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In a deep-learning-based algorithm, generative adversarial networks can generate images similar to an input. Using this algorithm, an artificial three-dimensional (3D) microstructure can be reproduced from two-dimensional images. Although the generated 3D microstructure has a similar appearance, its reproducibility should be examined for practical applications. This study used an automated serial sectioning technique to compare the 3D microstructures of two dual-phase steels generated from three orthogonal surface images with their corresponding observed 3D microstructures. The mechanical behaviors were examined using the finite element analysis method for the representative volume element, in which finite element models of microstructures were directly constructed from the 3D voxel data using a voxel coarsening approach. The macroscopic material responses of the generated microstructures captured the anisotropy caused by the microscopic morphology. However, these responses did not quantitatively align with those of the observed microstructures owing to inaccuracies in reproducing the volume fraction of the ferrite/martensite phase. Additionally, the generation algorithm struggled to replicate the microscopic morphology, particularly in cases with a low volume fraction of the martensite phase where the martensite connectivity was not discernible from the input images. The results demonstrate the limitations of the generation algorithm and the necessity for 3D observations.

This study provided the comparison between experimentally observed and computationally generated 3D microstructures of dual-phase steels in the macro- and microscopic material behaviors with finite element analysis method for periodic microstructure.

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The digestion of starch-based foods in the intestinal tract is important for human health. Modeling the details enhances fundamental understanding and glycemic prediction accuracy. It is, however, a challenge to take granular properties into account. A multiscale digestion model has been proposed to characterize mass transfer and hydrolysis reaction at both the intestine and particle scales, seamlessly integrating inter-scale mass exchange. A specific grid scheme was formulated for the shrinkage and transport of the particle computational domain. By incorporating additional glycemic-related processes, e.g., intestinal absorption, a dietary property-based glycemic prediction system has been developed. Its effectiveness was validated based on a human tolerance experiment of cooked rice particles. The model-based investigation comprehensively reveals the impact of initial size on digestion behavior, specifically in terms of enzyme distribution and particle evolution. This work also demonstrates the significance of modeling both particle-scale diffusion and intestine-scale transport, a combination not previously explored. The results indicate that ignoring the former mechanism leads to an overestimation of the glycemic peak by at least 50.8%, while ignoring the latter results in an underestimation of 16.3%.

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2'-deoxy-ATP (dATP) improves cardiac function by increasing the rate of crossbridge cycling and Ca[Formula: see text] transient decay. However, the mechanisms of these effects and how therapeutic responses to dATP are achieved when dATP is only a small fraction of the total ATP pool remain poorly understood. Here, we used a multiscale computational modeling approach to analyze the mechanisms by which dATP improves ventricular function. We integrated atomistic simulations of prepowerstroke myosin and actomyosin association, filament-scale Markov state modeling of sarcomere mechanics, cell-scale analysis of myocyte Ca[Formula: see text] dynamics and contraction, organ-scale modeling of biventricular mechanoenergetics, and systems level modeling of circulatory dynamics. Molecular and Brownian dynamics simulations showed that dATP increases the actomyosin association rate by 1.9 fold. Markov state models predicted that dATP increases the pool of myosin heads available for crossbridge cycling, increasing steady-state force development at low dATP fractions by 1.3 fold due to mechanosensing and nearest-neighbor cooperativity. This was found to be the dominant mechanism by which small amounts of dATP can improve contractile function at myofilament to organ scales. Together with faster myocyte Ca[Formula: see text] handling, this led to improved ventricular contractility, especially in a failing heart model in which dATP increased ejection fraction by 16% and the energy efficiency of cardiac contraction by 1%. This work represents a complete multiscale model analysis of a small molecule myosin modulator from single molecule to organ system biophysics and elucidates how the molecular mechanisms of dATP may improve cardiovascular function in heart failure with reduced ejection fraction.

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Numerical simulation of blood flow is a challenging topic due to the multiphase nature of this biological fluid. The choice of a specific method among the ones available in literature is often motivated by the physical scale of interest. Single-phase approximation allows for lower computational time, but does not consider this multiphase nature. Cell-level simulation, on the other hand, requires high computational resources and is limited to small scales. This work proposes a scale-up approach for cell-level simulation of blood flow, in the framework of unresolved CFD-DEM technique. This method offers the possibility to simulate hundreds of thousands of particles with limited computational effort, but requires specific models for fluid-particle interactions. Regarding blood flow, drag and lift force acting on the red blood cells (RBCs) are responsible for several macroscopic blood characteristics. Despite several correlations available for drag and lift force acting on rigid particles, specific force models for the simulation of deformable particles compatible with RBCs physics are missing. This study employs data obtained from cell-level simulations to derive equations then used in unresolved simulation of RBCs. The strategy followed during the modeling phase is presented, together with the model verification and validation. This approach returns satisfying results when used to simulate blood flow in large-scale channels. Up to half a million RBCs are considered, and computational effort is reported to allow a comparison with other existing methods. Future perspectives include further improvement of the model, such as a deeper understanding of particle-particle interactions.

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Meeting food safety requirements without jeopardizing quality attributes or sustainability involves adopting a holistic perspective of food products, their manufacturing processes and their storage and distribution practices. The virtualization of the food supply chain offers opportunities to evaluate, simulate, and predict challenges and mishaps potentially contributing to present and future food safety risks. Food systems virtualization poses several requirements: (1) a comprehensive framework composed of instrumental, digital, and computational methods to evaluate internal and external factors that impact food safety; (2) nondestructive and real-time sensing methods, such as spectroscopic-based techniques, to facilitate mapping and tracking food safety and quality indicators; (3) a dynamic platform supported by the Internet of Things (IoT) interconnectivity to integrate information, perform online data analysis and exchange information on product history, outbreaks, exposure to risky situations, etc.; and (4) comprehensive and complementary mathematical modeling techniques (including but not limited to chemical reactions and microbial inactivation and growth kinetics) based on extensive data sets to make realistic simulations and predictions possible. Despite current limitations in data integration and technical skills for virtualization to reach its full potential, its increasing adoption as an interactive and dynamic tool for food systems evaluation can improve resource utilization and rational design of products, processes and logistics for enhanced food safety. Virtualization offers affordable and reliable options to assist stakeholders in decision-making and personnel training. This chapter focuses on definitions and requirements for developing and applying virtual food systems, including digital twins, and their role and future trends in enhancing food safety.

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Composition is a powerful principle for systems biology, focused on the interfaces, interconnections, and orchestration of distributed processes. Whereas most systems biology models focus on the structure or dynamics of specific subsystems in controlled conditions, compositional systems biology aims to connect such models into integrative multiscale simulations. This emphasizes the space between models-a compositional perspective asks what variables should be exposed through a submodel's interface? How do coupled models connect and translate across scales? How can we connect domain-specific models across biological and physical research areas to drive the synthesis of new knowledge? What is required of software that integrates diverse datasets and submodels into unified multiscale simulations? How can the resulting integrative models be accessed, flexibly recombined into new forms, and iteratively refined by a community of researchers? This essay offers a high-level overview of the key components for compositional systems biology, including: 1) a conceptual framework and corresponding graphical framework to represent interfaces, composition patterns, and orchestration patterns; 2) standardized composition schemas that offer consistent formats for composable data types and models, fostering robust infrastructure for a registry of simulation modules that can be flexibly assembled; 3) a foundational set of biological templates-schemas for cellular and molecular interfaces, which can be filled with detailed submodels and datasets, and are designed to integrate knowledge that sheds light on the molecular emergence of cells; and 4) scientific collaboration facilitated by user-friendly interfaces for connecting researchers with datasets and models, and which allows a community of researchers to effectively build integrative multiscale models of cellular systems.

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Soft condensed matter is challenging to study due to the vast time and length scales that are necessary to accurately represent complex systems and capture their underlying physics. Multiscale simulations are necessary to study processes that have disparate time and/or length scales, which abound throughout biology and other complex systems. Herein we present ezAlign, an open-source software for converting coarse-grained molecular dynamics structures to atomistic representation, allowing multiscale modeling of biomolecular systems. The ezAlign v1.1 software package is publicly available for download at github.com/LLNL/ezAlign. Its underlying methodology is based on a simple alignment of an atomistic template molecule, followed by position-restraint energy minimization, which forces the atomistic molecule to adopt a conformation consistent with the coarse-grained molecule. The molecules are then combined, solvated, minimized, and equilibrated with position restraints. Validation of the process was conducted on a pure POPC membrane and compared with other popular methods to construct atomistic membranes. Additional examples, including surfactant self-assembly, membrane proteins, and more complex bacterial and human plasma membrane models, are also presented. By providing these examples, parameter files, code, and an easy-to-follow recipe to add new molecules, this work will aid future multiscale modeling efforts.

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A fundamental understanding of how HIV-1 envelope (Env) protein facilitates fusion is still lacking. The HIV-1 fusion peptide, consisting of 15 to 22 residues, is the N-terminus of the gp41 subunit of the Env protein. Further, this peptide, a promising vaccine candidate, initiates viral entry into target cells by inserting and anchoring into human immune cells. The influence of membrane lipid reorganization and the conformational changes of the fusion peptide during the membrane insertion and anchoring processes, which can significantly affect HIV-1 cell entry, remains largely unexplored due to the limitations of experimental measurements. In this work, we investigate the insertion of the fusion peptide into an immune cell membrane mimic through multiscale molecular dynamics simulations. We mimic the native T-cell by constructing a 9-lipid asymmetric membrane, along with geometrical restraints accounting for insertion in the context of gp41. To account for the slow timescale of lipid mixing while enabling conformational changes, we implement a protocol to go back and forth between atomistic and coarse-grained simulations. Our study provides a molecular understanding of the interactions between the HIV-1 fusion peptide and the T-cell membrane, highlighting the importance of conformational flexibility of fusion peptides and local lipid reorganization in stabilizing the anchoring of gp41 into the targeted host membrane during the early events of HIV-1 cell entry. Importantly, we identify a motif within the fusion peptide critical for fusion that can be further manipulated in future immunological studies.

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Pulmonary hypertension (PH) is a debilitating disease that alters the structure and function of both the proximal and distal pulmonary vasculature. This alters pressure-flow relationships in the pulmonary arterial and venous trees, though there is a critical knowledge gap in the relationships between proximal and distal hemodynamics in disease. Multiscale computational models enable simulations in both the proximal and distal vasculature. However, model inputs and measured data are inherently uncertain, requiring a full analysis of the sensitivity and uncertainty of the model. Thus, this study quantifies model sensitivity and output uncertainty in a spatially multiscale, pulse-wave propagation model of pulmonary hemodynamics. The model includes fifteen proximal arteries and twelve proximal veins, connected by a two-sided, structured tree model of the distal vasculature. We use polynomial chaos expansions to expedite sensitivity and uncertainty quantification analyses and provide results for both the proximal and distal vasculature. We quantify uncertainty in blood pressure, blood flow rate, wave intensity, wall shear stress, and cyclic stretch. The latter two are important stimuli for endothelial cell mechanotransduction. We conclude that, while nearly all the parameters in our system have some influence on model predictions, the parameters describing the density of the microvascular beds have the largest effects on all simulated quantities in both the proximal and distal arterial and venous circulations.

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CFRP hybrid bonded-bolted (HBB) joints combine the advantages of traditional joining methods, namely adhesive bonding, and bolting, to achieve optimal connection performance, making them the most favored connection method. The structural parameters of CFRP HBB joints, including overlap length, bolt-hole spacing, and fit clearance relationships, have a complex impact on connection performance. To enhance the connectivity performance of joint structures, this paper develops a multiscale finite element analysis model to investigate the impact of structural parameters on the strength of CFRP HBB joint structures. Coupled with experimental validation, the study reveals how changes in structural parameters affect the unidirectional tensile failure force of the joints. Building on this, an analytical approach and inverse design methodology for the mechanical properties of CFRP HBB joints based on deep supervised learning algorithms are developed. Neural networks accurately and efficiently predict the performance of joints with unprecedented combinations of parameters, thus expediting the inverse design process. This research combines experimentation and multiscale finite element analysis to explore the unknown relationships between the mechanical properties of CFRP HBB joints and their structural parameters. Furthermore, leveraging DNN neural networks, a rapid calculation method for the mechanical properties of hybrid joints is proposed. The findings lay the groundwork for the broader application and more intricate design of composite materials and their connection structures.

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Multiscale models provide a unique tool for studying complex processes that study events occurring at different scales across space and time. In the context of biological systems, such models can simulate mechanisms happening at the intracellular level such as signaling, and at the extracellular level where cells communicate and coordinate with other cells. They aim to understand the impact of genetic or environmental deregulation observed in complex diseases, describe the interplay between a pathological tissue and the immune system, and suggest strategies to revert the diseased phenotypes. The construction of these multiscale models remains a very complex task, including the choice of the components to consider, the level of details of the processes to simulate, or the fitting of the parameters to the data. One additional difficulty is the expert knowledge needed to program these models in languages such as C++ or Python, which may discourage the participation of non-experts. Simplifying this process through structured description formalisms - coupled with a graphical interface - is crucial in making modeling more accessible to the broader scientific community, as well as streamlining the process for advanced users. This article introduces three examples of multiscale models which rely on the framework PhysiBoSS, an add-on of PhysiCell that includes intracellular descriptions as continuous time Boolean models to the agent-based approach. The article demonstrates how to easily construct such models, relying on PhysiCell Studio, the PhysiCell Graphical User Interface. A step-by-step tutorial is provided as a Supplementary Material and all models are provided at: https://physiboss.github.io/tutorial/.

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Lung diseases such as cancer substantially alter the mechanical properties of the organ with direct impact on the development, progression, diagnosis, and treatment response of diseases. Despite significant interest in the lung's material properties, measuring the stiffness of intact lungs at sub-alveolar resolution has not been possible. Recently, we developed the crystal ribcage to image functioning lungs at optical resolution while controlling physiological parameters such as air pressure. Here, we introduce a data-driven, multiscale network model that takes images of the lung at different distending pressures, acquired via the crystal ribcage, and produces corresponding absolute stiffness maps. Following validation, we report absolute stiffness maps of the functioning lung at microscale resolution in health and disease. For representative images of a healthy lung and a lung with primary cancer, we find that while the lung exhibits significant stiffness heterogeneity at the microscale, primary tumors introduce even greater heterogeneity into the lung's microenvironment. Additionally, we observe that while the healthy alveoli exhibit strain-stiffening of ∼1.75 times, the tumor's stiffness increases by a factor of six across the range of measured transpulmonary pressures. While the tumor stiffness is 1.4 times the lung stiffness at a transpulmonary pressure of three cmH2O, the tumor's mean stiffness is nearly five times greater than that of the surrounding tissue at a transpulmonary pressure of 18 cmH2O. Finally, we report that the variance in both strain and stiffness increases with transpulmonary pressure in both the healthy and cancerous lungs. Our new method allows quantitative assessment of disease-induced stiffness changes in the alveoli with implications for mechanotransduction.

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This study investigates the application of quantum mechanical (QM) and multiscale computational methods in understanding the reaction mechanisms and kinetics of SN2 reactions involving methyl iodide with NH2OH and NH2O-, as well as the Claisen rearrangement of 8-(vinyloxy)dec-9-enoate. Our aim is to evaluate the accuracy and effectiveness of these methods in predicting experimental outcomes for these organic reactions. We achieve this by employing QM-only calculations and several hybrids of QM and molecular mechanics (MM) methods, namely QM/MM, QM1/QM2, and QM1/QM2/MM methodologies. For the SN2 reactions, our results demonstrate the importance of explicitly including solvent effects in the calculations to accurately reproduce the transition state geometry and energetics. The multiscale methods, particularly QM/MM and QM1/QM2, show promising performance in predicting activation energies. Moreover, we observe that the size of the MM active region significantly affects the accuracy of calculated activation energies, highlighting the need for careful consideration during the setup of multiscale calculations. In the case of the Claisen rearrangement, both QM-only and multiscale methods successfully reproduce the proposed reaction mechanism. However, the activation free energies calculated using a continuum solvation model, based on single-point calculations of QM-only structures, fail to account for solvent effects. On the other hand, multiscale methods more accurately capture the impact of solvents on activation free energies, with systematic error correction enhancing the accuracy of the results. Furthermore, we introduce a Python code for setting up multiscale calculations with ORCA, which is available on GitHub at https://github.com/iranimehdi/pdbtoORCA .

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Biologically detailed models of brain circuitry are challenging to build and simulate due to the large number of neurons, their complex interactions, and the many unknown physiological parameters. Simplified mathematical models are more tractable, but harder to evaluate when too far removed from neuroanatomy/physiology. We propose that a multiscale model, coarse-grained (CG) while preserving local biological details, offers the best balance between biological realism and computability. This paper presents such a model. Generally, CG models focus on the interaction between groups of neurons-here termed "pixels"-rather than individual cells. In our case, dynamics are alternately updated at intra- and interpixel scales, with one informing the other, until convergence to equilibrium is achieved on both scales. An innovation is how we exploit the underlying biology: Taking advantage of the similarity in local anatomical structures across large regions of the cortex, we model intrapixel dynamics as a single dynamical system driven by "external" inputs. These inputs vary with events external to the pixel, but their ranges can be estimated a priori. Precomputing and tabulating all potential local responses speed up the updating procedure significantly compared to direct multiscale simulation. We illustrate our methodology using a model of the primate visual cortex. Except for local neuron-to-neuron variability (necessarily lost in any CG approximation) our model reproduces various features of large-scale network models at a tiny fraction of the computational cost. These include neuronal responses as a consequence of their orientation selectivity, a primary function of visual neurons.

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A definitive understanding of the interplay between protein binding/migration and membrane curvature evolution is emerging but needs further study. The mechanisms defining such phenomena are critical to intracellular transport and trafficking of proteins. Among trafficking modalities, exosomes have drawn attention in cancer research as these nano-sized naturally occurring vehicles are implicated in intercellular communication in the tumor microenvironment, suppressing anti-tumor immunity and preparing the metastatic niche for progression. A significant question in the field is how the release and composition of tumor exosomes are regulated. In this perspective article, we explore how physical factors such as geometry and tissue mechanics regulate cell cortical tension to influence exosome production by co-opting the biophysics as well as the signaling dynamics of intracellular trafficking pathways and how these exosomes contribute to the suppression of anti-tumor immunity and promote metastasis. We describe a multiscale modeling approach whose impact goes beyond the fundamental investigation of specific cellular processes toward actual clinical translation. Exosomal mechanisms are critical to developing and approving liquid biopsy technologies, poised to transform future non-invasive, longitudinal profiling of evolving tumors and resistance to cancer therapies to bring us one step closer to the promise of personalized medicine.

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Data-driven modeling methods are studied for turbulent dynamical systems with extreme events under an unambiguous model framework. New neural network architectures are proposed to effectively learn the key dynamical mechanisms including the multiscale coupling and strong instability, and gain robust skill for long-time prediction resistive to the accumulated model errors from the data-driven approximation. The machine learning model overcomes the inherent limitations in traditional long short-time memory networks by exploiting a conditional Gaussian structure informed of the essential physical dynamics. The model performance is demonstrated under a prototype model from idealized geophysical flow and passive tracers, which exhibits analytical solutions with representative statistical features. Many attractive properties are found in the trained model in recovering the hidden dynamics using a limited dataset and sparse observation time, showing uniformly high skill with persistent numerical stability in predicting both the trajectory and statistical solutions among different statistical regimes away from the training regime. The model framework is promising to be applied to a wider class of turbulent systems with complex structures.

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In the Ross procedure, a patient's pulmonary valve is transplanted in the aortic position. Despite advantages of this surgery, reoperation is still needed in many cases due to excessive dilatation of the pulmonary autograft. To further understand the failure mechanisms, we propose a multiscale model predicting adaptive processes in the autograft at the cell and tissue scale. The cell-scale model consists of a network model, that includes important signaling pathways and relations between relevant transcription factors and their target genes. The resulting gene activity leads to changes in the mechanical properties of the tissue, modeled as a constrained mixture of collagen, elastin and smooth muscle. The multiscale model is calibrated with findings from experiments in which seven sheep underwent the Ross procedure. The model is then validated against a different set of sheep experiments, for which a qualitative agreement between model and experiment is found. Model outcomes at the cell scale, including the activity of genes and transcription factors, also match experimentally obtained transcriptomics data.

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Aside from the well-known role in protein synthesis, RNA can perform catalytic, regulatory, and other essential biological functions which are determined by its three-dimensional structure. In this regard, a great effort has been made during the past decade to develop computational tools for the prediction of the structure of RNAs from the knowledge of their sequence, incorporating experimental data to refine or guide the modeling process. Nevertheless, this task can become exceptionally challenging when dealing with long noncoding RNAs, constituted by more than 200 nucleotides, due to their large size and the specific interactions involved. In this chapter, we describe a multiscale approach to predict such structures, incorporating SAXS experimental data into a hierarchical procedure which couples two coarse-grained representations: Ernwin, a helix-based approach, which deals with the global arrangement of secondary structure elements, and SPQR, a nucleotide-centered coarse-grained model, which corrects and refines the structures predicted at the coarser level.We describe the methodology through its application on the Braveheart long noncoding RNA, starting from the SAXS and secondary structure data to propose a refined, all-atom structure.

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Naturally occurring coatings on aluminum metal, such as its oxide or hydroxide, serve to protect the material from corrosion. Understanding the conditions under which these coatings mechanically fail is therefore expected to be an important aspect of predictive models for aluminum component lifetimes. To this end, we develop and apply a molecular dynamics (MD) modeling framework for conducting tension tests that is capable of isolating factors governing the mechanical strength as a function of coating chemistry, defect morphology, and variables associated with the loading path. We consider two representative materials, including γ-Al2O3 and γ-Al(OH)3 (i.e., oxide and hydroxide), both of which form readily as aluminum surface coatings. Our results indicate that defects have a significant bearing on the strength of aluminum oxide, with grain boundaries serving to reduce the strain at failure from εzz = 0.300 to 0.219, relative to perfect single crystal. Our simulations also predict that porosity lowers the elastic stiffness and yield strength of the oxide. Relative to perfect crystal, we find porosity factors of 5%, 10% and 20% decrease the yield stress by 26%, 36% and 53%, respectively. MD predicts that perfect hydroxide and oxide single crystal have respective strains at failure of 0.08 and 0.31 under tensile uniaxial strain loading, and that the corresponding yield stresses are respectively 1.6 and 11.1 GPa. These data indicate that the hydroxide is substantially more susceptible to mechanical failure than the oxide. Our results, coupled with literature findings that indicate hot and humid conditions favor formation of hydroxide and defective oxide coatings, indicate the potential for a complicated dependence of aluminum corrosion susceptibility and stress corrosion cracking on aging history.

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This study aims to characterize graphene epoxy nanocomposite properties using multiscale modeling. Molecular dynamics was used to study the nanocomposite at the nanoscale and finite element analysis at the macroscale to complete the multiscale modeling. The coupling of these two scales was carried out using the Irving-Kirkwood averaging method. First, the functionalization of graphene was carried and 6% grafted graphene was selected based on Young's modulus and the tensile strength of the grafted graphene sheet. Functionalized graphene with weight fractions of 1.8, 3.7, and 5.6 wt.% were reinforced with epoxy polymer to form a graphene epoxy nanocomposite. The results showed that the graphene with 3.7 wt.% achieved the highest modulus. Subsequently, a functionalized graphene sheet with an epoxy matrix was developed to obtain the interphase properties using the MD modeling technique. The normal and shear forces at the interphase region of the graphene epoxy nanocomposite were investigated using a traction-separation test to analyze the mechanical properties including Young's modulus and traction forces. The mean stiffness of numerically tested samples with 1.8, 3.7, and 5.6 wt.% graphene and the stiffness obtained from experimental results from the literature were compared. The experimental results are lower than the multiscale model results because the experiments cannot replicate the molecular-scale behavior. However, a similar trend could be observed for the addition of up to 3.7 wt.% graphene. This demonstrated that the graphene with 3.7 wt.% shows improved interphase properties. The macroscale properties of the graphene epoxy nanocomposite models with 1.8 and 3.7 wt.% were comparatively higher.