Stochastic Simulation Research Articles

The bistable mitotic switch in fission yeast.

In favorable conditions, eukaryotic cells proceed irreversibly through the cell division cycle (G1-S-G2-M) in order to produce two daughter cells with the same number and identity of chromosomes of their progenitor. The integrity of this process is maintained by "checkpoints" that hold a cell at particular transition points of the cycle until all requisite events are completed. The crucial functions of these checkpoints seem to depend on irreversible bistability of the underlying checkpoint control systems. Bistability of cell cycle transitions has been confirmed experimentally in frog egg extracts, budding yeast cells and mammalian cells. For fission yeast cells, a recent paper by Patterson etal. (2021) provides experimental evidence for an abrupt transition from G2 phase into mitosis, and we show that these data are consistent with a stochastic model of a bistable switch governing the G2/M checkpoint. Interestingly, our model suggests that their experimental data could also be explained by a reversible/sigmoidal switch, and stochastic simulations confirm this supposition. We propose a simple modification of their experimental protocol that could provide convincing evidence for (or against) bistability of the G2/M transition in fission yeast.

Image‐based hybrid model incorporating initial spatial distribution for mesenchymal stem cell cultivation process design

AbstractThis work presents an image‐based hybrid model incorporating the initial spatial distribution for mesenchymal stem cell (MSC) cultivation process design. First, three levels of seeding bias in static MSC cultivation were experimentally investigated, and the resulting initial distribution was quantified using phase contrast microscopy image analysis. Second, the observed spatial heterogeneity was defined as a new parameter by calculating the standard deviation of the seeded cell number fraction among numerical 8 × 8 tiles on two‐dimensional plates. Third, the parameter was incorporated into kinetic models to consider spatial growth limitation. The model was then applied to simulate MSC cultivation processes with experimental data. Using dynamic and stochastic simulation outputs, feasible ranges of cell‐harvesting time could be determined to satisfy a given requirement for the minimum cell number and acceptable confluency level. The developed hybrid model could serve as a basis for quantitative decision‐making in the design of MSC cultivation processes.

Elastic particle model for coil-stretch transition of dilute polymers in an elongational flow

The phenomenon of the ‘coil-stretch’ (C-S) transition, wherein a long-chain polymer initially in a coiled state undergoes a sudden configuration change to become fully stretched under steady elongational flows, has been widely recognized. This transition can display intricate hysteresis behaviours under specific critical conditions, giving rise to unique rheological characteristics in dilute polymer solutions. Historically, microscopic stochastic models and Brownian dynamics simulations have shed light on the underlying mechanisms of the transition by uncovering bistable configurations of polymer chains. Following the initial work by Cerf (J. Chem. Phys., vol. 20, 1952, pp. 395–402), we introduce a continuum model in this study to investigate the C-S transition in a constant uniaxial elongational flow. Our approach involves approximating the unfolding process of the polymer chain as an axisymmetric deformation of an elastic particle. We make the assumption that the particle possesses uniform material properties, which can be represented by a nonlinear, strain-hardening constitutive equation to replicate the finite extensibility of the polymer chain. Subsequently, we analytically solve for the steady-state deformation using a polarization method. By employing this reduced model, we effectively capture the C-S transition and establish its specific correlations with material and geometric properties. The hysteresis phenomena can be comprehended through a force-balance analysis, which involves comparing the externally applied viscous forces with the intrinsic elastic responsive forces. We demonstrate that our model, while simple, unveils rich elastohydrodynamics of the C-S transition.

Risks to Human Health from the Consumption of Water from Aquifers in Gold Mining Areas in the Coastal Region of Ecuador

Artisanal and Small-scale Gold Mining (ASGM) is a source of supply in many areas of the world, especially in developing countries. This is often carried out illegally using toxic substances such as mercury. Mercury, due to its chemical–physical properties and the transport factors involved between the different environmental matrices, can percolate through soil and from surface water to groundwater. The objective of this study was to conduct a human health risk assessment. For this purpose, a screening of mercury concentrations was carried out, collecting 67 water samples at selected points, and a risk assessment was performed applying both a deterministic and a probabilistic approach. A deterministic approach is a specific analysis based on determining the values of the risk quotient (HQ) and the risk index (HI) for each receptor category (adults and children) and scenario (residential and recreational) considered; a probabilistic approach is based on stochastic simulation techniques and the evaluation of the statistical quantities. There was found to be a discrepancy between the results provided by the two approaches, with the deterministic approach suggesting a more worrisome picture. However, in general, the results showed a greater exposure in the provinces of El Oro and Esmeraldas, and a greater vulnerability of child receptors.

Probability density evolution method based stochastic simulation of near-fault pulse-like ground motions

Quantifying the near-fault effect and establishing a reasonable model of near-fault pulse-like ground motions are particularly important for seismic design of structures in near-fault regions. Given the pronounced randomness associated with earthquakes, this study first proposes a novel stochastic model of near-fault pulse-like ground motions by combining the improved finite-fault model (IFFM) and the multivariate copula-based velocity-pulse model (CVPM). Further, a probability density evolution method (PDEM) based stochastic simulation method is developed, by which the model parameters can be determined in a unified probability space so as to ensure the consistency of two independent models. For illustrative purposes, the observed records collected from the 1999 Chi-Chi earthquake are used to generate new stochastic ground motions set. Two ground motions sets based on classical stochastic simulation methods are also presented for comparison. Numerical results show that the proposed method for stochastic simulation of near-fault pulse-like ground motions is reliable; the statistics of peak ground accelerations and spectral characteristics of simulated samples are consistent with station records. Besides, the proposed method accommodates the noteworthy randomness and proportion consistency of components associated with near-fault pulse-like ground motions, making it suitable for the stochastic response and reliability analysis of seismic structures in near-fault regions. This superiority is challenging to classical stochastic simulation methods that lack reasonable consideration of randomness and correlation associated with model parameters.

Simulation of Full HIV Cluster Networks in a Nationally Representative Model Indicates Intervention Opportunities.

Clusters of rapid HIV transmission in the United States are increasingly recognized through analysis of HIV molecular sequence data reported to the National HIV Surveillance System. Understanding the full extent of cluster networks is important to assess intervention opportunities. However, full cluster networks include undiagnosed and other infections that cannot be systematically observed in real life. We replicated HIV molecular cluster networks during 2015-2017 in the United States using a stochastic dynamic network simulation model of sexual transmission of HIV. Clusters were defined at the 0.5% genetic distance threshold. Ongoing priority clusters had growth of ≥3 diagnoses/year in multiple years; new priority clusters first had ≥3 diagnoses/year in 2017. We assessed the full extent, composition, and transmission rates of new and ongoing priority clusters. Full clusters were 3-9 times larger than detected clusters, with median detected cluster sizes in new and ongoing priority clusters of 4 (range 3-9) and 11 (range 3-33), respectively, corresponding to full cluster sizes with a median of 14 (3-74) and 94 (7-318), respectively. A median of 36.3% (range 11.1%-72.6%) of infections in the full new priority clusters were undiagnosed. HIV transmission rates in these clusters were >4 times the overall rate observed in the entire simulation. Priority clusters reflect networks with rapid HIV transmission. The substantially larger full extent of these clusters, high proportion of undiagnosed infections, and high transmission rates indicate opportunities for public health intervention and impact.

Polyadic Opinion Formation: The Adaptive Voter Model on a Hypergraph

AbstractThe adaptive voter model is widely used to model opinion dynamics in social complex networks. However, existing adaptive voter models are limited to only pairwise interactions and fail to capture the intricate social dynamics that arises in groups. This paper extends the adaptive voter model to hypergraphs to explore how forces of peer pressure influence collective decision‐making. The model consists of two processes: individuals can either consult the group and change their opinion or leave the group and join a different one. The interplay between those two processes gives rise to a two‐phase dynamics. In the initial phase, the topology of the hypergraph quickly reaches a new stable state. In the subsequent phase, opinion dynamics plays out on the new topology depending on the mechanism by which opinions spread. If the group always follows the majority, the network rapidly converges to fragmented communities. In contrast, if individuals choose an opinion proportionally to its representation in the group, the system remains in a metastable state for an extended period of time. The results are supported both by stochastic simulations and an analytical mean‐field description in terms of hypergraph moments with a moment closure at the pair level.

Simulation-based evaluation of indoor positioning systems in connected aircraft cabins

With regard to the demanding aircraft cabin environment, this paper delves into the rising use of location-aware radio communication systems to streamline operational processes. We propose a hybrid deterministic and stochastic simulation approach, incorporating model-based ray-tracing and empirical residual simulation. The methodology presented allows for the evaluation of localization methods based on geometric relations, serving as both a data generation and validation tool. We elaborate how different radio properties and propagation phenomena influence these geometric relations and the localization process. This paper includes a publicly available dataset derived from the simulation approach, facilitating transparency and further analysis in the field of aircraft cabin radio localization systems.

Release potential, neglected leakage and reduction countermeasures of COD and Ammonia in MSWLs

In order to eliminate the impact of the industrial revolution on the environment and improve the water ecological environment, pollutant discharge reduction is imperative. With the acceleration of global discharge reduction process, the huge pollutant release potential and potential environmental effects of municipal solid waste landfills gradually appear, but its release amount and intensity have not been quantitatively revealed. We propose a coupling method of parameter stochastic simulation and physical process model simulation to estimate the hidden leakage of large-scale regional municipal solid waste landfills, and provide a methodology for estimating the hidden leakage of landfills in other countries and even in the whole world by taking China, which has the largest amount of waste generation among developing countries, as an example. Prior to the implementation of stringent construction quality control and assurance management requirements, the average annual leachate generation potential over the entire life cycle of 2600 landfills in China was estimated to be 4.66 × 108 m3, in which the concentrations of COD and NH3-N are 5.38 × 102–6.48 × 104 mg/L and 6.10–3.50 × 103 mg/L, respectively, and the total amounts are 5.21 × 103–7.81 × 108 t and 8.09 × 102–6.65 × 107 t, respectively. About 14 % of these pollutants may leak into the environmental media through the landfill liner with the average number of holes of 21.5/ha. For different regions, the overall release, discharge and leakage of COD and NH3-N in East China account for 35.70 %, 36.68 % and 29.60 % respectively, making it the region with the highest potential for discharge and risk of leakage. Meanwhile, the implementation of mandatory regulations related to leachate generation and control has led to a significant reduction in the leakage of pollutants. For instance, comprehensively detecting and repair of holes in the impermeable liner has reduced the number of holes to 2/ha, resulting in a reduction of >90 % in the leakage of pollutants.

WITHDRAWAL – Administrative Duplicate Publication: From Source to Building Fragility: Post-event Assessment of the 2013 M7.1 Bohol Philippines Earthquake

We use ground motion simulations of the 2013 Bohol Philippines earthquake along with a new post-disaster exposure/damage database to constrain building fragility and vulnerability. The large number of damaged buildings (>70,000) and the wide spread of seismic intensities caused by this earthquake make it an ideal candidate for such a study. An extensive survey was conducted leading to a robust description of over 25,000 damaged and undamaged structures. Ground motion fields were simulated using ground motion prediction equations and stochastic modeling, and the estimated and observed values were compared. The finite source model used in the simulation was based on the analysis of aftershocks and SAR data. The ground motions were associated with the empirical database to derive fragility and vulnerability models. Results indicate that the pattern of damage is best captured in the stochastic simulation. Constraints were placed on seismic building fragility and vulnerability models, which can promote more effective implementation of construction regulations and practices.

Conditional stochastic simulation of fluvial reservoirs using multi-scale concurrent generative adversarial networks

Conditional stochastic simulation of fluvial reservoirs using multi-scale concurrent generative adversarial networks

Trade-offs in concentration sensing in dynamic environments

When cells measure concentrations of chemical signals, they may average multiple measurements over time in order to reduce noise in their measurements. However, when cells are in an environment that changes over time, past measurements may not reflect current conditions—creating a new source of error that trades off against noise in chemical sensing. What statistics in the cell’s environment control this trade-off? What properties of the environment make it variable enough that this trade-off is relevant? We model a single eukaryotic cell sensing a chemical secreted from bacteria (e.g., folic acid). In this case, the environment changes because the bacteria swim—leading to changes in the true concentration at the cell. We develop analytical calculations and stochastic simulations of sensing in this environment. We find that cells can have a huge variety of optimal sensing strategies ranging from not time averaging at all to averaging over an arbitrarily long time or having a finite optimal averaging time. The factors that primarily control the ideal averaging are the ratio of sensing noise to environmental variation and the ratio of timescales of sensing to the timescale of environmental variation. Sensing noise depends on the receptor-ligand kinetics, while environmental variation depends on the density of bacteria and the degradation and diffusion properties of the secreted chemoattractant. Our results suggest that fluctuating environmental concentrations may be a relevant source of noise even in a relatively static environment.

Plasmid-mediated phenotypic noise leads to transient antibiotic resistance in bacteria

The rise of antibiotic resistance is a critical public health concern, requiring an understanding of mechanisms that enable bacteria to tolerate antimicrobial agents. Bacteria use diverse strategies, including the amplification of drug-resistance genes. In this paper, we showed that multicopy plasmids, often carrying antibiotic resistance genes in clinical bacteria, can rapidly amplify genes, leading to plasmid-mediated phenotypic noise and transient antibiotic resistance. By combining stochastic simulations of a computational model with high-throughput single-cell measurements of blaTEM-1 expression in Escherichia coli MG1655, we showed that plasmid copy number variability stably maintains populations composed of cells with both low and high plasmid copy numbers. This diversity in plasmid copy number enhances the probability of bacterial survival in the presence of antibiotics, while also rapidly reducing the burden of carrying multiple plasmids in drug-free environments. Our results further support the tenet that multicopy plasmids not only act as vehicles for the horizontal transfer of genetic information between cells but also as drivers of bacterial adaptation, enabling rapid modulation of gene copy numbers. Understanding the role of multicopy plasmids in antibiotic resistance is critical, and our study provides insights into how bacteria can transiently survive lethal concentrations of antibiotics.

Probabilistic Design Methods for Gust-Based Loads on Wind Turbines

The IEC 61400-1 standard specifies design load cases (DLCs) to be considered in the design of wind turbine structures. Specifically, DLC 2.3 considers the occurrence of a gust while the turbine shuts down due to an electrical fault. Originally, this load case used a deterministic wind event called the extreme operating gust (EOG), but the standard now also includes an approach for calculating the extreme response based on stochastic simulations with turbulent wind. This study presents and compares existing approaches with novel probabilistic design approaches for DLC 2.3 based on simulations with turbulent wind. First, a semiprobabilistic approach is proposed, where the inverse first-order reliability method (iFORM) is used for the extrapolation of the response for electrical faults occurring at a given rate. Next, three probabilistic approaches are formulated for the calculation of the reliability index, which differs in how the aggregation is performed over wind conditions and whether faults are modeled using a Poisson distribution or just by the rate. An example illustrates the methods considering the tower fore-aft bending moment at the tower base and shows that the approach based on iFORM can lead to reductions in material usage compared to the existing methods. For reliability assessment, the probabilistic approach using the Poisson process is needed for high failure rates, and the reliabilities obtained for designs using all semiprobabilistic methods are above the target level, indicating that further reductions may be obtained via the use of probabilistic design methods.

Dynamics of large-scale solar PV adoption feedback effects: A technical, economic, and environmental assessment

Large residential solar photovoltaic (PV) penetration has a compound effect on the grid load reductions, PV hosts’ economic savings, and the achievable environmental benefits, which is not fully understood. This study combines process-based energy balance modeling, life cycle assessment, regression analysis, and stochastic demand simulations to assess the technical, economic, and environmental tradeoffs under increased residential solar PV adoption, using Boston, MA as a testbed. It was found that increased PV adoption may lead to a steeper ramp-up in the grid during winter months, but a flattened peak load curve during summer months, emphasizing the need for seasonal time-of-use rates and energy storage. It also reduces electricity wholesale prices, lowering PV hosts' economic benefits by about $15 million under 100 % adoption. The largest buildings present the highest load reduction (top 5.5 %) and environmental benefits (top 16.6 %), but they are the least cost efficient (top 14.5 %), requiring tradeoff balance.

Abstract 97: A E2F6 ceRNA network suppresses dendritic cell function, via PBX1/IL-10 signaling, in ovarian cancer

Abstract It is reported that long-term use of estrogen could increase the risk of ovarian cancer. However, the role of estrogen in immunoevasion is not fully explored. We have previously demonstrated that estrogen-mediated upregulation of E2F6, and, c-Kit, by epigenetic silencing of miR-193a, and a competing endogenous (ceRNA) mechanism. In this study, we found that PBX1, a transcriptional activator of the immunosuppressive cytokine, IL-10, is also a target of miR-193a. Importantly, overexpression of the E2F6 3’UTR upregulates both E2F6 and, PBX1, as well as IL10 in ovarian cancer cell lines, suggesting that ceRNA mechanism exists between E2F6 and PBX1. These phenomena are further supported by our stochastic simulation of the estrogen-mediated E2F6 ceRNA network on the distribution of E2F6 and PBX1 mRNA in cancer cells, which is consistent with the TCGA ovarian cancer RNA-Seq dataset. Importantly, monocyte-derived dendritic cell activation of T-cell function was inhibited by pretreatment of conditioned media derived from ovarian cancer cells overexpressing E2F6 3’UTR; such inhibition was rescueable by an anti-IL-10 antibody. Clinically, IL10 level was higher in ovarian cancer patients with higher E2F6 and PBX1, and in ovarian cancer cell lines overexpressed with E2F6 3’UTR. Taken together, these results showed that E2F6 could suppress anti-tumor immune response of dendritic cell, E2F6 ceRNA network. Epigenetic intervention in restoring the expression of miR-193a may be able to enhance anti-tumor immune response against ovarian cancer. Citation Format: Michael W.Y. Chan, Yin-Chen Chen, Ching-Wen Lin, Frank Cheng, Ching-Cher Sanders Yan, Chao-Ping Hsu, Yu-Min Chuang, Jie-Ting Low, Xiaojing Ma, Yao-Ting Huang, Chia-Bin Chang, Chin Li, Hung-Cheng Lai, Shu-Fen Wu, Shih-Hsun Hung, Je-Chiang Tsai. A E2F6 ceRNA network suppresses dendritic cell function, via PBX1/IL-10 signaling, in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 97.

Study of the mechanical behavior and multiscale simulation of the crack propagation in a bilinga wooden beam

The mechanical behavior of local wood species (Bilinga) in the south west region in Cameroon during rainy and dry seasons and the mechanical behavior of wooden beam under bend loading are studied. The three points flexural tests were used to determine the mechanical properties of the wood under study. ANSYS 2020 R1 finite element (FE) software is used for numerical simulations at a macroscopic level using one of the newer technologies called Smart crack growth, which was introduced in the 2019 version. The geometry was modeled in SolidWorks with an initial crack length of 4 and 8 mm introduced in each sample and then imported to ANSYS workbench for further analysis with ANSYS which has all the tools to perform linear fracture. The stress intensity factor (SIF) determines the fracture toughness of a material which is subjected to linear-elastic fracture mechanics (LEFM) where a variable of the critical stress intensify is denoted as KIc. The fatigue crack growth was modeled using Paris’ law. The crack growth was simulated based on Mode I crack specimen with an initial crack length of 4 and 8 mm, respectively. The stochastic multiscale modeling of crack growth on meso- and microscale is used to compare the crack growth rate in the approach of a heterogeneous material and taking into account the microstructure and fracture mechanism of the Bilinga wood. The results of stochastic modeling of the crack growth in the array of cracks and pores of a characteristic size shows that the simulation is close to FE-modeling results. Therefore, the stochastic simulation of the crack growth in wood at meso- and microscale shows the lower local stress intensity factors and slower crack growth due to the existence of the scale-time hierarchy. The crack growth rate vcr at a macroscale ranges within 0.845 – 0.9 × 10–3 m/sec which corresponds to the macroscopic value of the fracture toughness KIc.

Cell4D: a general purpose spatial stochastic simulator for cellular pathways

BackgroundWith the generation of vast compendia of biological datasets, the challenge is how best to interpret ‘omics data alongside biochemical and other small-scale experiments to gain meaningful biological insights. Key to this challenge are computational methods that enable domain-users to generate novel hypotheses that can be used to guide future experiments. Of particular interest are flexible modeling platforms, capable of simulating a diverse range of biological systems with low barriers of adoption to those with limited computational expertise.ResultsWe introduce Cell4D, a spatial-temporal modeling platform combining a robust simulation engine with integrated graphics visualization, a model design editor, and an underlying XML data model capable of capturing a variety of cellular functions. Cell4D provides an interactive visualization mode, allowing intuitive feedback on model behavior and exploration of novel hypotheses, together with a non-graphics mode, compatible with high performance cloud compute solutions, to facilitate generation of statistical data. To demonstrate the flexibility and effectiveness of Cell4D, we investigate the dynamics of CEACAM1 localization in T-cell activation. We confirm the importance of Ca2+ microdomains in activating calmodulin and highlight a key role of activated calmodulin on the surface expression of CEACAM1. We further show how lymphocyte-specific protein tyrosine kinase can help regulate this cell surface expression and exploit spatial modeling features of Cell4D to test the hypothesis that lipid rafts regulate clustering of CEACAM1 to promote trans-binding to neighbouring cells.ConclusionsThrough demonstrating its ability to test and generate hypotheses, Cell4D represents an effective tool to help integrate knowledge across diverse, large and small-scale datasets.

Direct stochastic simulation of a rarefied gas flow in channels of variable cross section

Direct stochastic simulation of a rarefied gas flow in channels of variable cross section

Stream sediment pollution: a compositional baseline assessment

AbstractA high concentration of potentially toxic elements (PTEs) can affect ecosystem health in many ways. It is therefore essential that spatial trends in pollutants are assessed and monitored. Two questions must be addressed when quantifying pollution: how to define a non-polluted sample and how to reduce the problem’s dimensionality. A geochemical dataset is a composition of variables (chemical elements), where the components represent the relative importance of each part of the whole. Therefore, to comply with the compositional constraints, a compositional approach was used. A novel compositional pollution indicator (CPI) based on compositional data (CoDa) principles such as the properties of sparsity and simplicity was computed. A dataset of 12 chemical elements in 33 stream-sediment samples were collected from depths of 0–10 cm in a grid of 1 km × 1 km and analyzed. Maximum concentrations of 3.8% Pb, 750 µg g−1 As, and 340 µg g–1 Hg were obtained near the mine tailings. The methodological approach involved geological background selection in terms of a trimmed subsample that could be assumed to contain only non-pollutants (Al and Fe) and the selection of a list of pollutants (As, Zn, Pb, and Hg) based on expert knowledge criteria and previous studies. Finally, a stochastic sequential Gaussian simulation of the new CPI was performed. The results of the hundred simulations performed were summarized through the mean image map and maps of the probability of exceeding a given statistical threshold, allowing the characterization of the spatial distribution and the associated variability of the CPI. A high risk of contamination along the Grândola River was observed. As the main economic activities in this area are agricultural and involve animal stocks, it is crucial to establish two lines of intervention: the installation of a surveillance network for continuous control in all areas and the definition of mitigation actions for the northern area with high levels of contamination. Graphical Abstract