Microscopic Model Research Articles

Modeling and analysis of heterogeneous traffic flow with mixed regular and connected human-driven vehicles considering driver stochasticity

This paper presents a novel microscopic modeling framework to explore the impact of driver stochasticity and other related factors on heterogeneous traffic flows, consisting of both regular human-driven vehicles (RHDVs) and connected human-driven vehicles (CHDVs). We apply the stochastic optimal velocity car-following model for RHDVs, while an extended version of the stochastic continuous car-following model, taking into account optimal velocity guidance and driver compliance, is devised for CHDVs. The stability of CHDV car-following model is examined theoretically and numerically. Simulation experiments are conducted to analyze the stochastic heterogeneous traffic flow properties (i.e. stability and safety) with the proposed microscopic modeling framework under diverse parameter settings including CHDV penetration rate, driver compliance, and driver stochasticity. Results show that traffic flow stability and safety improve with the increase in CHDV penetration rate and driver compliance but deteriorate as the driver’s stochasticity strength increases. CHDVs significantly enhance traffic stability when the CHDV penetration rate or driver compliance exceeds a certain threshold, typically ranging between 0.5 and 0.7. Additionally, there is an interaction between driver stochasticity and CHDV penetration rate in their effect on traffic flow stability, where the contribution of stochasticity to traffic oscillations first increases and then decreases as the penetration rate rises. When the penetration rate is between 0 and 0.4, the traffic risk in emergency situations can be significantly reduced as the penetration rate increases. These findings may guide the management and control strategies of heterogeneous traffic flow.

Impact of nuclear level density and radiative strength function models on the S factors of (α,γ) reactions relevant to p-nuclei production

Abstract 
Abstract. The astrophysical S factors of (α,γ) reactions are crucial inputs to nuclear reaction network simulations for studying p-nuclei production. Previous work revealed considerable discrepancies between theoretical and experimental S factors using different nuclear level densities (NLD) and radiative strength functions (RSF). Accordingly, in this article, we studied the influence of six RSF and four NLD models on the astrophysical S factors of (α, γ) reactions on 16 target nuclei. The microscopic NLD and RSF models under investigation are based on the Hartree-Fock + Bardeen-Cooper- Schrieffer (HFBCS) and Hartree-Fock-Bogulubov (HFB) models. The obtained results have shown that within the α-OMP proposed in Ref. [1], RSF and NLD models used in the S factor computation yield root mean square (rms) deviations of less than 0.750, and phenomenological NLD and RSF models predict astrophysical S better than microscopic ones in all cases considered. More research is needed on NLD and RSF models to improve prediction accuracy and reduce deviations in calculations of the S factors of radiative α captures.

Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs

Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights’ hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights’ KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters’ values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown.

Characteristics of Supercritical CO2 Non-Mixed Phase Replacement in Intraformational Inhomogeneous Low-Permeability Reservoirs

Under the influence of the sedimentation process, the phenomenon of intraformational non-homogeneity is widely observed in low-permeability reservoirs. In the development process of water and gas replacement (WAG), the transport law of water and gas and the distribution of residual oil are seriously affected by the non-homogeneity of reservoir properties. In this paper, a study on two types of reservoirs with certain lengths and thicknesses is carried out, and a reasonable development method is proposed according to the characteristics of each reservoir. Firstly, through indoor physical simulation experiments combined with low-field nuclear magnetic resonance scanning (NMR), this study investigates the influence of injection rate and core length on the double-layer low-permeability inhomogeneous core replacement and pore throat mobilization characteristics. Then, a two-layer inhomogeneous low-permeability microscopic model is designed to investigate the model’s replacement and pore throat mobilization characteristics under the combined influence of rhythmites, gravity, the injection rate, etc. Finally, based on the results of the core replacement and numerical simulation, a more reasonable development method is proposed for each type of reservoir. The results show that for inhomogeneous cores of a certain length, the WAG process can significantly increase the injection pressure and effectively seal the high-permeability layer through the Jamin effect to improve the degree of recovery. Moreover, for positive and reverse rhythm reservoirs of a certain thickness, the injection rate can be reduced according to the physical properties of the reservoir, and the gravity overburden phenomenon of the gas is used to achieve the effective development of the upper layers. The effect of the development of a positive rhythm reservoir therefore improved significantly. These findings provide data support for improving the development effectiveness of CO2 in low-permeability inhomogeneous reservoirs and emphasize the importance of the influence of multiple factors, such as injection flow rate, gravity, and rhythm, in CO2 replacement.

Microscopic Modeling Techniques for Steel Fiber Reinforced Concrete: Applications and Future Directions

This article reviews micro-modeling methods for steel fiber-reinforced concrete (SFRC), including finite element, discrete element, and computational fluid dynamics approaches, to analyze its mechanical and structural behavior. The models offer predictive insights on stress response, crack propagation, and durability in SFRC, particularly in high-stress structural applications. This study discusses the limitations of current modeling practices, such as high computational demand and parameter determination challenges, and suggests future improvements to enhance simulation accuracy and scalability. This review aims to support the advancement of SFRC in structural engineering applications.

Revealing the Microscopic Mechanism of Elementary Vortex Pinning in Superconductors

Vortex pinning is a crucial factor that determines the critical current of practical superconductors and enables their diverse applications. However, the underlying mechanism of vortex pinning has long been elusive, lacking a clear microscopic explanation. Here, using high-resolution scanning tunneling microscopy, we studied single vortex pinning induced by point defect in layered FeSe-based superconductors. We found the defect-vortex interaction drives low-energy vortex bound states away from EF, creating a “mini” gap that effectively lowers the system energy and enhances pinning. By measuring the local density of states, we directly obtained the elementary pinning energy and estimated the pinning force via the spatial gradient of pinning energy. The results are consistent with bulk critical current measurement. Furthermore, we showed that a general microscopic quantum model incorporating defect-vortex interaction can naturally capture our observation. It suggests that the local pairing near pinned vortex core is actually enhanced compared to unpinned vortex, which is beyond the traditional understanding that nonsuperconducting regions pin vortices. Our study thus unveils a general microscopic mechanism of vortex pinning in superconductors and provides insights for enhancing the critical current of practical superconductors. Published by the American Physical Society 2024

Electrostatic-elastic coupling in colloidal crystals

Abstract Electrostatic-elastic coupling in colloidal crystals, composed of a mobile Coulomb gas permeating a fixed background crystalline lattice of charged colloids, is studied on the continuum level in order to analyze the lattice-mediated interactions between mobile charges. The linearized, Debye-H{" u}ckel-like mean-field equations incorporating a minimal coupling between electrostatic and displacement fields imply an additional effective attractive interaction between mobile charges. For small screening lengths, the interactions between like mobile charges exhibit colloid lattice-mediated effective interaction, ranging from weak to strong attraction, while for large screening lengths the lattice mediated interaction is purely attractive. Continuum theory incorporating the standard lattice elasticity and electrostatics of mobile charges, augmented by the minimal electrostatic-elastic coupling terms, can serve as a baseline for more detailed microscopic models.

Rapid and Specific Detection of Bacillus cereus Using Phage Protein-Based Lateral Flow Assays.

Rapid and precise diagnostic techniques are essential for identifying foodborne pathogens, including Bacillus cereus (B. cereus), which poses significant challenges to food safety. Traditional detection methods are limited by long incubation times and high costs. In this context, gold nanoparticle (AuNP)-based lateral flow assays (LFAs) are emerging as valuable tools for rapid screening. However, the use of antibodies in LFAs faces challenges, including complex production processes, ethical concerns, or variability. Here, we address these challenges by proposing an innovative approach using bacteriophage-derived proteins for pathogen detection on LFAs. We used the engineered endolysin cell-wall-binding domain (CBD) and distal tail proteins (Dit) from bacteriophages that specifically target B. cereus. The protein-binding properties, essential for the formation of efficient capture and detection biointerfaces in LFAs, were extensively characterized from the microstructural to the LFA device level. Machine-learning models leverage knowledge of the protein sequence to predict advantageous protein orientations on the nitrocellulose membrane and AuNPs. The study of the biointerface binding quantified the degree of attachment of AuNPs to bacteria, providing, for the first time, a microscopic model of the number of AuNPs binding to bacteria. It highlighted the binding of up to one hundred 40 nm AuNPs per bacterium in conditions mimicking LFAs. Eventually, phage proteins were demonstrated as efficient bioreceptors in a straightforward LFA prototype combining the two proteins, providing a rapid colorimetric response within 15 min upon the detection of 105 B. cereus cells. Recombinantly produced phage binding proteins present an opportunity to generate a customizable library of proteins with precise binding capabilities, offering a cost-effective and ethical alternative to antibodies. This study enhances our understanding of phage protein biointerfaces, laying the groundwork for their utilization as efficient bioreceptors in LFAs and rapid point-of-care diagnostic assays, thus potentially strengthening public health measures.

Research on the Blocking Mechanism of Stagnant Water and the Prediction of Scaling Trend in Fractured Reservoirs in Keshen Gas Field

In this paper, well Keshen 221 was taken as the research object. The stagnant water–rock static experiment showed that, after 8 weeks of the residual water–rock static reaction, the pore size of the inner profile of the rock slice increased from 5 μm to 90 μm, and calcium carbonate crystals were deposited in the hole. Combined with the microscopic visualization model, it is observed that the reservoir blockage mostly occurs at the pore throat diameter, and the small fracture (30 μm) is blocked first, then the large fracture (50 μm). So, it is inferred that the blockage of the reservoir flow channel is caused by the migration of the crystals precipitated by the interaction between the stagnant water and the reservoir rock. On this basis, the TOUGHREACT reservoir model was further constructed to simulate the scaling of the stagnant water in the reservoir matrix and used to compare the scaling of the fractures with 7% and 30% porosity and the retained water at 0.658 m and 768 m. The pre-results of reservoir scaling show that the scaling is more serious when the fractures occur in the far well zone than when the fractures occur in the well entry zone. At the same location, the deposition of large fractures is six times that of small fractures, and the scaling is more severe in large fractures.

Understanding gravitationally induced decoherence parameters in neutrino oscillations using a microscopic quantum mechanical model

In this work, a microscopic quantum mechanical model for gravitationally induced decoherence introduced by Blencowe and Xu is investigated in the context of neutrino oscillations. The focus is on the comparison with existing phenomenological models and the physical interpretation of the decoherence parameters in such models. The results show that for neutrino oscillations in vacuum gravitationally induced decoherence can be matched with phenomenological models with decoherence parameters of the form Γ ij ∼ Δ m 4 ij E -2. When matter effects are included, the decoherence parameters exhibit a dependence on the varying matter density across the Earth layers. This behavior can be explained by the nature of the coupling between neutrinos and the gravitational wave environment, as suggested by linearised gravity. On a theoretical level, these different models can be characterised by a different choice of Lindblad operators, with the model with decoherence parameters that do not include matter effects being less suitable from the point of view of linearised gravity. Consequently, in the case of neutrino oscillations in matter, the microscopic model does not agree with many existing phenomenological models that assume constant decoherence parameters in matter. Nonetheless, we identify the KamLAND experimental setup as particularly well-suited to establish the first experimental constraints on the model parameters, namely the neutrino coupling to the gravitational wave environment and its temperature, based on a prior analysis using the phenomenological model.

Research on the microscopic mechanism and model applicability differences of CH4 and CO2 adsorption based on molecular dynamics

To verify the accuracy and rationality of the molecular simulation model, the microscopic mechanism of gas adsorption was analyzed and clarified from a microscopic perspective. The effect of molecular models created by direct construction and supercell expansion on the adsorption capacities of CH4 and CO2 has been analyzed. Eight coal-based molecular configurations were created to simulate the isothermal adsorption process of CH4 and CO2 using various coal-based molecular configurations. Using physical experiments, the validity of different construction methods for coal-based molecular structures was verified. The direct construction method was chosen to simulate the effect of various coal-based molecular numbers on the adsorption capacity of CH4 and CO2. The results show that the adsorption capacity of CO2 on coal is greater than that of CH4. The isothermal adsorption curves of CH4 and CO2 in coal-based molecular systems constructed using different methods reveal that the adsorption of methane on coal molecules adheres to the Langmuir monolayer adsorption theory. The adsorption sites for methane increase with the rise in free volume. The structure directly constructed and the supercell expansion directly determines the amount of methane adsorbed by each coal based molecule. The adsorption constants of CH4 and CO2 on the supercell extended structure are more volatile than those on the directly constructed structure, and the structure constructed directly for different coal based molecules is better than the supercell extended structure. The simulation using the direct construction method shows that as the number of coal-based molecules in the molecular configuration increases, The isothermal adsorption quantities of CH4 and CO2 increase linearly. The molecular configuration is not affected by the number of anthracite molecules. Molecular configuration is not affected by the number of anthracite molecules. The rationality of model construction has no obvious relationship with the number of coal based molecules.

What to expect from microscopic nuclear modelling for keff calculations?

What to expect from microscopic nuclear modelling for keff calculations?

Electrolyte Effects in Electrocatalytic Kinetics†

Comprehensive SummaryTuning electrolyte properties is a widely recognized strategy to enhance activity and selectivity in electrocatalysis, drawing increasing attention in this domain. Despite extensive experimental and theoretical studies, debates persist about how various electrolyte components influence electrocatalytic reactions. We offer a concise review focusing on current discussions, especially the contentious roles of cations. This article further examines how different factors affect the interfacial solvent structure, particularly the hydrogen‐bonding network, and delves into the microscopic kinetics of electron and proton‐coupled electron transfer. We also discuss the overarching influence of solvents from a kinetic modeling perspective, aiming to develop a robust correlation between electrolyte structure and reactivity. Lastly, we summarize ongoing research challenges and suggest potential directions for future studies on electrolyte effects in electrocatalysis. Key ScientistsIn 1956, Marcus theory was developed to describe the mechanism of outer‐sphere electron transfer (OS‐ET). In 1992, Nocera et al. directly measured proton‐coupled electron transfer (PCET) kinetics for the first time, and their subsequent research in 1995 investigated the effects of proton motion on electron transfer (ET) kinetics. In 1999 and 2000, Hammes‐schiffer et al. developed the multistate continuum theory for multiple charge reactions and deduced the rate expressions for nonadiabatic PCET reactions in solution, laying the theoretical foundation for the analysis of PCET kinetics in electrochemical processes. In 2006, Saveant et al. verified the concerted proton and electron transfer (CPET) mechanism in the oxidation of phenols coupled with intramolecular amine‐driven proton transfer (PT). Their subsequent work in 2008 reported the pH‐dependent pathways of electrochemical oxidation of phenols.Electrolyte effects in electrocatalysis have gained emphasis in recent years. In 2009, Markovic's pioneering work proposed non‐covalent interactions between hydrated alkaline cations and adsorbed OH species in oxygen reduction reaction (ORR)/hydrogen oxidation reaction (HOR). In 2011, Markovic et al. significantly enhanced hydrogen evolution reaction (HER) activity in alkaline solution by improving water dissociation, which was assumed to dominate the sluggish HER kinetics in such media. In comparation, Yan et al. applied hydrogen binding energy (HBE) theory in 2015 to explain the pH‐dependent HER/HOR activity. Cations play a significant role in regulating the selectivity and activity of carbon dioxide reduction (CO2RR). In 2016 and 2017, Karen Chan et al. introduced the electric field generated by solvated cations to explain the cation effects on electrochemical CO2RR. Conversely, in 2021, Koper et al. suggested that short‐range electrostatic interactions between partially desolvated metal cations and CO2 stabilized CO2 and promoted CO2RR.Recent researches have combined the exploration of the electrical double layer (EDL) structure with theoretical analysis of PCET kinetics. In 2019, Huang et al. developed a microscopic Hamiltonian model to quantitatively understand the sluggish hydrogen electrocatalysis in alkaline media. In 2021, two meticulous studies from Shao‐Horn's group analyzed the effects of cations on reorganization energy and the impacts of hydrogen bonds between proton donors and acceptors on proton tunneling kinetics, respectively. Electrolyte effects on proton transport process were researched in recent years. In 2022, Hu et al. and Chen et al. proposed that the cation‐induced electric field distribution and pH‐dependent hydrogen bonding network connectivity played essential roles in proton transport, separately.

Do the sparser access points have less impact on arterial traffic? A microscopic simulation-based study

A dynamic microscopic traffic flow simulation within a ring arterial context was developed to investigate the effects of access point spacing on urban arterial flow under a right-in-right-out access management system. The microscopic traffic flow model, centered on car-following and lane-changing behaviors, was established based on vehicle interactions. The car-following aspect encompasses free driving, car-following behavior, and deceleration and braking states while lane-changing considerations include decision-making and acceptable gap assessment. Experimental scenarios account for arterial traffic density, access traffic demand intensity, average access point spacing, and variation coefficient of access point spacing. The traffic flow and speeds within the ring arterial were evaluated across 5040 operational conditions (equating to 5880 simulation hours). The traffic flow trends and speed variations with density across different access spacing scenarios were analyzed. We made an intriguing discovery: the impact on arterial traffic flow increases with larger average access point spacing, challenging conventional traffic planning recommendations that advocate for greater spacing. Additionally, access traffic minimally affects the overall arterial flow when arterial traffic volume is low. By highlighting these critical insights, this study introduces novel considerations for designing and managing access points.

Identifying the role of route choice behavior of motorcycle riders in microscopic simulation modeling under mixed traffic conditions

Microscopic simulation models are pivotal in transportation planning for their ability to intricately capture complex traffic patterns. This study specifically explores the role of dynamic route choice behavior within micro-simulation frameworks, emphasizing its application in the context of motorcycle-dominated traffic in Ninh Kiều District, Cần Thơ, Vietnam. By integrating discrete choice models for route selection with rigorous calibration and validation in AIMSUN, the study developed a comprehensive traffic simulation model for large-scale networks. Findings reveal that motorcycles dynamically adjust routes based on current traffic conditions rather than adhering strictly to shortest path strategies. However, the availability of roadside traffic information systems accessible to motorcycles in developing countries is limited, hindering optimal route choices. To address this gap and encourage the enhancement of route choice behavior, the study evaluates the impact of real-time traffic information provision using Macroscopic Fundamental Diagrams within the micro-simulation model. The results demonstrate significant improvements in network efficiency when informed route selection behaviors are facilitated, particularly with comprehensive coverage across all road types. Higher compliance rates notably improved network capacity by up to 84%. Overall, this study contributes methodologically by increasing model precision and practical insights into managing heterogeneous traffic environments, highlighting the transformative potential of traffic information systems and advocating for broader implementation in similar motorcycle-centric regions, which currently still rarely extend beyond toll roads. Future research should extend these methodologies to diverse urban contexts for broader validation and application.

Linear magnetoresistance from glassy orders

Several strongly correlated metals display B-linear magnetoresistance (LMR) with a universal slope, in sharp contrast to the [Formula: see text] scaling predicted by Fermi liquid theory. We provide a unifying explanation of the origin of LMR by focusing on a common feature in their phase diagrams-proximity to symmetry-breaking orders. Specifically, we demonstrate via two microscopic models that LMR with a universal slope arises ubiquitously near ordered phases, provided the order parameter either i) has a finite wave-vector, or ii) has nodes on the Fermi surface. We elucidate the distinct physical mechanisms at play in these two scenarios and derive upper and lower bounds on the field range for which LMR is observed. Finally, we discuss possible extensions of our picture to strange metal physics at higher temperatures and argue that our theory provides an understanding of recent experimental results on thin film cuprates and moiré materials.

Effect of electrostatic confinement on the dome-shaped superconducting phase diagram at the LaAlO3/SrTiO3 interface

The two-dimensional electron gas (2DEG) at the LaAlO\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_3$$\\end{document}/SrTiO\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_3$$\\end{document} (LAO/STO) interface exhibits gate-tunable superconductivity with a dome-like shape of critical temperature as a function of electron concentration. This behavior has not been unambiguously explained yet. Here, we develop a microscopic model based on the Schrödinger–Poisson approach to determine the electronic structure of the LAO/STO 2DEG, which we then apply to study the principal characteristics of the superconducting phase within the real-space pairing mean-field approach. For the electron concentrations reported in the experiment, we successfully reproduce the dome-like shape of the superconducting gap. According to our analysis such behavior results from the interplay between the Fermi surface topology and the gap symmetry, with the dominant extended s-wave contribution. Similarly as in the experimental report, we observe a bifurcation effect in the superconducting gap dependence on the electron density when the 2DEG is electrostatically doped either with the top gate or the bottom gate. Our findings explains the dome-shaped phase diagram of the considered heterostucture with good agreement with the experimental data which, in turn, strongly suggest the appearance of the extended s-wave symmetry of the gap in 2DEG at the LAO/STO interface.

Understanding and simulating mechanochromism in dye-dispersed polymer blends: from atomistic insights to macroscopic properties.

In this work, we propose a computational protocol enabling the simulation of mechanochromic responses in dye-dispersed polymer blends. The main objective is the modeling of the molecular-level structural changes responsible for the modulation of the photophysical properties that lead to the mechanochromic phenomenon. In this demonstrative study, we focus on predicting the changes in optical absorption displayed by a model system consisting of a dimer of a tetraphenylethylene derivative dispersed in a polyethylene matrix. The blend is subjected to an external stimulus that causes a modulation of the polymer matrix density that translates, in turn, into the emergence of specific mechanical constraints on the optically active dimers. The accurate description of this phenomenon requires the reliable sampling of the dimer configurations induced by the interaction with the matrix under stress. These molecular geometries are associated with modified electronic structures that confer novel absorption responses to the dispersed dyes. In the present contribution, the sampling of these structures is achieved through classical molecular dynamics (MD) simulations including a model element to apply an anisotropic mechanical force. This element allows the microscopic modeling of the chains' and dyes' structural rearrangements under stress. After the sampling, we compare the results of two approaches for the prediction of the optical response: (i) the calculation of a mean response from a statistical average over quantum chemical calculations on the sampled MD structures and (ii) a prediction via a more expensive hybrid scheme allowing the relaxation of the sampled molecular geometries in the presence of the matrix constraints.

Opening the species box: What parsimonious microscopic models of speciation have to say about macroevolution.

In the last two decades, lineage-based models of diversification, where species are viewed as particles that can divide (speciate) or die (become extinct) at rates depending on some evolving trait, have been very popular tools to study macroevolutionary processes. Here, we argue that this approach cannot be used to break down the inner workings of species diversification and that "opening the species box" is necessary to understand the causes of macroevolution, but that too detailed speciation models also fail to make robust macroevolutionary predictions. We set up a general framework for parsimonious models of speciation that rely on a minimal number of mechanistic principles: (i) reproductive isolation is caused by excessive dissimilarity between genotypes; (ii) dissimilarity results from a balance between differentiation processes and homogenizing processes; and (iii) dissimilarity can feed back on these processes by decelerating homogenization. We classify such models according to the main homogenizing process : (1) clonal evolution models (ecological drift), (2) models of genetic isolation (gene flow) and (3) models of isolation by distance (spatial drift). We review these models and their specific predictions on macroscopic variables such as species abundances, speciation rates, interfertility relationships or phylogenetic tree structure. We propose new avenues of research by displaying conceptual questions remaining to be solved and new models to address them: the failure of speciation at secondary contact, the feedback of dissimilarity on homogenization, the emergence in space of breeding barriers.

Bayesian metamodeling of early T-cell antigen receptor signaling accounts for its nanoscale activation patterns.

T cells respond swiftly, specifically, sensitively, and robustly to cognate antigens presented on the surface of antigen presenting cells. Existing microscopic models capture various aspects of early T-cell antigen receptor (TCR) signaling at the molecular level. However, none of these models account for the totality of the data, impeding our understanding of early T-cell activation. Here, we study early TCR signaling using Bayesian metamodeling, an approach for systematically integrating multiple partial models into a metamodel of a complex system. We inform the partial models using multiple published super-resolution microscopy datasets. Collectively, these datasets describe the spatiotemporal organization, activity, interactions, and dynamics of TCR, CD45 and Lck signaling molecules in the early-forming immune synapse, and the concurrent membrane alterations. The resulting metamodel accounts for a distinct nanoscale dynamic pattern that could not be accounted for by any of the partial models on their own: a ring of phosphorylated TCR molecules, enriched at the periphery of early T cell contacts and confined by a proximal ring of CD45 molecules. The metamodel suggests this pattern results from limited activity range for the Lck molecules, acting as signaling messengers between kinetically-segregated TCR and CD45 molecules. We assessed the potential effect of Lck activity range on TCR phosphorylation and robust T cell activation for various pMHC:TCR association strengths, in the specific setting of an initial contact. We also inspected the impact of localized Lck inhibition via Csk recruitment to pTCRs, and that of splicing isoforms of CD45 on kinetic segregation. Due to the inherent scalability and adaptability of integrating independent partial models via Bayesian metamodeling, this approach can elucidate additional aspects of cell signaling and decision making.