hyper-mutation facilitates resistance evolution during treatment, but rarely contributes to treatment failure. Our findings highlight the time and concentration dependence of different bacterial mechanisms to escape AB killing, which should be considered when designing 'failure-proof' treatments. [ABSTRACT FROM AUTHOR]

Mathematical modeling provides a rigorous way to quantify immunological processes and discriminate between alternative mechanisms driving specific biological phenomena. It is typical that mathematical models of immunological phenomena are developed by modelers to explain specific sets of experimental data after the data have been collected by experimental collaborators. Whether the available data are sufficient to accurately estimate model parameters or to discriminate between alternative models is not typically investigated. While previously collected data may be sufficient to guide development of alternative models and help estimating model parameters, such data often do not allow to discriminate between alternative models. As a case study, we develop a series of power analyses to determine optimal sample sizes that allow for accurate estimation of model parameters and for discrimination between alternative models describing clustering of CD8 T cells around Plasmodium liver stages. In our typical experiments, mice are infected intravenously with Plasmodium sporozoites that invade hepatocytes (liver cells), and then activated CD8 T cells are transferred into the infected mice. The number of T cells found in the vicinity of individual infected hepatocytes at different times after T cell transfer is counted using intravital microscopy. We previously developed a series of mathematical models aimed to explain highly variable number of T cells per parasite; one of such models, the density-dependent recruitment (DDR) model, fitted the data from preliminary experiments better than the alternative models, such as the density-independent exit (DIE) model. Here, we show that the ability to discriminate between these alternative models depends on the number of parasites imaged in the analysis; analysis of about n = 50 parasites at 2, 4, and 8 h after T cell transfer will allow for over 95% probability to select the correct model. The type of data collected also has an impact; following T cell clustering around individual parasites over time (called as longitudinal (LT) data) allows for a more precise and less biased estimates of the parameters of the DDR model than that generated from a more traditional way of imaging individual parasites in different liver areas/mice (cross-sectional (CS) data). However, LT imaging comes at a cost of a need to keep the mice alive under the microscope for hours which may be ethically unacceptable. We finally show that the number of time points at which the measurements are taken also impacts the precision of estimation of DDR model parameters; in particular, measuring T cell clustering at one time point does not allow accurately estimating all parameters of the DDR model. Using our case study, we propose a general framework on how mathematical modeling can be used to guide experimental designs and power analyses of complex biological processes. [ABSTRACT FROM AUTHOR]

Sample size estimation is a necessary and crucial step in clinical trial research. Statistical requirements, limited patient availability and high financial risk of a clinical trial necessitate the proper calculation of this measure. The aim of this paper is to discuss the reasons why the estimation of the sample size is important and, based on the obtained results, to show how this process may be completed in selected cases. Stochastic simulations based on the Monte Carlo methods approach are applied. Therefore, new challenges facing this area of research are mentioned. [ABSTRACT FROM AUTHOR]

Lignin is an aromatic biopolymer found in ubiquitous sources of woody biomass. Designing and optimizing lignin valorization processes requires a fundamental understanding of lignin structures. Experimental characterization techniques, such as 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra, could elucidate the global properties of the polymer molecules. Computer models could extend the resolution of experiments by representing structures at the molecular and atomistic scales. We introduce a graph-based multiscale modeling framework for lignin structure generation and visualization. The framework employs accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms. We obtain structure libraries for various lignin feedstocks based on literature and new experimental NMR data for poplar wood, pinewood, and herbaceous lignin. The framework could guide researchers towards feasible lignin structures, efficient space exploration, and future kinetics modeling. Its software implementation in Python, LigninGraphs, is open-source and available on GitHub. [ABSTRACT FROM AUTHOR]

Abstract Introduction The objective of this study is to show the importance of interspecies links and temporal network dynamics of a multi-species livestock movement network. Although both cattle and sheep networks have been previously studied, cattle-sheep multi-species networks have not generally been studied in-depth. The central question of this study is how the combination of cattle and sheep movements affects the potential for disease spread on the combined network. Materials and methods Our analysis considers static and temporal representations of networks based on recorded animal movements. We computed network-based node importance measures of two single-species networks, and compared the top-ranked premises with the ones in the multi-species network. We propose the use of a measure based on contact chains calculated in a network weighted with transmission probabilities to assess the importance of premises in an outbreak. To ground our investigation in infectious disease epidemiology, we compared this suggested measure with the results of disease simulation models with asymmetric probabilities of transmission between species. Results Our analysis of the temporal networks shows that the premises which are likely to drive the epidemic in this multi-species network differ from the ones in both the cattle and the sheep networks. Although sheep movements are highly seasonal, the estimated size of an epidemic is significantly larger in the multi-species network than in the cattle network, independently of the period of the year. Finally, we demonstrate that a measure based on contact chains allow us to identify around 30% of the key farms in a simulated epidemic, ignoring markets, whilst static network measures identify less than 10% of these farms. Conclusion Our results ascertain the importance of combining species networks, as well as considering layers of temporal livestock movements in detail for the study of disease spread.

A primary challenge in building predictive models from temporal data is selecting the appropriate model topology and the regulatory functions that describe the data. In this paper we introduce a method for building model prototypes. The method takes as input a collection of time course data. After network inference, we use our toolbox to simulate the model as a stochastic Boolean model. Our method provides a model that can qualitatively reproduce the patterns of the original data and can further be used for model analysis, making predictions, and designing interventions. We ap- plied our method to a time-course, gene-expression data that were collected during salamander tail regeneration under control and intervention conditions. The inferred model captures important regulations that were previously validated in the research literature and gives novel interactions for future testing. The toolbox for inference and simulations is freely available at github.com/alanavc/prototype-model. [ABSTRACT FROM AUTHOR]

Abstract Background Soil-transmitted helminths (STH) are intestinal parasites estimated to infect over 1.5 billion people. Current treatment programmes are aimed at morbidity control through school-based deworming programmes (targeting school-aged children, SAC) and treating women of reproductive age (WRA), as these two groups are believed to record the highest morbidity. More recently, however, the potential for interrupting transmission by treating entire communities has been receiving greater emphasis and the feasibility of such programmes are now under investigation in randomised clinical trials through the Bill & Melinda Gates Foundation funded DeWorm3 studies. Helminth parasites are known to be highly aggregated within human populations, with a small minority of individuals harbouring most worms. Empirical evidence from the TUMIKIA project in Kenya suggests that aggregation may increase significantly after anthelminthic treatment. Methods A stochastic, age-structured, individual-based simulation model of parasite transmission is employed to better understand the factors that might induce this pattern. A simple probabilistic model based on compounded negative binomial distributions caused by age-dependencies in both treatment coverage and exposure to infection is also employed to further this understanding. Results Both approaches confirm helminth aggregation is likely to increase post-mass drug administration as measured by a decrease in the value of the negative binomial aggregation parameter, k. Simple analytical models of distribution compounding describe the observed patterns well. Conclusions The helminth aggregation that was observed in the field was replicated with our stochastic individual-based model. Further work is required to generalise the probabilistic model to take account of the respective sensitivities of different diagnostics on the presence or absence of infection.

In this paper, we address the problem of high greenhouse gas emissions from oil and gas platforms in Norway. We look at the potential of integrating an energy system composed of wind turbines and battery systems to unload the electrical power generated by gas turbines being the main source of emissions today. We propose a simulation model of the energy system, the power demand, the available wind speed, and different control strategies. By putting the models together, we evaluate the performance of various compositions of the system and determine their impact on emissions and battery lifetime. The numerical results show that changing today’s practices has great potential to reduce greenhouse gases, with amounts varying between 30% and 80% compared with today’s level.

Climate changes may affect ecosystems destabilising relationships among species. We investigate the spatial rock-paper-scissors models with a regional unevenness that reduces the selection capacity of organisms of one species. Our results show that the regionally weak species predominates in the local ecosystem, while spiral patterns appear far from the region, where individuals of every species play the rock-paper-scissors game with the same strength. Because the weak species controls all local territory, it is attractive for the other species to enter the local ecosystem to conquer the territory. However, our stochastic simulations show that the transitory waves formed when organisms of the strong species reach the region are quickly destroyed because of local strength unbalance in the selection game rules. Computing the effect of the topology on population dynamics, we find that the prevalence of the weak species becomes more significant if the transition of the selection capacity to the area of uneven rock-paper-scissors rules is smooth. Finally, our findings show that the biodiversity loss due to the arising of regional unevenness is minimised if the transition to the region where the cyclic game is unbalanced is abrupt. Our results may be helpful to biologists in comprehending the consequences of changes in the environmental conditions on species coexistence and spatial patterns in complex systems.

The most common gene regulation mechanism is when a transcription factor (TF) protein binds to a regulatory sequence to increase or decrease RNA transcription. However, TFs face two main challenges when searching for these sequences. First, the sequences are vanishingly short relative to the genome length. Second, there are many nearly identical sequences scattered across the genome, causing proteins to suspend the search. But as pointed out in a computational study of LacI regulation in Escherichia coli , such almost-targets may lower search times if considering DNA looping. In this paper, we explore if this also occurs over chromosome-wide distances. To this end, we developed a cross-scale computational framework that combines established facilitated-diffusion models for basepair-level search and a network model capturing chromosome-wide leaps. To make our model realistic, we used Hi-C data sets as a proxy for 3D proximity between long-ranged DNA segments and binding profiles for more than 100 TFs. Using our cross-scale model, we found that median search times to individual targets critically depend on a network metric combining node strength (sum of link weights) and local dissociation rates. Also, by randomizing these rates, we found that some actual 3D target configurations stand out as considerably faster or slower than their random counterparts. This finding hints that chromosomes’ 3D structure funnels essential TFs to relevant DNA regions.

Summary Available models of fire risk assessment can still hardly be used for practical engineering problems. They are mostly loosely integrated, complex, computationally demanding applications, and require additional pre‐ or post‐hand‐calculations. However, the latest achievements in computer science enable establishing an easy‐to‐use web‐application with access to enormous computing power in a cloud requiring minimal management effort. In this article, we present a new tool for probabilistic fire risk assessment called Aamks. Our goal is to build an easy‐to‐use, science‐based, practical engineering tool to support building design in day‐to‐day work. To meet the overall goal, we aim at working out an integrated, web‐based application with computational efficiency and scalability allowed by cloud computing. Aamks performs a stochastic analysis of life safety in building fires using deterministic models for fire and evacuation and stochastic sampling of the uncertain input parameters. [ABSTRACT FROM AUTHOR]

A numerically efficient globally averaged number density approach is used to simulate a reaction-diffusion system using a particle-based stochastic simulation algorithm called reactive multiparticle collision (RMPC) dynamics. Constant diffusivity of the particles is achieved through a time-varying rotation angle (also called collision angle). Variation in the diffusion coefficient between two different chemical species is achieved in one of two ways: (i) using a different kBT/m value for one species compared to the other, or (ii) using the same kBT/m value for both species, but using a different probability to free-stream for one species compared to another. For smaller diffusivities and larger spatial inhomogeneities, bath particles were necessary for the model to agree with the PDE solution. The latter approach was further used without a bath, and shown to be capable of producing Turing patterns after long simulation times. The significance of our work is that RMPC can serve as a feasible simulation tool for both short and long-term simulations, can handle spatial inhomogeneities, can model a fairly large range of diffusivities in a reaction-diffusion scenario, and is capable of producing Turing patterns. An advantage of this method includes more detailed system information in feasible simulation times.

The accumulation of motile cells at solid interfaces increases the rate of surface encounters and the likelihood of surface attachment, leading to surface colonization and biofilm formation. The cell density distribution in the vicinity of a physical boundary is influenced by the interactions between the microswimmers and their physical environment, including hydrodynamic and steric interactions, as well as by stochastic effects. Disentangling the contributions of these effects remains an experimental challenge. Here, we use a custom-made four-camera view microscope to track a population of motile puller-type Chlamydomonas reinhardtii in a relatively unconstrained three-dimensional (3D) domain. Our experiments yield an extensive sample of 3D trajectories including cell-surface encounters with a planar wall, from which we extract a full description of the dynamics and the stochasticity of swimming cells. We use this large data sample and combine it with Monte Carlo simulations to determine the link between the dynamics at the single-cell level and the cell density. Our experiments demonstrate that the near-wall scattering is bimodal, corresponding to steric and hydrodynamic effects. We find, however, that this near-wall dynamics has little influence on the cell accumulation at the surface. On the other hand, we present evidence of a cell-induced surface-directed rotation leading to a vertical orbiting behavior and hopping trajectories, consistent with long-range hydrodynamic effects. We identify this long-range effect to be at the origin of the significant surface accumulation of cells. [ABSTRACT FROM AUTHOR]

While facultative sex is common in sexually reproducing species, for reasons of tractability most mathematical models assume that such sex is asynchronous in the population. In this paper, we develop a model of switching environments to instead capture the effect of an entire population transitioning synchronously between sexual and asexual modes of reproduction. We use this model to investigate the evolution of the number of self-incompatible mating types in finite populations, which empirically can range from two to thousands. When environmental switching is fast, we recover the results of earlier studies that implicitly assumed populations were engaged in asynchronous sexual reproduction. However when the environment switches slowly, we see deviations from previous asynchronous theory, including a lower number of mating types at equilibrium and bimodality in the stationary distribution of mating types. We provide analytic approximations for both the fast and slow switching regimes, as well as a numerical scheme based on the Kolmogorov equations for the system to quickly evaluate the model dynamics at intermediate parameters. Our approach exploits properties of integer partitions in number theory. We also demonstrate how additional biological processes such as selective sweeps can be accounted for in this switching environment framework, showing that beneficial mutations can further erode mating type diversity in synchronous facultatively sexual populations. [ABSTRACT FROM AUTHOR]

Nitrate represents the most widespread contaminant in shallow aquifers, especially in urban areas, and poses risks to human health, when the contaminated groundwater is ingested. In urban environments, the release of nitrate in groundwater can occur from multiple sources and is frequently associated with sewage leakage and septic tank infiltration. The Rio Claro Aquifer, located on the campus of the São Paulo State University at Rio Claro, offers an attractive example of a shallow aquifer impacted by nitrate contamination. Old sewage spills are considered to be the main sources of contamination; however, their locations remain largely unknown. Because of the scarce data and heterogeneous aquifer geology, the direct backward location approach is unsuitable in this case. Aiming to predict the probable locations of contamination sources, we developed a probabilistic backward location approach to identify the backward location in multiple geological scenarios using stochastic simulations. The numerical flow simulation and backward particle tracking were conducted based on 100 stochastic scenarios generated with Markov chains using lithological data from core descriptions. The multiple backward locations generated by stochastic simulations allowed us to build a density map to identify the region most likely to contain the contamination sources, thus simplifying the investigation and mitigation of the sewage spills. [ABSTRACT FROM AUTHOR]

Numerous meat sheep breeding programs in developed and developing countries are characterized by incomplete sire information and a predominant use of natural matings. These two parameters potentially affect the benefit of genomic selection (GS), especially for the selection of a late-in-life trait. Using stochastic simulations, the genetic gains obtained using genomic and conventional strategies for a maternal trait were evaluated in meat sheep population. Natural mating and artificial insemination (AI)-based designs, inspired by the current diversity of designs used for French meat sheep breeds, were modeled and three genomic strategies were tested and compared with a conventional selection strategy: parentage assignment, GS based on a male or a male and female reference population. Genomic selection based on a male reference population did not always outperform conventional selection. Its benefit depended on the design, the level of missing information on dam sires, and the level of AI. Genomic selection based on a male and female reference population always outperformed the conventional selection strategy, even if only 25 % of the females in the nucleus were genotyped.

Summary: Intrathymic development of committed progenitor (pro)-T cells from multipotent hematopoietic precursors offers an opportunity to dissect the molecular circuitry establishing cell identity in response to environmental signals. This transition encompasses programmed shutoff of stem/progenitor genes, upregulation of T cell specification genes, proliferation, and ultimately commitment. To explain these features in light of reported cis-acting chromatin effects and experimental kinetic data, we develop a three-level dynamic model of commitment based upon regulation of the commitment-linked gene Bcl11b. The levels are (1) a core gene regulatory network (GRN) architecture from transcription factor (TF) perturbation data, (2) a stochastically controlled chromatin-state gate, and (3) a single-cell proliferation model validated by experimental clonal growth and commitment kinetic assays. Using RNA fluorescence in situ hybridization (FISH) measurements of genes encoding key TFs and measured bulk population dynamics, this single-cell model predicts state-switching kinetics validated by measured clonal proliferation and commitment times. The resulting multi-scale model provides a mechanistic framework for dissecting commitment dynamics.

Background: Plant dispersal is a critical factor driving ecological responses to global changes. Knowledge on the mechanisms of dispersal is rapidly advancing, but selective pressures responsible for the evolution of dispersal strategies remain elusive. Recent advances in animal movement ecology identified general strategies that may optimize efficiency in animal searches for food or habitat. Here we explore the potential for evolution of similar general movement strategies for plants. Methods: We propose that seed dispersal in plants can be viewed as a strategic search for suitable habitat, where the probability of finding such locations has been optimized through evolution of appropriate dispersal kernels. Using model simulations, we demonstrate how dispersal strategies can optimize key dispersal trade-offs between finding habitat, avoiding kin competition, and colonizing new patches. These trade-offs depend strongly on the landscape, resulting in a tight link between optimal dispersal strategy and spatiotemporal habitat distribution. Results: Our findings reveal that multi-scale seed dispersal strategies that combine a broad range of dispersal scales, including Lévy-like dispersal, are optimal across a wide range of dynamic and patchy landscapes. At the extremes, static and patchy landscapes select for dispersal strategies dominated by short distances, while uniform and highly unpredictable landscapes both select for dispersal strategies dominated by long distances. Conclusions: By viewing plant seed dispersal as a strategic search for suitable habitat, we provide a reference framework for the analysis of plant dispersal data. Consideration of the entire dispersal kernel, including distances across the full range of scales, is key. This reference framework helps identify plant species' dispersal strategies, the evolutionary forces determining these strategies and their ecological consequences, such as a potential mismatch between plant dispersal strategy and altered spatiotemporal habitat dynamics due to land use change. Our perspective opens up directions for future studies, including exploration of composite search behaviour and 'informed searches' in plant species with directed dispersal. [ABSTRACT FROM AUTHOR]

This case-study is dedicated to the seismic risk assessment of cylindrical fixed-based reinforced concrete and prestressed concrete water tanks situated in Romania. The fixed-based water tanks are designed according to the current seismic design prescriptions in Romania. The six selected sites are all under the influence of the Vrancea intermediate-depth seismic source. The seismic performance is evaluated using site-specific ground motion stochastic simulations and using the approach given in Eurocode 8-4 (2006). The largest mean annual failure probabilities are obtained for the two sites (Amara and Bucharest) with the highest long-period spectral amplifications. The study shows that from the strength point of view, the two types of tanks have very small corresponding failure probabilities and from the serviceability point of view, depending on the type of the tank and its position, sloshing or cracking due to bending are the prevalent failure modes. [ABSTRACT FROM AUTHOR]

Recent advances in fluorescence microscopy have made it possible to measure the fluctuations of nascent (actively transcribed) RNA. These closely reflect transcription kinetics, as opposed to conventional measurements of mature (cellular) RNA, whose kinetics is affected by additional processes downstream of transcription. Here, we formulate a stochastic model which describes promoter switching, initiation, elongation, premature detachment, pausing, and termination while being analytically tractable. We derive exact closed-form expressions for the mean and variance of nascent RNA fluctuations on gene segments, as well as of total nascent RNA on a gene. We also obtain exact expressions for the first two moments of mature RNA fluctuations and approximate distributions for total numbers of nascent and mature RNA. Our results, which are verified by stochastic simulation, uncover the explicit dependence of the statistics of both types of RNA on transcriptional parameters and potentially provide a means to estimate parameter values from experimental data. [ABSTRACT FROM AUTHOR]

Predicting gene expression from DNA sequence remains a major goal in the field of gene regulation. A challenge to this goal is the connectivity of the network, whose role in altering gene expression remains unclear. Here, we study a common autoregulatory network motif, the negative single-input module, to explore the regulatory properties inherited from the motif. Using stochastic simulations and a synthetic biology approach in E. coli, we find that the TF gene and its target genes have inherent asymmetry in regulation, even when their promoters are identical; the TF gene being more repressed than its targets. The magnitude of asymmetry depends on network features such as network size and TF-binding affinities. Intriguingly, asymmetry disappears when the growth rate is too fast or too slow and is most significant for typical growth conditions. These results highlight the importance of accounting for network architecture in quantitative models of gene expression.

In this work, we study a linear birth–death process starting from random initial conditions. First, we consider these initial conditions as a random number of particles following different standard probabilistic distributions – Negative-Binomial and its closest Geometric, Poisson or Pólya–Aeppli distributions. It is proved analytically and numerically that in these cases the random number of particles alive at any positive time follows the same probability law like the initial condition, but with different parameters depending on time. The random initial conditions cannot change the critical parameter of branching mechanism, but they impact the extinction probability. Finally, the numerical model is extended to an application for studying branching processes with more complex initial conditions. This is demonstrated with a linear birth–death process initialised with Pólya urn sampling scheme. The obtained preliminary results for particle distribution show close relation to Pólya–Aeppli distribution. [ABSTRACT FROM AUTHOR]

High densities of local-scale daily precipitation series across relatively large domains are of special interest for a wide range of applications (e.g., hydrological modeling, agriculture). The focus of the present study is to post-process gridded precipitation from a single reanalysis to correct bias and scale mismatch with observations, and to extend the same post-processing at sites without historical data. A Stochastic Model Output Statistical approach combined with meta-Gaussian spatiotemporal random fields, calibrated at sites, is employed to post-process the Climate Forecast System Reanalysis (CFSR) precipitation. The post-processed data, characterized by local parameters, is then mapped across the Great Lakes region (Canada) using two different approaches: (1) kriging, and (2) Vector Generalized Additive Model (VGAM) with spatial covariates. The kriging enables the interpolation of these parameters, while the spatial VGAM helps to spatially post-process CFSR precipitation using a single model. The k-fold cross-validation procedure is employed to assess the ability of the two approaches to predict selected characteristics and climate indices. The kriging and spatial VGAM approaches modeled effectively the distribution of the precipitation process to similar extents (e.g., mean daily precipitation, variability and the number of wet days). The kriging approach produces slightly better estimates of climate indices than the spatial VGAM models. Both approaches demonstrate significant improvement of the metric estimation compared to those of CFSR without post-processing. [ABSTRACT FROM AUTHOR]