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In this paper, we propose a novel temporally and spatially segregated numerical scheme to discretize the coupled electromechanical model of myocardium. We perform several numerical experiments with activation of a myocardial slab with structural inhomogeneity and evaluate the dependence of numerical errors on the size of spatial and temporal discretization steps. In our study, we show that the spatial step for the mechanical equations hm⩽2.5 mm yields reasonable results with noticeable errors only in the region of myocardial inhomogeneity. We also show that time step τm⩽1 ms can be used for temporal discretization of mechanical equations, and the propagation velocity of the activation and contraction fronts differs from the reference one by no more than 1.3%for such time step. Finally, we show that the increase of time discretization steps of the mechanical equations τm and the monodomain equation τe leads to phase errors with opposite signs, and we can compensate these errors by tuning the relationship between the time steps. [ABSTRACT FROM AUTHOR]

In the realm of practical engineering, engineers commonly employ rod system models for modeling and analysis, which consequently precludes them from calculating the nominal forces exerted on welds at intricate nodes. This paper addresses the design challenges of the innovative bracket-crane truss structure by proposing a simplified nominal force calculation formula for critical welds of the integrated node. This study commences with the establishment of the frame model and ABAQUS multiscale models, utilizing engineering project drawings and data, followed by a verification of the similarities between the two simulation methods. This similarity of outcomes provides a foundation for directly using the computational results from the frame model in future calculations of the forces at the weld locations. From a mechanical standpoint, this paper derives nominal force calculation formulas for horizontal and vertical welds at critical locations for three node types. Additionally, a formula for calculating nominal shear forces in vertical welds at the end plate of support nodes is introduced. The applicability of these derived formulas is subsequently validated, ensuring their efficacy in accurately capturing relevant forces at critical locations. The presented nominal force calculation formula serves as a valuable tool for optimizing the design and guaranteeing the structural integrity of the integrated node within this distinctive engineering context. [ABSTRACT FROM AUTHOR]

Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models. [ABSTRACT FROM AUTHOR]

We present a methodology for systematically extending epidemic models to multilevel and multiscale spatio-temporal pandemic ones. Our approach builds on the use of coloured stochastic and continuous Petri nets facilitating the sound component-based extension of basic SIR models to include population stratification and also spatio-geographic information and travel connections, represented as graphs, resulting in robust stratified pandemic metapopulation models. The epidemic components and the spatial and stratification data are combined together in these coloured models and built in to the underlying expanded models. As a consequence this method is inherently easy to use, producing scalable and reusable models with a high degree of clarity and accessibility which can be read either in a deterministic or stochastic paradigm. Our method is supported by a publicly available platform PetriNuts; it enables the visual construction and editing of models; deterministic, stochastic and hybrid simulation as well as structural and behavioural analysis. All models are available as Supplementary Material, ensuring reproducibility. All uncoloured Petri nets can be animated within a web browser at https:// www-dssz.informatik.tu-cottbus.de/DSSZ/Research/ModellingEpidemics, assisting the comprehension of those models. We aim to enable modellers and planners to construct clear and robust models by themselves. [ABSTRACT FROM AUTHOR]

In the realm of practical engineering, engineers commonly employ rod system models for modeling and analysis, which consequently precludes them from calculating the nominal forces exerted on welds at intricate nodes. This paper addresses the design challenges of the innovative bracket-crane truss structure by proposing a simplified nominal force calculation formula for critical welds of the integrated node. This study commences with the establishment of the frame model and ABAQUS multiscale models, utilizing engineering project drawings and data, followed by a verification of the similarities between the two simulation methods. This similarity of outcomes provides a foundation for directly using the computational results from the frame model in future calculations of the forces at the weld locations. From a mechanical standpoint, this paper derives nominal force calculation formulas for horizontal and vertical welds at critical locations for three node types. Additionally, a formula for calculating nominal shear forces in vertical welds at the end plate of support nodes is introduced. The applicability of these derived formulas is subsequently validated, ensuring their efficacy in accurately capturing relevant forces at critical locations. The presented nominal force calculation formula serves as a valuable tool for optimizing the design and guaranteeing the structural integrity of the integrated node within this distinctive engineering context.

Over 90% of breast cancer is cured; yet there remain highly aggressive breast cancers that develop rapidly and are extremely difficult to treat, much less prevent. Breast cancers that rapidly develop between breast image screening are called "interval cancers." The efforts of our team focus on identifying multiscale integrated strategies to identify biologically aggressive precancerous breast lesions. Our goal is to identify spatiotemporal changes that occur prior to development of interval breast cancers. To accomplish this requires integration of new technology. Our team has the ability to perform single cell in situ transcriptional profiling, noncontrast biological imaging, mathematical analysis, and nanoscale evaluation of receptor organization and signaling. These technological innovations allow us to start to identify multidimensional spatial and temporal relationships that drive the transition from biologically aggressive precancer to biologically aggressive interval breast cancer. This article is categorized under: Cancer > Computational Models Cancer > Molecular and Cellular Physiology Cancer > Genetics/Genomics/Epigenetics.

Plasmonic metal nanoparticles (PMNPs) are capable of localized surface plasmon resonance (LSPR) and have become an important component in many experimental settings, such as the surface‐enhanced spectroscopy and plasmonic photocatalysts, in which PMNPs are used to regulate the nearby molecular photophysical and photochemical behaviors by means of the complex interplay between the plasmon and molecular quantum transitions. Building computational models of these coupled plasmon‐molecule systems can help us better understand the bound molecular properties and reactivity, and make better decisions to design and control such systems. Ab initio modeling the nanosystem remains highly challenging. Many hybrid quantum‐classical (or ‐quantum) computing models have thus been developed to model the coupled systems, in which the molecular system of interest is designated as the quantum mechanical (QM) sub‐region and treated by the excited‐state electronic structure approaches such as the time‐dependent density functional theory (TDDFT), while the electromagnetic response of PMNPs is usually described using either a computational/classical electrodynamic (CED) model, polarizable continuum model(PCM), a polarizable molecular mechanics (MM) force field, or a collective of optical oscillators in QED model, leading to many hybrid approaches, such as QM/CED, QM/PCM, QM/MM or ab initio QED. In this review, we summarize recent advances in the development of these hybrid models as well as their advantages and limitations, with a specific emphasis on the TDDFT‐based approaches. Some numerical simulations on the plasmon‐enhanced absorption and Raman spectroscopy, plasmon‐driven water splitting reaction and interfacial electronic injection dynamics in dye‐sensitized solar cell are demonstrated. This article is categorized under:Electronic Structure Theory > Ab Initio Electronic Structure MethodsTheoretical and Physical Chemistry > SpectroscopySoftware > Quantum ChemistryElectronic Structure Theory > Combined QM/MM Methods [ABSTRACT FROM AUTHOR]

The rapid response of photosynthetic organisms to fluctuations in ambient light intensity is incompletely understood at both the molecular and membrane levels. In this review, we describe research from our group over a 10-year period aimed at identifying the photophysical mechanisms used by plants, algae and mosses to control the efficiency of light harvesting by photosystem II on the seconds-to-minutes time scale. To complement the spectroscopic data, we describe three models capable of describing the measured response at a quantitative level. The review attempts to provide an integrated view that has emerged from our work, and briefly looks forward to future experimental and modelling efforts that will refine and expand our understanding of a process that significantly influences crop yields.

Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system's individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa. [ABSTRACT FROM AUTHOR]

Deep-frying is usually considered a single-step process involving immersion in a high boiling point liquid, commonly vegetable oil. This study addresses the second step when French fries are removed from the oil bath, When the potato strip crosses the oil-air interface, it lifts oil up with two possible fates, penetrating the crust or flowing along the surface or dripping at the bottom ends. The entire process was imaged at high acquisition rates (> 100 Hz) and quantified by capturing each oil droplet. Oil imbibition and drainage were decoupled by comparing the results for real French fries and impervious metallic geometries. The effects of geometries and shapes were studied and used to validate a generic oil coating-dripping model coupled with our multiscale model of oil imbibition at the tissue scale (AIChE J·, 61: 2329-2353, 2015). Oil uptake appears as a non-monotonic function of time. Oil is lost from the surface as 3 and 5 drops within the first seconds. Residual heat transfer and vaporization in regions fully covered by oil generate a steam film capable of destabilizing droplets. Oil thermal contraction during cooling creates an additional suction force. Numerical simulations and comparing the behaviors between pervious and impervious French fries show that oil uptake could be cut by half by improving dripping and preventing cooling and steam condensation for a short period. The main factors affecting the dripping kinetics and the possibility to deoil already impregnated products are discussed. [ABSTRACT FROM AUTHOR]

Geopolymer concrete is an eco-efficient and environmentally friendly construction material. Various ashes were used as the binder in geopolymer concrete, such as fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin ash, and Palm oil fuel ash. Fly ash was commonly consumed to prepare geopolymer concrete composites. It is essential to have 28 days resting period of the concrete to attain compressive strength in the structural design. In the present investigation, several soft computing models were employed to form the predictive models for forecasting the compressive strength of ground granulated blast furnace slag (GGBFS) concrete. A complete dataset of 268 samples was extracted from published research articles and analyzed to establish models. The modeling process incorporated seven effective parameters such as water content (W), temperature (T), water-to-binder ratio (w/b), ground granulated blast furnace slag-to-binder ratio (GGBFS/b), fine aggregate (FA) content, coarse aggregate (CA) content, and the superplasticizer dosage (SP) that were examined and measured on the compressive strength of GGBFS concrete by utilizing various modeling techniques, viz., Linear Regression (LR), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Support Vector Regression (SVR), Grey Wolf Optimization (GWO), Differential Evolution (DE), and Mantra Rays Foraging Optimization (MRFO). The compressive strength of the training datasets was predicted using the SVR-PSO and SVR-GWO models, with a reliable coefficient of correlation of 0.9765 and 0.9522, respectively. [ABSTRACT FROM AUTHOR]

Recently the understanding of complex biological systems has been increased considering the important interplay among different scholars coming from different applied sciences such as mathematics, physics and information sciences. As known, the modeling of a complex system requires the analysis of the different interactions occurring among the different components of the system. Moreover, the analysis of a complex system can be performed at different scales; usually the microscopic, the mesoscopic and the macroscopic scales are the most representation scales. However, a multiscale approach is required. A unified approach that takes into account the different phenomena occurring at each observation scale is the desire of this century. This editorial article deals with the topic of this special issue, which is devoted to the new developments in the multiscale modeling of complex biological systems with special attention to the interplay between different scholars.

Healthy lung function depends on a complex systemof interactionswhich regulate the mechanical and biochemical environment of individual cells to the whole organ. Perturbations from these regulated processes give rise to significant lung dysfunction such as chronic inflammation, airway hyperresponsiveness and airway remodelling characteristic of asthma. Importantly, there is ongoing mechanobiological feedback where mechanical factors including airway stiffness and oscillatory loading have considerable influence over cell behavior. The recently proposed area of mechanopharmacology recognises these interactions and aims to highlight the need to consider mechanobiology when identifying and assessing pharmacological targets. However, these multiscale interactions can be difficult to study experimentally due to the need for measurements across a wide range of spatial and temporal scales. On the other hand, integrative multiscale mathematical models have begun to show success in simulating the interactions between different mechanobiological mechanisms or cell/tissue-types across multiple scales. When appropriately informed by experimental data, these models have the potential to serve as extremely useful predictive tools, where physical mechanisms and emergent behaviours can be probed or hypothesised and, more importantly, exploited to propose new mechanopharmacological therapies for asthma and other respiratory diseases. In this review, we first demonstrate via an exemplar, howa multiscale mathematical model of acute bronchoconstriction in an airway could be exploited to propose new mechanopharmacological therapies. We then review current mathematical modelling approaches in respiratory disease and highlight hypotheses generated by such models that could have significant implications for therapies in asthma, but that have not yet been the subject of experimental attention or investigation. Finally we highlight modelling approaches that have shown promise in other biological systems that could be brought to bear in developing mathematical models for optimisation of mechanopharmacological therapies in asthma, with discussion of how they could complement and accelerate current experimental approaches. [ABSTRACT FROM AUTHOR]

What role does phenotypic complexity play in the systems-level function of an embodied agent? The organismal phenotype is a topologically complex structure that interacts with a genotype, developmental physics, and an informational environment. Using this observation as inspiration, we utilize a type of embodied agent that exhibits layered representational capacity: meta-brain models. Meta-brains are used to demonstrate how phenotypes process information and exhibit self-regulation from development to maturity. We focus on two candidate structures that potentially explain this capacity: folding and layering. As layering and folding can be observed in a host of biological contexts, they form the basis for our representational investigations. First, an innate starting point (genomic encoding) is described. The generative output of this encoding is a differentiation tree, which results in a layered phenotypic representation. Then we specify a formal meta-brain model of the gut, which exhibits folding and layering in development in addition to different degrees of representation of processed information. This organ topology is retained in maturity, with the potential for additional folding and representational drift in response to inflammation. Next, we consider topological remapping using the developmental Braitenberg Vehicle (dBV) as a toy model. During topological remapping, it is shown that folding of a layered neural network can introduce a number of distortions to the original model, some with functional implications. The paper concludes with a discussion on how the meta-brains method can assist us in the investigation of enactivism, holism, and cognitive processing in the context of biological simulation. [ABSTRACT FROM AUTHOR]

Precast concrete segments reinforced with a combination of steel fibers and conventional rebar (RC-SFRC) have gained prominence in the mechanized construction of tunnels in recent years due to the advantages offered by this hybrid reinforcement solution in enhancing the mechanical behavior of these structural members. This research aims to understand better the flexural behavior of RC-SFRC tunnel segments proposed to replace conventional reinforcement in São Paulo Metro Line 5. An experimental program and numerical analyses using a multiscale model were conducted to predict the post-cracking parameters of SFRC through three-point bending tests according to EN14651 and assess the flexural behavior of full-scale tunnel segments. In both scenarios, 2D multiscale numerical models with discrete and explicit representations of the reinforcements were applied. The comparison of numerical and experimental results in terms of crack width, mean crack spacing, deflection, and the ultimate and service loads derived from design predictions demonstrate that the proposed RC-SFRC reinforcement represents an advantageous alternative, significantly enhancing the performance of the segments for both serviceability and ultimate limit states. Furthermore, the responses show that the adopted numerical strategy is promising and can be consolidated as a tool for optimizing the design process. [Display omitted] • A comprehensive study of the flexural behavior of RC-SFRC is performed. • A multiscale model is employed to predict the SFRC post-cracking parameters. • Assessment of the design load of the RC-SFRC precast segment is carried out. • A multiscale model is employed to predict the flexural behavior of full-scale tunnel segments. • RC-SFRC tunnel segments presented an enhanced performance for SLS and ULS. [ABSTRACT FROM AUTHOR]

We present a methodology for systematically extending epidemic models to multilevel and multiscale spatio-temporal pandemic ones. Our approach builds on the use of coloured stochastic and continuous Petri nets facilitating the sound component-based extension of basic SIR models to include population stratification and also spatio-geographic information and travel connections, represented as graphs, resulting in robust stratified pandemic metapopulation models. The epidemic components and the spatial and stratification data are combined together in these coloured models and built in to the underlying expanded models. As a consequence this method is inherently easy to use, producing scalable and reusable models with a high degree of clarity and accessibility which can be read either in a deterministic or stochastic paradigm. Our method is supported by a publicly available platform PetriNuts; it enables the visual construction and editing of models; deterministic, stochastic and hybrid simulation as well as structural and behavioural analysis. All models are available as Supplementary Material, ensuring reproducibility. All uncoloured Petri nets can be animated within a web browser at https://www-dssz.informatik.tu-cottbus.de/DSSZ/Research/ModellingEpidemics, assisting the comprehension of those models. We aim to enable modellers and planners to construct clear and robust models by themselves. [ABSTRACT FROM AUTHOR]

Despite its severe operating conditions, associated energy consumption, and environmental concerns, the manufacture of nitrogen‐rich fertilizers still relies heavily on producing ammonia in centralized chemical plants via the Haber–Bosch process. A distributed and more sustainable scheme considers the on‐site production of carbon‐neutral fertilizers at ambient conditions in photocatalytic reactors powered by sunlight. Among the different strategies proposed to boost the nitrogen reduction ability of conventional catalysts, the incorporation of plasmonic nanomaterials is gaining widespread interest owing to their unique optical tunability and their potential to improve the efficiency and selectivity of many chemical transformations. This Perspective examines the state‐of‐the‐art for the nitrogen reduction reaction via plasmon‐driven photocatalysis and discusses design principles for advancing it. The different physical mechanisms underlying the operation of plasmonic materials in a catalytic setting, and the dimensions along which the catalysts can be tuned to harness them are detailed. Paths to overcome current frontiers in the field, including design strategies of plasmonic photocatalysts, the development of complementary characterization techniques, the standardization of the reaction conditions and ammonia quantification methods, and the possibilities offered by theoretical methods to drive material discovery, identifying fundamental bottlenecks, and proposing directions for the advancement of this emerging field are outlined. [ABSTRACT FROM AUTHOR]

Most cardiac diseases affect the left ventricle (LV), with its treatment being the focus of the majority of cardiovascular medical therapies and mechanical support. Many diseases also affect the right ventricle (RV) and pulmonary circulation, either directly or from secondary effects on the LV and systemic circulation. The development of therapies to treat RV failure is critical for the management of many patients with advanced heart and lung disease, but models to develop mechanical therapies to target the RV are currently lacking. While the RV and pulmonary vasculature have many similarities to the LV and systemic vasculature, there are notable differences between them at a morphological and functional level. This results in distinct patterns of coupling between the RV and pulmonary circulation. Models such as the Windkessel use lumped parameters that characterize the mechanical properties of the entire RV and pulmonary circulation. Contemporary computational approaches that have been used to model the pulmonary vasculature rely on patient-derived anatomy for the proximal pulmonary arteries and other approaches, such as fractal-based models for the distal vasculature. At this time, these models are still being optimized and significantly more work will be required to fully implement them and test their utility across multiple patient-derived anatomies. The development of these models for the RV and pulmonary circulation will be critical to address the unmet need of mechanical therapies for disease of the RV and pulmonary circulation.

Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system’s individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa.

The mechanical balance equations for a body with microstructure are derived from an expansion of the general Noll's axiom of frame-indifference that takes into account the behavior of measures of microstructural interactions. Next, we introduce perfect internal constraints and adopt an extended determinism principle to analyze the consequences of their presence. Finally, we define the class of continua with partially constrained microstructure to give a complete dynamical description for a broad family of peculiar materials such as suspensions of rigid rotating granules, pseudo-Cosserat continua and partially constrained micro-spins. [ABSTRACT FROM AUTHOR]

Recently the understanding of complex biological systems has been increased considering the important interplay among different scholars coming from different applied sciences such as mathematics, physics and information sciences. As known, the modeling of a complex system requires the analysis of the different interactions occurring among the different components of the system. Moreover, the analysis of a complex system can be performed at different scales; usually the microscopic, the mesoscopic and the macroscopic scales are the most representation scales. However, a multiscale approach is required. A unified approach that takes into account the different phenomena occurring at each observation scale is the desire of this century. This editorial article deals with the topic of this special issue, which is devoted to the new developments in the multiscale modeling of complex biological systems with special attention to the interplay between different scholars. [ABSTRACT FROM AUTHOR]

The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.

Updating Earth models used by the scientific community in geologic studies and hazard assessment has a significant societal impact but is computationally prohibitive due to the large spatial scale. The advent of urban seismology allowed rapid development of local high‐resolution models using short‐term dense seismic arrays to become conventional. To incorporate the details in these local models in community models, we developed a technique for constructing window taper functions like the cosine taper in arbitrarily shaped spatial domains on regular grids. We apply our algorithm to the problem of low‐frequency ground shaking estimation near the southernmost San Andreas fault by creating two hybrid models. These models consist of basin‐scale (top 10 km or less) high‐resolution models developed using controlled source data embedded into two popular Southern California Earthquake Center community models. We evaluate the models by computing long period (6–30 s) wavefield energy misfits using 11 earthquakes with moment magnitudes between 3.5 and 5.5 not used in developing any of the models under consideration. One of the hybrid models produces an ∼24% decrease while the other has an ∼0.6% increase in the overall median misfit relative to their original community models. The overlapping misfit values between the models and variability in waveform fit for different events and stations emphasize the difficulties in model validation. Our approach can merge any type of gridded multiscale and multidimensional datasets, and represents a valuable tool for modeling in the computational sciences. Plain Language Summary: Earth models are helpful to society and play an essential role in exploring for natural resources, geologic hazard assessments, and understanding how our planet works. The models are developed in various scales ranging from the entire Earth to a metropolitan area. It tends to be the case that the bigger models are more expensive to create, especially with cutting‐edge methods. We experiment with a simple technique for updating big models by replacing the most critical parts with smaller, more accurate models. When we test our new tool for earthquake ground motion prediction, we achieve some results that show that it can be useful. Key Points: We develop an algorithm for merging gridded multiscale and multidimensional datasets and smoothly embed basin models in two regional modelsOne hybrid model produces an ∼24% decrease and the other has an ∼0.6% increase in median waveform misfit relative to their regional modelsMisfit overlap and variability with stations and events between models show the complexities of model validation [ABSTRACT FROM AUTHOR]

Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silico offers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.

A new class of multiscale scheme is presented for micro‐hydrodynamic problems based on a dual representation of the fluid observables. The hybrid model is first tested against the classical flow between two parallel plates and then applied to a plug flow within a micrometer‐sized striction and a shear flow within a microcavity. Both cases demonstrate the capability of the multiscale approach to reproduce the correct macroscopic hydrodynamics also in the presence of refined grids (one and two levels), while retaining the correct thermal fluctuations, embedded in the multiparticle collision method. This provides the first step toward a novel class of fully mesoscale hybrid approaches able to capture the physics of fluids at the micro‐ and nanoscales whenever a continuum representation of the fluid falls short of providing the complete physical information, due to a lack of resolution and thermal fluctuations. [ABSTRACT FROM AUTHOR]

A multiscale approach to model fiber‐reinforced composites, those that are characterized by an isotropic orientation of fibers, is presented. To this end, a bottom‐up approach is used to formulate a hierarchical model. The primary basis for the mesoscopic description revolves around the assumption that the composite network consists of fibers resting on foundations of the native material matrix. Molecular dynamics (MD) simulations of such fibers on foundations are performed, and crucial material parameters, such as the stiffness of the particle matrix and Young's modulus of the fibers are evaluated. Subsequently, a micro‐mechanical constitutive model is formulated, wherein fiber‐reinforced composites are characterized by a homogeneous distribution and an isotropic orientation of fibers. The fibers are modeled as beams undergoing bending and stretching while resting on Winkler‐type of elastic foundations. The 3D macroscopic network behavior is finally presented. As an example, the particle matrix used is a silica aerogel and the fibers are modeled as double‐walled carbon nanotubes. In the proposed modeling approach, MD simulations are shown to provide a physical estimation of the micro‐mechanical model parameters. [ABSTRACT FROM AUTHOR]

We develop a sequence based alpha-carbon model to incorporate a mean field estimate of the orientation dependence of the polypeptide chain that gives rise to specific hydrogen bond pairing to stabilize alpha-helices and beta-sheets. We illustrate the success of the new protein model in capturing thermodynamic measures and folding mechanism of proteins L and G. Compared to our previous coarse-grained model, the new model shows greater folding cooperativity and improvements in designability of protein sequences, as well as predicting correct trends for kinetic rates and mechanism for proteins L and G. We believe the model is broadly applicable to other protein folding and protein-protein co-assembly processes, and does not require experimental input beyond the topology description of the native state. Even without tertiary topology information, it can also serve as a mid-resolution protein model for more exhaustive conformational search strategies that can bridge back down to atomic descriptions of the polypeptide chain.

We develop a sequence based alpha-carbon model to incorporate a mean field estimate of the orientation dependence of the polypeptide chain that gives rise to specific hydrogen bond pairing to stabilize alpha-helices and beta-sheets. We illustrate the success of the new protein model in capturing thermodynamic measures and folding mechanism of proteins L and G. Compared to our previous coarse-grained model, the new model shows greater folding cooperativity and improvements in designability of protein sequences, as well as predicting correct trends for kinetic rates and mechanism for proteins L and G. We believe the model is broadly applicable to other protein folding and protein-protein co-assembly processes, and does not require experimental input beyond the topology description of the native state. Even without tertiary topology information, it can also serve as a mid-resolution protein model for more exhaustive conformational search strategies that can bridge back down to atomic descriptions of the polypeptide chain.

The rapid response of photosynthetic organisms to fluctuations in ambient light intensity is incompletely understood at both the molecular and membrane levels. In this review, we describe research from our group over a 10-year period aimed at identifying the photophysical mechanisms used by plants, algae and mosses to control the efficiency of light harvesting by photosystem II on the seconds-to-minutes time scale. To complement the spectroscopic data, we describe three models capable of describing the measured response at a quantitative level. The review attempts to provide an integrated view that has emerged from our work, and briefly looks forward to future experimental and modelling efforts that will refine and expand our understanding of a process that significantly influences crop yields.

In this study, we propose a new two‐scale fatigue model based on continuum damage mechanics. A representative volume element (RVE) consisting of microinclusions and a matrix is constructed. Further, damage‐coupled constitutive equations are derived. The degradation in the mechanical properties of the RVE is determined by the damaged inclusions and matrix using the Mori‐Tanaka scheme. A numerical calculation of the fatigue lives of notched specimens is executed. This new model predicts high‐cycle fatigue (HCF) life more effectively, considering the two‐segment characteristic of S‐N curves of smooth specimens. This study provides novel insights into the evolution mechanism of HCF damage. [ABSTRACT FROM AUTHOR]

Summary: Bayesian hierarchical models are used to share information between related samples and to obtain more accurate estimates of sample level parameters, common structure and variation between samples. When the parameter of interest is the distribution or density of a continuous variable, a hierarchical model for continuous distributions is required. Various such models have been described in the literature using extensions of the Dirichlet process and related processes, typically as a distribution on the parameters of a mixing kernel. We propose a new hierarchical model based on the Pólya tree, which enables direct modelling of densities and enjoys some computational advantages over the Dirichlet process. The Pólya tree also enables more flexible modelling of the variation between samples, providing more informed shrinkage and permitting posterior inference on the dispersion function, which quantifies the variation between sample densities. We also show how the model can be extended to cluster samples in situations where the observed samples are believed to have been drawn from several latent populations. [ABSTRACT FROM AUTHOR]

Mathematical models that are based on differential equations require detailed knowledge about the parameters that are included in the equations. Some of the parameters can be measured experimentally while others need to be estimated. When the models become more sophisticated, such as in the case of multiscale models of hepatitis C virus dynamics that deal with partial differential equations (PDEs), several strategies can be tried. It is possible to use parameter estimation on an analytical approximation of the solution to the multiscale model equations, namely the long-term approximation, but this limits the scope of the parameter estimation method used and a long-term approximation needs to be derived for each model. It is possible to transform the PDE multiscale model to a system of ODEs, but this has an effect on the model parameters themselves and the transformation can become problematic for some models. Finally, it is possible to use numerical solutions for the multiscale model and then use canned methods for the parameter estimation, but the latter is making the user dependent on a black box without having full control over the method. The strategy developed here is to start by working directly on the multiscale model equations for preparing them toward the parameter estimation method that is fully coded and controlled by the user. It can also be adapted to multiscale models of other viruses. The new method is described, and illustrations are provided using a user-friendly simulator that incorporates the method. [ABSTRACT FROM AUTHOR]

Composite materials fail under extreme working conditions, particularly at high temperature, due to delamination (separation of fibers from matrix). And therefore it is needed to switch over functionally graded materials (FGMs) which can sustain at high temperature conditions (250–2000°C). There is a need to analyze the fracture and fatigue characteristics of FGM structures and so through this review the emphasis is given on fracture analysis of FGM materials. It has been reported that a combination of extended finite element method and isogeometric analysis methodologies has been used for general mixed-mode crack propagation problems after the introduction of extended isogeometric analysis. Furthermore, recent computational advances have been in the form of multiscale simulations where the part of model is simulated by a finer modeling scale, which can represent details of the material behavior and the interacting effects of material constituents in the finest way. The review is also focused on new advances in analytical and numerical methods for the stress, vibration, and buckling analyses of FGMs. Emphasis has been primarily on to restrict 2D analysis with sorts of compromise in the accuracy of results. First shear deformation theory (FSDT) and third-order shear deformation theory have been extensively used among the various 2D plate theories. FSDT can help us in terms of getting reasonably accurate results with less computational afford. This paper also outlines review on carbon nanotubes (CNT) reinforced FGMs, functionally graded nanocomposites, functionally graded single-walled CNT, FG nanobeam as well as functionally graded piezoelectric materials. Future applications would be based on these smart materials which are supposed to serve us in adverse conditions. Of course, with rise and advent of promising nanotechnology and its potential impact on aerospace industry as well as on other areas, it becomes important to us to compile this review article. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the simulation of blood ow in microvascular networks and the surrounding tissue. To reduce the computational complexity of this issue, the network structures are modeled by a one-dimensional graph, whose location in space is determined by the centerlines of the three-dimensional vessels. The surrounding tissue is considered as a homogeneous porous medium. Darcy's equation is used to simulate ow in the extra-vascular space, where the mass exchange with the blood vessels is accounted for by means of line source terms. However, this model reduction approach still causes high computational costs, particularly when larger parts of an organ have to be simulated. This observation motivates the consideration of a further model reduction step. Thereby, we homogenize the fine scale structures of the microvascular networks resulting in a new hybrid approach modeling the fine scale structures as a heterogeneous porous medium and the ow in the larger vessels by one-dimensional ow equations. Both modeling approaches are compared with respect to mass uxes and averaged pressures. The simulations have been performed on a microvascular network that has been extracted from the cortex of a rat brain. [ABSTRACT FROM AUTHOR]

The primary plant cell wall is a dynamically regulated composite material of multiple biopolymers that forms a scaffold enclosing the plant cells. The mechanochemical make-up of this polymer network regulates growth, morphogenesis, and stability at the cell and tissue scales. To understand the dynamics of cell wall mechanics, and how it correlates with cellular activities, several experimental frameworks have been deployed in recent years to quantify the mechanical properties of plant cells and tissues. Here we critically review the application of biomechanical tool sets pertinent to plant cell mechanics and outline some of their findings, relevance, and limitations. We also discuss methods that are less explored but hold great potential for the field, including multiscale in silico mechanical modeling that will enable a unified understanding of the mechanical behavior across the scales. Our overview reveals significant differences between the results of different mechanical testing techniques on plant material. Specifically, indentation techniques seem to consistently report lower values compared with tensile tests. Such differences may in part be due to inherent differences among the technical approaches and consequently the wall properties that they measure, and partly due to differences between experimental conditions. [ABSTRACT FROM AUTHOR]

With advances in numerical modeling techniques, the combined finite-discrete-element-method (FDEM) is increasingly being used to study the mechanical behavior and failure processes of brittle geomaterials under various loading conditions. The progressive rock fracturing process from initial formation to subsequent propagation can be simulated explicitly in FDEM models where the corresponding mechanical response in the simulations is governed by a set of microparameters. Previous studies have calibrated these microparameters by comparing the macroscopic results of simulated laboratory tests with those obtained in physical tests or by comparing larger-scale model results to observed field performance of engineered structures. Very few studies, however, have calibrated models at both the laboratory and field scales, and there is, therefore, a lack of understanding of the scale-dependency of FDEM material parameter inputs. To explore this scale issue, this study presents a series of calibrated laboratory-scale models of Creighton granite. Next, models of 8 m-wide pillars with different width-to-height ratios are calibrated against the strength trends predicted by empirical relationships. In the context of the pillar models developed, the effects of each input parameter on the macroscopic pillar stress-strain behavior are presented, considering the yield stress, peak strength, and post-yield behaviors. Ultimately, it is found that while the same set of input parameters can be used to reproduce both the expected laboratory specimen behavior and the expected pillar peak strengths at different width-to-height ratios, the post-yield behavior of the pillar models obtained using the laboratory parameters is much more brittle than would be expected in reality. [ABSTRACT FROM AUTHOR]

Parameter estimation in mathematical models that are based on differential equations is known to be of fundamental importance. For sophisticated models such as age-structured models that simulate biological agents, parameter estimation that addresses all cases of data points available presents a formidable challenge and efficiency considerations need to be employed in order for the method to become practical. In the case of age-structured models of viral hepatitis dynamics under antiviral treatment that deal with partial differential equations, a fully numerical parameter estimation method was developed that does not require an analytical approximation of the solution to the multiscale model equations, avoiding the necessity to derive the long-term approximation for each model. However, the method is considerably slow because of precision problems in estimating derivatives with respect to the parameters near their boundary values, making it almost impractical for general use. In order to overcome this limitation, two steps have been taken that significantly reduce the running time by orders of magnitude and thereby lead to a practical method. First, constrained optimization is used, letting the user add constraints relating to the boundary values of each parameter before the method is executed. Second, optimization is performed by derivative-free methods, eliminating the need to evaluate expensive numerical derivative approximations. The newly efficient methods that were developed as a result of the above approach are described for hepatitis C virus kinetic models during antiviral therapy. Illustrations are provided using a user-friendly simulator that incorporates the efficient methods for both the ordinary and partial differential equation models.

Many single-stranded RNA viruses, including major viral pathogens, present RNA-encoded virus assembly instructions (VAIs) within their genetic message that can be isolated from the genetic code and repurposed for the design of virus-like particles. These VAIs rely on multiple dispersed RNA secondary structure elements with a consensus recognition motif for the capsid (core) protein, called packaging signals (PSs), which collectively promote capsid assembly. In this talk, I will provide evidence for the evolutionary conservation of the PS-encoded assembly instructions among different viruses in a viral family and discuss the implications of this discovery for viral evolution. I will then demonstrate how the VAIs can be exploited for therapy. In particular, defective interfering particles occur spontaneously in viral evolution as mutant strains lacking essential parts of the viral genome. Their ability to replicate in the presence of wild-type virus at the expense of virally produced resources can be mimicked by therapeutic interfering particles (TIPs). I will introduce a novel approach to the design of such TIPs based on synthetic nucleic acid sequences containing the VAIs but otherwise lacking genetic information. Using multiscale models of a viral infection, I will demonstrate the potential of these particles in both the prophylaxis and treatment of RNA viral infections.

Abstract Many multiscale finite element formulations can become computationally expensive because they rely on detailed models of the element's internal displacement field. This issue is exacerbated in the presence of nonlinear problems, where numerical iterations are generally needed. We propose a method that utilizes machine learning to generate a direct relationship between the element state and its forces, which avoids the complex task of finding the internal displacement field and eliminates the need for numerical iterations. To generate our model, we choose an existing finite element formulation, extract data from an instance of that element, and feed that data to the machine learning algorithm. The result is an approximated model of the element that can be used in the same context. Unlike most data-driven techniques applied to individual elements, our method is not tied to any particular machine learning algorithm, and it does not impose any restriction on the solver of choice. In addition, we guarantee that our elements are physically accurate by enforcing frame indifference and conservation of linear and angular momentum. Our results indicate that this can considerably reduce the error of the method and the computational cost of producing and solving the model. Highlights • We use machine learning to find the forces corresponding to the element's state. • The method avoids the complex task of finding the internal displacement field. • We increase the performance of the method by enforcing known physical constraints. • The method is not tied to any particular machine learning algorithm. • The method does not impose any restriction on the solver of choice. [ABSTRACT FROM AUTHOR]

In this paper, we study two-dimensional multiscale chemotaxis models based on a combination of the macroscopic evolution equation for chemoattractant and microscopic models for cell evolution. The latter is governed by a Boltzmann-type kinetic equation with a local turning kernel operator which describes the velocity change of the cells. The parabolic scaling yields a non-dimensional kinetic model with a small parameter, which represents the mean free path of the cells. We propose a new asymptotic preserving numerical scheme that reflects the convergence of the studied micro-macro model to its macroscopic counterpart-the Patlak-Keller-Segel system-in the singular limit. The method is based on the operator splitting strategy and a suitable combination of the higher-order implicit and explicit time discretizations. In particular, we use the so-called even-odd decoupling and approximate the stiff terms arising in the singular limit implicitly. We prove that the resulting scheme satisfies the asymptotic preserving property. More precisely, it yields a consistent approximation of the Patlak-Keller-Segel system as the mean-free path tends to 0. The derived asymptotic preserving method is used to get better insight to the blowup behavior of two-dimensional kinetic chemotaxis model. [ABSTRACT FROM AUTHOR]