Multiscale Modeling Study Research Articles

Intraglomerular crosstalk elaborately regulates podocyte injury and repair in diabetic patients: insights from a 3D multiscale modeling study

Podocytes are mainly involved in the regulation of glomerular filtration rate (GFR) under physiological condition. Podocyte depletion is a crucial pathological alteration in diabetic nephropathy (DN) and results in a broad spectrum of clinical syndromes such as protein urine and renal insufficiency. Recent studies indicate that depleted podocytes can be regenerated via differentiation of the parietal epithelial cells (PECs), which serve as the local progenitors of podocytes. However, the podocyte regeneration process is regulated by a complicated mechanism of cell-cell interactions and cytokine stimulations, which has been studied in a piecemeal manner rather than systematically. To address this gap, we developed a high-resolution multi-scale multi-agent mathematical model in 3D, mimicking the in situ glomerulus anatomical structure and micro-environment, to simulate the podocyte regeneration process under various cytokine perturbations in healthy and diabetic conditions. Our model showed that, treatment with pigment epithelium derived factor (PEDF) or insulin-like growth factor-1 (IGF-1) alone merely ameliorated the glomerulus injury, while co-treatment with both cytokines replenished the damaged podocyte population gradually. In addition, our model suggested that continuous administration of PEDF instead of a bolus injection sustained the regeneration process of podocytes. Part of the results has been validated by our in vivo experiments. These results indicated that amelioration of the glomerular stress by PEDF and promotion of PEC differentiation by IGF-1 are equivalently critical for podocyte regeneration. Our 3D multi-scale model represents a powerful tool for understanding the signaling regulation and guiding the design of cytokine therapies in promoting podocyte regeneration.

Active multi-scale modeling and gas permeability study of porous metal fiber sintered felt for proton exchange membrane fuel cells

Porous metal fiber sintered felt (PMFSF) is a promising critical component in proton exchange membrane fuel cells, having the ability of simultaneously acting as the flow field plate, gas diffusion layers and also the catalyst layers support, and owing the property of multi-scale surface morphology. A simple multi-scale mathematical method was proposed to actively construct three dimensional models of PMFSF’s microstructure by synthesizing the implicit periodic surface (PS) model and the Weierstrass-Mandelbrot (W-M) fractal geometry. In this method, the PS model described the macro overall fiber shape, and the W-M fractal geometry modeled the micro fractal roughness topography attached. Based on the method, multi-scale fractal PMFSF models were reconstructed according to morphology parameters of physical PMFSFs, and were discretized in ANSYS/ICEM to generate refined mesh for computational fluid dynamics analysis. To verify the validity of the proposed modeling approach, PMFSFs with different porosity and fiber orientation are generated, and then the effects of the fractal surface topography and the fractal parameters such as fractal dimension and height scaling parameter on the gas permeability of PMFSF were investigated. The numeric simulation results show that the influence of the fractal topography on the in-plane and through-plane permeability of PMFSF cannot be ignored, and the permeability of fractal PMFSF models agrees with experimental measurements better. Especially, the results imply that the fractal morphology may have the potential to adjust the anisotropic properties of PMFSFs’ permeability. It is further found that the larger the fractal dimension is and the lower the height scaling parameter is, the better the permeability of PMFSF will be. The synthesis approach and numerical simulation method may facilitate the development of active functional design mode to predict and optimize key characteristics of PMFSF ahead of manufacture.

Multi-scale habitat selection modeling: a review and outlook

Scale is the lens that focuses ecological relationships. Organisms select habitat at multiple hierarchical levels and at different spatial and/or temporal scales within each level. Failure to properly address scale dependence can result in incorrect inferences in multi-scale habitat selection modeling studies. Our goals in this review are to describe the conceptual origins of multi-scale habitat selection modeling, evaluate the current state-of-the-science, and suggest ways forward to improve analysis of scale-dependent habitat selection. We reviewed more than 800 papers on habitat selection from 23 major ecological journals published between 2009 and 2014 and recorded a number of characteristics, such as whether they addressed habitat selection at multiple scales, what attributes of scale were evaluated, and what analytical methods were utilized. Our results show that despite widespread recognition of the importance of multi-scale analyses of habitat relationships, a large majority of published habitat ecology papers do not address multiple spatial or temporal scales. We also found that scale optimization, which is critical to assess scale dependence, is done in less than 5 % of all habitat selection modeling papers and less than 25 % of papers that address “multi-scale” habitat analysis broadly defined. Our review confirms the existence of a powerful conceptual foundation for multi-scale habitat selection modeling, but that the majority of studies on wildlife habitat are still not adopting multi-scale frameworks. Most importantly, our review points to the need for wider adoption of a formal scale optimization of organism response to environmental variables.

Helium segregation on surfaces of plasma-exposed tungsten

We report a hierarchical multi-scale modeling study of implanted helium segregation on surfaces of tungsten, considered as a plasma facing component in nuclear fusion reactors. We employ a hierarchy of atomic-scale simulations based on a reliable interatomic interaction potential, including molecular-statics simulations to understand the origin of helium surface segregation, targeted molecular-dynamics (MD) simulations of near-surface cluster reactions, and large-scale MD simulations of implanted helium evolution in plasma-exposed tungsten. We find that small, mobile Hen (1 ⩽ n ⩽ 7) clusters in the near-surface region are attracted to the surface due to an elastic interaction force that provides the thermodynamic driving force for surface segregation. This elastic interaction force induces drift fluxes of these mobile Hen clusters, which increase substantially as the migrating clusters approach the surface, facilitating helium segregation on the surface. Moreover, the clusters’ drift toward the surface enables cluster reactions, most importantly trap mutation, in the near-surface region at rates much higher than in the bulk material. These near-surface cluster dynamics have significant effects on the surface morphology, near-surface defect structures, and the amount of helium retained in the material upon plasma exposure. We integrate the findings of such atomic-scale simulations into a properly parameterized and validated spatially dependent, continuum-scale reaction-diffusion cluster dynamics model, capable of predicting implanted helium evolution, surface segregation, and its near-surface effects in tungsten. This cluster-dynamics model sets the stage for development of fully atomistically informed coarse-grained models for computationally efficient simulation predictions of helium surface segregation, as well as helium retention and surface morphological evolution, toward optimal design of plasma facing components.

Influence of the “surface effect” on the segregation parameters of S in Fe(100): A multi-scale modelling and Auger Electron Spectroscopy study

The article takes a new look at the process of atomic segregation by considering the influence of surface relaxation on the segregation parameters; the activation energy (Q), segregation energy (ΔG), interaction parameter (Ω) and the pre-exponential factor (D0). Computational modelling, namely Density Functional Theory (DFT) and the Modified Darken Model (MDM) in conjunction with Auger Electron Spectroscopy (AES) was utilized to study the variation of the segregation parameters for S in the surface region of Fe(100). Results indicate a variation in each of the segregation parameters as a function of the atomic layer under consideration. Values of the segregation parameters varied more dramatically as the surface layer is approached, with atomic layer 2 having the largest deviations in comparison to the bulk values. This atomic layer had the highest Q value and formed the rate limiting step for the segregation of S towards the Fe(100) surface. It was found that the segregation process is influenced by two sets of segregation parameters, those of the surface region formed by atomic layer 2, and those in the bulk material. This article is the first to conduct a full scale investigation on the influence of surface relaxation on segregation and labelled it the “surface effect”.

Explaining irreversible hygroscopic strains in paper: a multi-scale modelling study on the role of fibre activation and micro-compressions

This paper proposes a meso-structural model for the discrete fibrous network of paper, which is able to upscale the irreversible phenomena from the underlying hygro-mechanics towards the effective behaviour at the macro-scale. The swelling of individual fibres, induced by moisture content variations, is transmitted to the network through inter-fibre bonds and governs the resulting effective material response. The present approach is based on a recently developed discrete meso-structural model for the reversible part of the response, which distinguishes between the influence on the hygroscopic behaviour of free-standing fibre segments and inter-fibre bonds. The network structure, fibre and bond geometries and hygro-elastic properties are explicitly incorporated. The major novelty of this contribution is the extended fibre constitutive model, enabling to describe some typical irreversible mechanisms and instability effects related to the history of the manufacturing. This extension strongly affects the effective material behaviour beyond the elastic range. Despite the valuable work in the literature, no papers can be found dedicated to meso-structural models including these phenomena. Using an appropriate homogenization strategy, the unit-cell is solved analytically, capturing irreversible shrinkage in restrained dried paper and the occurrence of local buckling within the bond for freely dried material. The proposed scale transition offers a deeper insight into the complex relation between the events occurring at the different length scales. The potential of the proposed methodology is demonstrated through the convincing agreement with experimental data obtained from the literature.

Effects of polymer graft properties on protein adsorption and transport in ion exchange chromatography: a multiscale modeling study.

Multiscale simulation is used to study the adsorption of lysozyme onto ion exchangers obtained by grafting charged polymers into a porous matrix, in systems with various polymer properties and strengths of electrostatic interaction. Molecular dynamics simulations show that protein partitioning into the polymer-filled pore space increases with the overall charge content of the polymers, while the diffusivity in the pore space decreases. However, the combination of greatly increased partitioning and modestly decreased diffusion results in macroscopic transport rates that increase as a function of charge content, as the large concentration driving force due to enhanced pore space partitioning outweighs the reduction in the pore space diffusivity. Matrices having greater charge associated with the grafted polymers also exhibit more diffuse intraparticle concentration profiles during transient adsorption. In systems with a high charge content per polymer and a low protein loading, the polymers preferentially partition toward the surface due to favorable interactions with the surface-bound protein. These results demonstrate the potential of multiscale modeling to illuminate qualitative trends between molecular properties and the adsorption equilibria and kinetic properties observable on macroscopic scales.

Biofuel purification in GME zeolitic–imidazolate frameworks: From ab initio calculations to molecular simulations

A multiscale modeling study is reported on the adsorption of ethanol/water in five zeolitic–imidazolate frameworks (ZIFs) for biofuel purification. The ZIFs (ZIF‐68, −69, −78, −79, and −81) have isoreticular Gmelinite topology but differ in organic linkers. The simulated adsorption isotherms of ethanol and water in ZIF‐68 agree fairly well with experimental data. At a low pressure, ZIF‐78 exhibits the highest uptake due to strong hydrogen‐bonding between NO2 groups and adsorbates. The heats of adsorption at infinite dilution largely follow the trend of binding energies estimated from ab initio calculations. At a high pressure, the uptake is governed primarily by free volume but also affected by hydrogen‐bonding. Among the five ZIFs, ZIF‐79 with hydrophobic CH3 groups shows the highest adsorptive selectivity for ethanol/water mixtures. This study provides microscopic insights into the adsorption and separation of ethanol/water in ZIFs, and would facilitate the development of new ZIFs for biofuel purification. © 2015 American Institute of Chemical Engineers AIChE J, 61: 2763–2775, 2015

A multiscale modeling study for the convective mass transfer in a subsurface aquifer

Quantitative and realistic computer simulations of mass transfer associated with \(\hbox {CO}_2\) disposal in subsurface aquifers is a challenging endeavor. This article has proposed a novel and efficient multiscale modeling framework, and has examined its potential to study the penetrative mass transfer in a \(\hbox {CO}_2\) plume that migrates in an aquifer. Numerical simulations indicate that the migration of the injected \(\hbox {CO}_2\) enhances the vorticity generation, and the dissolution of \(\hbox {CO}_2\) has a strong effect on the natural convection mass transfer. The vorticity decays with the increase of the porosity. The time scale of the vertical migration of a \(\hbox {CO}_2\) plume is strongly dependent on the rate of \(\hbox {CO}_2\) dissolution. Comparisons confirm the near optimal performance of the proposed multiscale model. These primary results with an idealized computational model of the \(\hbox {CO}_2\) migration in an aquifer brings the potential of the proposed multiscale model to the field of heat and mass transfer in the geoscience.

CO2 capture in rht metal–organic frameworks: multiscale modeling from molecular simulation to breakthrough prediction

A multiscale modeling study is reported for CO2 capture in rht metal–organic frameworks.

Prion Proteins and Mechanisms of Interaction with Model Membranes

Prions are infectious agents responsible for transmissible spongiform encephalopathies, a fatal neurodegenerative disease in mammals, including humans. Prions propagate biological information by conversion of the nonpathological version of the prion protein, PrPc, to the infectious conformation, PrPSc. To shed light on the biogenesis of PrPSc on the cell surface, we will report on multiscale molecular modeling studies of enthalpy-driven binding modes of PrPc to model membranes and the conformational response of PrPc to such binding events. Our preliminary results suggest the existence of preferential binding spots on the PrPc surface driven by favorable protein-membrane electrostatics interactions. Upon binding, the conformational space of PrPc is reduced.

Hemodynamic effects of left pulmonary artery stenosis after superior cavopulmonary connection: A patient-specific multiscale modeling study

Currently, no quantitative guidelines have been established for treatment of left pulmonary artery (LPA) stenosis. This study aims to quantify the effects of LPA stenosis on postoperative hemodynamics for single-ventricle patients undergoing stage II superior cavopulmonary connection (SCPC) surgery, using a multiscale computational approach. Image data from 6 patients were segmented to produce 3-dimensional models of the pulmonary arteries before stage II surgery. Pressure and flow measurements were used to tune a 0-dimensional model of the entire circulation. Postoperative geometries were generated through stage II virtual surgery; varying degrees of LPA stenosis were applied using mesh morphing and hemodynamics assessed through coupled 0-3-dimensional simulations. To relate metrics of stenosis to clinical classifications, pediatric cardiologists and surgeons ranked the degrees of stenosis in the models. The effects of LPA stenosis were assessed based on left-to-right pulmonary artery flow split ratios, mean pressure drop across the stenosis, cardiac pressure-volume loops, and other clinically relevant parameters. Stenosis of >65% of the vessel diameter was required to produce a right pulmonary artery:LPA flow split <30%, and/or a mean pressure drop of >3.0 mm Hg, defined as clinically significant changes. The effects of <65% stenosis on SCPC hemodynamics and physiology were minor and may not justify the increased complexity of adding LPA arterioplasty to the SCPC operation. However, in the longer term, pulmonary augmentation may affect outcomes of the Fontan completion surgery, as pulmonary artery distortion is a risk factor that may influence stage III physiology.

CO2 capture in cation-exchanged metal–organic frameworks: Holistic modeling from molecular simulation to process optimization

While CO2 capture has been extensively investigated in different metal–organic frameworks (MOFs), their performance under practical process conditions is scarcely examined. In this study, a multi-scale modeling study is reported to examine CO2 capture from flue gas by vacuum swing adsorption (VSA) process using cation-exchanged rho zeolite-like MOFs (rho-ZMOFs) as adsorbents. Three rho-ZMOFs namely Na-, Mg- and Al-rho-ZMOFs are considered and compared with 13X Zeolite, which is the current benchmark for post-combustion capture from dry flue gas. First, Monte Carlo (MC) simulations are conducted to estimate the adsorption isotherms of pure CO2 and N2 in the range from 0 to 100kPa and their mixtures of varying composition at a total pressure of 100kPa. The pure gas isotherms are then fitted by the dual-site Langmuir model. Subsequently, the extended dual-site Langmuir model is used to satisfactorily predict the equilibrium behavior of CO2/N2 binary mixtures over a wide range of composition. In addition, micropore diffusivities are calculated from molecular dynamics (MD) simulations and compared with the estimated macropore diffusivities in order to determine the controlling mechanism of mass transfer. Macropore is expected when these adsorbents are pelletized before using in a separation process. For each rho-ZMOF adsorbent, a four-step VSA process with light product pressurization for CO2 capture and concentration (CCC) from dry flue gas containing 15% CO2 in balance N2 is then simulated using a non-isothermal, non-isobaric model. The process is optimized using multi-objective optimization based on a genetic algorithm for minimizing the energy penalty and maximizing the process productivity, subject to the purity and recovery constraints of 95 and 90%, respectively. The operating spaces of the three rho-ZMOFs are similar to that of 13X zeolite. However, the minimum energy penalty in Al-rho-ZMOF (156kWh/t CO2) is lower than in 13X (165kWh/t CO2). While identifying the cation-exchanged rho-ZMOFs as interesting candidates for CO2 capture, this study also demonstrates that the multi-scale modeling approach adopted here is an effective methodology to screen and design novel MOFs for CCC and other separation applications.

Mechanism of the pH-Controlled Self-Assembly of Nanofibers from Peptide Amphiphiles.

Stimuli-responsive, self-assembling nanomaterials hold a great promise to revolutionize medicine and technology. However, current discovery is slow and often serendipitous. Here we report a multiscale modeling study to elucidate the pH-controlled self-assembly of nanofibers from the peptide amphiphiles, palmitoyl-I-A3E4-NH2. The coarse-grained simulations revealed the formation of random-coil based spherical micelles at strong electrostatic repulsion. However, at weak or no electrostatic repulsion, the micelles merge into a nanofiber driven by the β-sheet formation between the peptide segments. The all-atom constant pH molecular dynamics revealed a cooperative transition between random coil and β-sheet in the pH range 6–7, matching the CD data. Interestingly, although the bulk pKa is more than one unit below the transition pH, consistent with the titration data, the highest pKa’s coincide with the transition pH, suggesting that the latter may be tuned by modulating the pKa’s of a few solvent-buried Glu side chains. Together, these data offer, to our best knowledge, the first multiresolution and quantitative view of the pH-dependent self-assembly of nanofibers. The novel protocols and insights gained are expected to advance the computer-aided design and discovery of pH-responsive nanomaterials.

Role of atomic variability in dielectric charging: A first-principles-based multiscale modeling study

We present a dielectric charging model that combines ab initio calculations of trap levels with a continuum-level transport model and apply it to interpret charging currents in amorphous silicon nitride. Density functional theory calculations on an ensemble of structures predict a distribution of electron trap levels up to 1.8 eV below the conduction band edge and provide insight into the physical trapping mechanisms. Incorporating this information in the continuum model, as opposed to the standard approach of a single adjustable trap level, not only leads to a more accurate description of experimental current transients in metal-insulator-metal capacitors, but also to a more precise and physical determination of associated material properties such as metal-dielectric barrier height.

Toward the virtual stomach: progress in multiscale modeling of gastric electrophysiology and motility

Experimental progress in investigating normal and disordered gastric motility is increasingly being complimented by sophisticated multiscale modeling studies. Mathematical modeling has become a valuable tool in this effort, as there is an ever-increasing need to gain an integrative and quantitative understanding of how physiological mechanisms achieve coordinated functions across multiple biophysical scales. These interdisciplinary efforts have been particularly notable in the area of gastric electrophysiology, where they are beginning to yield a comprehensive and integrated in silico organ modeling framework, or 'virtual stomach'. At the cellular level, a number of biophysically based mathematical cell models have been developed, and these are now being applied in areas including investigations of gastric electrical pacemaker mechanisms, smooth muscle electrophysiology, and electromechanical coupling. At the tissue level, micro-structural models are being creatively developed and employed to investigate clinically significant questions, such as the functional effects of ICC degradation on gastrointestinal (GI) electrical activation. At the organ level, high-resolution electrical mapping and modeling studies are combined to provide improved insights into normal and dysrhythmic gastric electrical activation. These efforts are also enabling detailed forward and inverse modeling studies at the 'whole body' level, with implications for diagnostic techniques for gastric dysrhythmias. These recent advances, together with several others highlighted in this review, collectively demonstrate a powerful trend toward applying mathematical models to effectively investigate structure-function relationships and overcome multiscale challenges in basic and clinical GI research.

A multiscale modeling study of loss processes in block-copolymer-based solar cell nanodevices

Flexible photovoltaic devices possess promising perspectives in opto-electronic technologies, where high mobility and/or large-scale applicability are important. However, their usefulness in such applications is currently still limited due to the low level of optimization of their performance and durability. For the improvement of these properties, a better understanding and control of small-scale annihilation phenomena involved in the photovoltaic process, such as exciton loss and charge carrier loss, is necessary, which typically implicates multiple length- and time-scales. Here, we study the causes for their occurrence on the example of nanostructured diblock- and triblock-copolymer systems by making use of a novel solar-cell simulation algorithm and explore new routes to optimize their photovoltaic properties. A particular focus is set on the investigation of exciton and charge carrier loss phenomena and their dependence on the inter-monomeric interaction strength, chain architecture, and external mechanical loading. Our simulation results reveal that in the regime from low up to intermediate χ-parameters an increasing number of continuous percolation paths is created. In this parameter range, the internal quantum efficiency (IQE) increases up to a maximum, characterized by a minimum in the number of charge losses due to charge recombination. In the regime of high χ-parameters both block-copolymer systems form nanostructures with a large number of bottlenecks and dead ends. These lead to a large number of charge losses due to charge recombination, charge trapping, and a deteriorated exciton dissociation, resulting in a significant drop in the IQE. Moreover, we find that the photovoltaic performance of the triblock-copolymer material decreases with increasing mechanical loading, caused by a growing number of charge losses due to charge recombination and charge accumulation. Finally, we demonstrate that the process of charge trapping in defects can be reversed by changing the polarity of the electrodes, which confers these materials the ability to be used as charge storage media.

Multi-Scale Modeling Study of Barium Nitrate Reduction in NOx Traps

Nitrogen oxide (NOx) abatement from lean-burning engines is a major environmental challenge for the upcoming years. With increasing regulations in terms of NOx emission, only sophisticated engine aftertreatment techniques can meet the required emissions. Lean NOx Traps (LNT) are one of those few catalytic techniques that are potential candidates for a large-scale application. Their operation is based on alternating NOx storage cycles over a basic storage material with nitrate reducing cycles with an external reductant (CO, H2 and hydrocarbons). Because of the complex chemistry involved, modeling reactions taking place during storage-reduction cycling is still poorly understood. We address one of the key issues in kinetic modeling related to LNT regeneration that is the competition between direct and indirect reduction pathways. Recent experimental work has evidenced the formation of barium isocyanates but no relevant kinetic modeling has been done up to now. The present modelling study, based on multi-scale approaches (from DFT to kinetic modelling), shows that both direct (isocyanate) and indirect (decomposition) pathways may contribute significantly to nitrate reduction.

Predicting Agricultural Impacts of Large-Scale Drought: 2012 and the Case for Better Modeling

The 2012 growing season saw one of the worst droughts in a generation in much of the United States and cast a harsh light on the need for better analytic tools and a comprehensive approach to predicting and preparing for the effects of extreme weather on agriculture. We present an example of a simulation-based forecast for the 2012 US maize growing season produced as part of a high-resolution multi-scale predictive mechanistic modeling study designed for decision support, risk management, and counterfactual analysis. We estimate national average yields of 7.507 t/ha for 2012, 24.6% below the expected value based on increasing trend yield alone, with an interval based on resampled forecasts errors stretching from 5.586 to 8.967 t/ha. On average, the median yield simulations deviate from NASS observations by 8.3% from 1979 to 2011.

Functionalized metal–organic framework MIL-101 for CO2 capture: multi-scale modeling from ab initio calculation and molecular simulation to breakthrough prediction

By synergizing ab initio calculation, molecular simulation and breakthrough prediction, we investigate CO2 capture in metal–organic framework MIL-101 functionalized by a series of groups (–NH2, –CH3, –Cl, –NO2 and –CN). CO2 uptake and isosteric heat in a low-pressure regime increase in the order of MIL-101 < MIL-101-CN < MIL-101-NO2 < MIL-101-Cl < MIL-101-CH3 < MIL-101-NH2. This order follows the strength of the binding energies between CO2 and the functional groups. However, the effect of the functional groups is marginal for N2 adsorption. In terms of the separation of a CO2/N2 mixture, CO2/N2 selectivity is enhanced by functionalization following the order of MIL-101 < MIL-101-CN < MIL-101-CH3 < MIL-101-NO2 < MIL-101-Cl < MIL-101-NH2. At an infinite dilution, the enhancement of CO2/N2 selectivity is 2.5 times. The predicted breakthrough time is extended by functionalization, and the longest breakthrough time in MIL-101-NH2 is 2 times that in MIL-101. Furthermore, the working capacity of CO2 increases by approximately 40%. This multi-scale modeling study suggests that CO2 capture in MIL-101 can be considerably improved by functionalization, in terms of CO2 capacity, CO2/N2 selectivity, breakthrough time and working capacity.