Mechanistic Computational Model Research Articles

I’m stuck! How to efficiently debug computational solid mechanics models so you can enjoy the beauty of simulations

A substantial fraction of the time that computational modellers dedicate to developing their models is actually spent trouble-shooting and debugging their code. However, how this process unfolds is seldom spoken about, maybe because it is hard to articulate as it relies mostly on the mental catalogues we have built with the experience of past failures. To help newcomers to the field of material modelling, here we attempt to fill this gap and provide a perspective on how to identify and fix mistakes in computational solid mechanics models. To this aim, we describe the components that make up such a model and then identify possible sources of errors. In practice, finding mistakes is often better done by considering the symptoms of what is going wrong. As a consequence, we provide strategies to narrow down where in the model the problem may be, based on observation and a catalogue of frequent causes of observed errors. In a final section, we also discuss how one-time bug-free models can be kept bug-free in view of the fact that computational models are typically under continual development. We hope that this collection of approaches and suggestions serves as a “road map” to find and fix mistakes in computational models, and more importantly, keep the problems solved so that modellers can enjoy the beauty of material modelling and simulation.

Bioabsorbable, elastomer-coated magnesium alloy coils for treating saccular cerebrovascular aneurysms

Cerebral aneurysm embolization is a therapeutic approach to prevent rupture and resultant clinical sequelae. Current, non-biodegradable metallic coils (platinum or tungsten) are the first-line choice to secure cerebral aneurysms. However, clinical studies report that up to 17% of aneurysms recur within 1 year after coiling, leading to retreatment and additional surgery. It would be ideal for the aneurysm coiling material to induce acute thrombotic occlusion, contribute to a tissue development process to fortify the degenerated vessel wall, and ultimately resorb to avoid leaving a permanent foreign body. With these properties in mind, a new fatty amide-based polyurethane urea (PHEUU) elastomer was synthesized and coated on biodegradable metallic (Mg alloy) coils to prepare a bioabsorbable cerebral saccular aneurysm embolization device. The chemical structure of PHEUU was confirmed using two-dimensional nuclear magnetic resonance spectroscopy. PHEUU showed comparable physical properties to elastomeric biodegradable polyurethanes lacking fatty amide immobilization, modest enzymatic degradation profiles in the first 8 wks, inherent antioxidant activity (>70% at 48 h), no cytotoxicity, and better protection for the underlying Mg alloy than poly(lactic-co-glycolic acid) (PLGA) against surface corrosion and cracking. Rat aortic smooth muscle cell attachment and platelet deposition were higher with the PHEUUs compared to bare or PLGA coated Mg alloy in vitro. PHEUU-coated Mg alloy coils showed the potential to design a fully bioabsorbable embolization coil amenable to clinical placement conditions based on computational mechanics modeling and blood-contacting test using an in vitro aneurysm model. In vivo studies using a mouse aneurysm model elicited comparable inflammatory cytokine expression to a commercially available platinum coil.

Designing of Thiazolidinones for COVID-19 and its Allied Diseases: An In silico Evaluation.

In silico studies in terms of density functional theory (DFT), molecular docking, and ADMET (absorption, distribution, metabolism, excretion and toxicity) were performed for 55 thiazolidinones compounds derived from different amines and aldehydes. DFT is a computational quantum mechanical modeling method used to predict the various properties of the compounds. Different parameters such as Electronegativity (x), Chemical Hardness (ŋ), Chemical Potential (μ), Ionization potential (IP), and Electron Affinity (EA), etc. were calculated by Koopmans theorem. The compounds were docked with Molecular Operating Environment (MOE) software using already reported PDB files of BChE, AChE, and α‐glucosidase. To analyze the Spike Glycoprotein of SARS‐Cov‐2 and heterocyclic compounds, molecular interactions study was carried out between Spike Glycoprotein of SARS‐Cov‐2 (6VXX) and 55 synthetic heterocyclic compounds. It was performed by the utilization of PyRx Virtual Screening Tool and AutoDock Vina based virtual environment was used in PyRx. Maximum binding affinity was observed with compound A7 which was −8.7 kcal/mol and then with A5 which was −8.5 respectively. In the case of the AChE enzyme, B5 has a maximum docking score of −12.9027 kcal/mol while C7 depicted the maximum score for the BChE enzyme with a value of −8.6971 kcal/mol. The docking studies revealed that C6 compound has maximum binding capacity toward glucosidase (−14.8735 kcal/mol). ADMET properties of under consideration compounds were determined by Swiss online‐based software which concluded that these molecules have a drug‐like properties and having no violation.

Computational modeling of protracted HCMV replication using genome substrates and protein temporal profiles

Human cytomegalovirus (HCMV) is a major cause of illness in immunocompromised individuals. The HCMV lytic cycle contributes to the clinical manifestations of infection. The lytic cycle occurs over ∼96 h in diverse cell types and consists of viral DNA (vDNA) genome replication and temporally distinct expression of hundreds of viral proteins. Given its complexity, understanding this elaborate system can be facilitated by the introduction of mechanistic computational modeling of temporal relationships. Therefore, we developed a multiplicity of infection (MOI)-dependent mechanistic computational model that simulates vDNA kinetics and late lytic replication based on in-house experimental data. The predictive capabilities were established by comparison to post hoc experimental data. Computational analysis of combinatorial regulatory mechanisms suggests increasing rates of protein degradation in association with increasing vDNA levels. The model framework also allows expansion to account for additional mechanisms regulating the processes. Simulating vDNA kinetics and the late lytic cycle for a wide range of MOIs yielded several unique observations. These include the presence of saturation behavior at high MOIs, inefficient replication at low MOIs, and a precise range of MOIs in which virus is maximized within a cell type, being 0.382 IU to 0.688 IU per fibroblast. The predicted saturation kinetics at high MOIs are likely related to the physical limitations of cellular machinery, while inefficient replication at low MOIs may indicate a minimum input material required to facilitate infection. In summary, we have developed and demonstrated the utility of a data-driven and expandable computational model simulating lytic HCMV infection.

Machine learning aided synthesis and screening of HER catalyst: Present developments and prospects

ABSTRACT There have been significant advancements in catalysis today, especially with the aid of artificial intelligence (AI). Understanding the relationship between the descriptors and catalytic performance and underlying principles is crucial for hydrogen evolution reaction (HER) catalysis. As of today, the usage and application of machine learning (ML) are soaring and there has been a phenomenal demand owing to its immense pattern recognition capability and optimization potential. Its applications in computational quantum mechanical modeling have inspired promising development in HER catalyst scrutiny and screening. The present communication reviews the general scheme of ML, data sources, and computational tools for HER catalyst screening and therefore provides a strong basis for its application. Furthermore, the analysis elucidates how ML algorithms are used for the prediction of a) adsorption energies, b) reaction descriptors, c) structure–property relationships, d) catalyst discovery, and e) synthesis conditions and reaction design of HER catalysts. Lastly, key constraints and the need for an integrative platform have been highlighted. Based on literature surveys, recent trends, material advancements, systems understanding, and key challenges in deploying ML tools, future directions with a road map have been provided for an evolved design and development in the case of HER catalysis.

Mechanics Informed Neutron Noise Monitoring to Perform Remote Condition Assessment for Reactor Vessel Internals

Abstract The use of neutron noise analysis in pressurized water reactors to detect and diagnose degradation represents the practice of pro-active structural health monitoring for reactor vessel internals. Recent enhancements to this remote condition monitoring and diagnostic computational framework quantify the sensitivity of the structural dynamics to different degradation scenarios. This methodology leverages benchmarked computational structural mechanics models and machine learning methods to enhance the interpretability of neutron noise measurement results. The novelty of the methodology lies not in the particular technologies and algorithms but our amalgamation into a holistic computational framework for structural health monitoring. Recent experience revealed the successful deployment of this methodology to pro-actively diagnose different degradation scenarios, thus enabling prognostic asset management for reactor structures.

Entire Process Simulation of Friction Stir Welding — Part 1: Experiments and Simulation

Understanding structure-parameter-property relationships in friction stir welding of aluminum alloys is a challenge despite its wide application in load-bearing components. In this paper, we propose a combined strategy for mapping the macro- and microstructural responses of these joints. A combined model based on experiment validation was adopted for the prediction of tensile strength. This included the computational fluid dynamics model, precipitation evolution model, dynamic recrystallization and recovery model, and computational solid mechanics model. The comparison between the experimental results and the combined model proved the rationality and accuracy of this numerical model.

Endothelial Regulation of Microvascular Growth and Stability by Ang‐Tie and VEGF Signaling Pathways: A Mechanistic Computational Systems Biology Model

The Angiopoietin‐Tie (Ang‐Tie) pathway is a key signaling pathway regulating vascular stability and permeability, and it significantly intersects and crosstalk with the vascular endothelial growth factor (VEGF) signaling pathway, a major signaling pathway regulating angiogenesis and vascular permeability. Disrupted Ang‐Tie and VEGF signaling is linked to vascular dysfunction related to cancer, systemic inflammation, cardiovascular disease, and diabetic macular edema. Ang‐Tie pathway is regulated by various molecular mechanisms, including ligand‐receptor interactions, receptor multimerization, binding of co‐receptor Tie1, inhibition by vascular endothelial protein tyrosine phosphatase (VE‐PTP), receptor extracellular domain shedding, junctional localization, trafficking, and turnover. VEGF pathway is regulated by ligand binding, homo‐ and heterodimerization of VEGF receptors VEGFR1 and VEGFR2, interaction with neuropilin, thrombospondin‐1/CD47, receptor internalization, recycling, and degradation. Ang‐Tie and VEGF signaling pathways also share crosstalk mechanisms and integrative downstream signaling through phosphoinositide 3‐kinases (PI3K)/Akt, sphingosine‐dependent Erk activation, Src sequestration via RhoA/mDia, and calcium cycling, forming a complex reaction network that requires an integrative model to study the signaling outcomes on a systems level. The present study uses a mechanistic computational model of the Ang‐Tie and VEGF signaling pathways, their regulatory mechanisms, crosstalk, and integrative downstream signaling to quantitatively characterize the molecular control of vascular growth, permeability, and stability by endothelial cells. The model captures and reproduces key experimental observations of the Ang‐Tie/VEGF signaling network with detailed molecular mechanisms. The model significantly expands our previous computational models of the Ang‐Tie signaling pathway [1,2] and integrates our systems biology understanding of the VEGF pathway and their crosstalk. The model serves as a platform for quantitatively predicting the signaling outcome of varying ligand concentrations and receptor expressions in physiological and pathological conditions, identifying molecular mechanisms and targets for therapeutic interventions targeting the signaling network, and simulating the effects of drugs and their combinations on the signaling outcome on a network level.

Computational Modeling of Substrate‐Dependent Differential Regulation of Mitochondrial Bioenergetics in the Heart and Kidney Cortex and Outer Medulla

RationaleIt is recognized that the kinetics and efficiency of mitochondrial O2 consumption for ATP production depend on the choice of respiratory substrates. Various substrates differentially generate the reducing equivalents NADH and FADH2 via the tricarboxylic acid (TCA) cycle that feed electrons to the electron transport chain (ETC) driving oxidative phosphorylation (OxPhos). However, the contributions of these substrates and their combinations have not been systematically characterized in either the heart or the kidney, the two major energy consuming organs in the body.MethodBioenergetic responses (respiration and membrane potential) of mitochondria isolated from the heart and kidney cortex and outer medulla (OM) of adult Sprague‐Drawly rats were characterized with different substrate combinations and different ADP perturbations. To better understand these distinct substrate and tissue‐specific mitochondrial bioenergetics, thermodynamically‐constrained mechanistic computational models were developed by integrating the kinetics and regulation of mitochondrial substrate transport and oxidation, TCA cycle, and ETC/OxPhos, incorporating published data and the novel mitochondrial bioenergetics data from our laboratory. Intrinsic model parameters such as Michaelis‐Menten constants were assumed identical and fixed for the heart and kidney, while extrinsic model parameters such as maximal enzymatic rates were separately estimated based on experimental data for the heart and kidney mitochondrial bioenergetics.ResultsConsistent with the data, model simulations of the ADP‐induced state 3 responses of the heart and kidney cortex and OM mitochondria yielded dramatically different values, which for a given organ were also very distinct for different substrates. In addition, heart mitochondria energized with succinate without rotenone (blocker of complex I) and saturated [ADP] failed to produce a robust state 3 response. This was in contrast to kidney cortex and OM mitochondria for which responses to succinate±rotenone were similar, indicating that reverse electron transfer via complex I played less prominent regulatory role in the kidney than the heart. In addition, oxaloacetate, a TCA cycle intermediate, was shown to accumulate faster in the heart compared to the kidney, thereby potently inhibiting succinate oxidation more in the heart than the kidney. As such, the model was able to quantitatively characterize how various substrate combinations account for the differential contributions of different substrate oxidation pathways within the mitochondria towards OxPhos and ATP synthesis in the heart and kidney cortex and OM.ConclusionModeling of these data provided a deeper quantitative understanding of key determinants of kinetic and molecular mechanisms involved in the differential regulation of substrate‐dependent mitochondrial bioenergetics in the heart and kidney cortex and OM.

Root Causes and Mechanisms of Failure of Wind Turbine Blades: Overview

A review of the root causes and mechanisms of damage and failure to wind turbine blades is presented in this paper. In particular, the mechanisms of leading edge erosion, adhesive joint degradation, trailing edge failure, buckling and blade collapse phenomena are considered. Methods of investigation of different damage mechanisms are reviewed, including full scale testing, post-mortem analysis, incident reports, computational simulations and sub-component testing. The most endangered regions of blades include the protruding parts (tip, leading edges), tapered and transitional areas and bond lines/adhesives. Computational models of different blade damage mechanisms are discussed. The role of manufacturing defects (voids, debonding, waviness, other deviations) for the failure mechanisms of wind turbine blades is highlighted. It is concluded that the strength and durability of wind turbine blades is controlled to a large degree by the strength of adhesive joints, interfaces and thin layers (interlaminar layers, adhesives) in the blade. Possible solutions to mitigate various blade damage mechanisms are discussed.

A Computational Model of Bacterial Population Dynamics in Gastrointestinal Yersinia enterocolitica Infections in Mice.

Simple SummaryComputational modeling of bacterial infection is an attractive way to simulate infection scenarios. In the long-term, such models could be used to identify factors that make individuals more susceptible to infection, or how interference with bacterial growth influences the course of bacterial infection. This study used different mouse infection models (immunocompetent, lacking a microbiota, and immunodeficient models) to develop a basic mathematical model of a Yersinia enterocolitica gastrointestinal infection. We showed that our model can reflect our findings derived from mouse infections, and we demonstrated how crucial the exact knowledge about parameters influencing the population dynamics is. Still, we think that computational models will be of great value in the future; however, to foster the development of more complex models, we propose the broad implementation of the interdisciplinary training of mathematicians and biologists.The complex interplay of a pathogen with its virulence and fitness factors, the host’s immune response, and the endogenous microbiome determine the course and outcome of gastrointestinal infection. The expansion of a pathogen within the gastrointestinal tract implies an increased risk of developing severe systemic infections, especially in dysbiotic or immunocompromised individuals. We developed a mechanistic computational model that calculates and simulates such scenarios, based on an ordinary differential equation system, to explain the bacterial population dynamics during gastrointestinal infection. For implementing the model and estimating its parameters, oral mouse infection experiments with the enteropathogen, Yersinia enterocolitica (Ye), were carried out. Our model accounts for specific pathogen characteristics and is intended to reflect scenarios where colonization resistance, mediated by the endogenous microbiome, is lacking, or where the immune response is partially impaired. Fitting our data from experimental mouse infections, we can justify our model setup and deduce cues for further model improvement. The model is freely available, in SBML format, from the BioModels Database under the accession number MODEL2002070001.

Determination of length scale parameters of strain gradient continuum theory for crystalline solids using a computational quantum mechanical model based on density functional theory

Although the classical continuum theory is advantageous in finding solutions to a wide range of engineering problems, it cannot describe some phenomena such as dispersion of acoustic waves, effects of surfaces and interfaces on the mechanical behavior of small-scale structures, and microstructure contribution in special materials. Owing to this fact, several enhanced continuum theories have evolved in the literature. However, the difficulty in determination of the length scale parameters that appear in the governing equations of such theories hampers their widespread use in practice. To date, except for a very limited number of materials, there is no known experimental procedure for the identification of these parameters. In this research, the internal length scales for an augmented continuum theory, i.e., Mindlin's strain gradient theory, have been theoretically determined for some crystalline materials with cubic structure that are of engineering interest, using ab initio DFT. According to the values obtained for these parameters, it can be perceived that the strain gradient theory is a valuable tool for capturing the size effects at even the smallest scales comparable to the dimensions of a unit cell of a crystal lattice.

Predictive Radiation Oncology – A New NCI–DOE Scientific Space and Community

With a widely attended virtual kickoff event on January 29, 2021, the National Cancer Institute (NCI) and the Department of Energy (DOE) launched a series of 4 interactive, interdisciplinary workshops-and a final concluding "World Café" on March 29, 2021-focused on advancing computational approaches for predictive oncology in the clinical and research domains of radiation oncology. These events reflect 3,870 human hours of virtual engagement with representation from 8 DOE national laboratories and the Frederick National Laboratory for Cancer Research (FNL), 4 research institutes, 5 cancer centers, 17 medical schools and teaching hospitals, 5 companies, 5 federal agencies, 3 research centers, and 27 universities. Here we summarize the workshops by first describing the background for the workshops. Participants identified twelve key questions-and collaborative parallel ideas-as the focus of work going forward to advance the field. These were then used to define short-term and longer-term "Blue Sky" goals. In addition, the group determined key success factors for predictive oncology in the context of radiation oncology, if not the future of all of medicine. These are: cross-discipline collaboration, targeted talent development, development of mechanistic mathematical and computational models and tools, and access to high-quality multiscale data that bridges mechanisms to phenotype. The workshop participants reported feeling energized and highly motivated to pursue next steps together to address the unmet needs in radiation oncology specifically and in cancer research generally and that NCI and DOE project goals align at the convergence of radiation therapy and advanced computing.

Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is associated with risk of sudden cardiac death (SCD) due to ventricular arrhythmias (VAs) arising from the proliferation of fibrosis in the heart. Current clinical risk stratification criteria inadequately identify at-risk patients in need of primary prevention of VA. Here, we use mechanistic computational modeling of the heart to analyze how HCM-specific remodeling promotes arrhythmogenesis and to develop a personalized strategy to forecast risk of VAs in these patients. We combine contrast-enhanced cardiac magnetic resonance imaging and T1 mapping data to construct digital replicas of HCM patient hearts that represent the patient-specific distribution of focal and diffuse fibrosis and evaluate the substrate propensity to VA. Our analysis indicates that the presence of diffuse fibrosis, which is rarely assessed in these patients, increases arrhythmogenic propensity. In forecasting future VA events in HCM patients, the imaging-based computational heart approach achieved 84.6%, 76.9%, and 80.1% sensitivity, specificity, and accuracy, respectively, and significantly outperformed current clinical risk predictors. This novel VA risk assessment may have the potential to prevent SCD and help deploy primary prevention appropriately in HCM patients.

Cortical tension initiates the positive feedback loop between cadherin and F-actin

Adherens junctions physically link two cells at their contact interface via extracellular binding between cadherin molecules and intracellular interactions between cadherins and the actin cytoskeleton. Cadherin and actomyosin cytoskeletal dynamics are regulated reciprocally by mechanical and chemical signals, which subsequently determine the strength of cell-cell adhesions and the emergent organization and stiffness of the tissues they form. However, an understanding of the integrated system is lacking. We present a new mechanistic computational model of intercellular junction maturation in a cell doublet to investigate the mechanochemical cross talk that regulates adherens junction formation and homeostasis. The model couples a two-dimensional lattice-based simulation of cadherin dynamics with a reaction-diffusion representation of the reorganising actomyosin network through its regulation by Rho signalling at the intracellular junction. We demonstrate that local immobilization of cadherin induces cluster formation in a cis-less-dependent manner. We then recapitulate the process of cell-cell contact formation. Our model suggests that cortical tension applied on the contact rim can explain the ring distribution of cadherin and actin filaments (F-actin) on the cell-cell contact of the cell doublet. Furthermore, we propose and test the hypothesis that cadherin and F-actin interact like a positive feedback loop, which is necessary for formation of the ring structure. Different patterns of cadherin distribution were observed as an emergent property of disturbances of this positive feedback loop. We discuss these findings in light of available experimental observations on underlying mechanisms related to cadherin/F-actin binding and the mechanical environment.

Investigating Histone Modification Dynamics by Mechanistic Computational Modeling.

The maintenance of transcriptional states regulated by histone modifications and controlled switching between these states are fundamental concepts in our understanding of nucleosome-mediated epigenetic memory. Any approach relying on genome-wide bioinformatic analyses alone offers limited scope for dissecting the molecular mechanisms involved in maintenance and switching. Mechanistic mathematical models-describing the dynamics of histone modifications at individual genomic loci-offer an alternative way to investigate these mechanisms. These models, in conjunction with quantitative experimental data-ChIP data, quantification of mRNA levels, and single-cell fluorescence tracking in clonal lineages-can generate predictions that drive more targeted experiments, allowing us to understand mechanisms that would be challenging to unravel by a purely experimental approach. In this chapter, we describe a generic stochastic modeling framework that can be used to capture histone modification dynamics and associated molecular processes-including transcription and read-write feedback by chromatin modifying complexes-at individual genomic loci. Using a specific example-transcriptional silencing by Polycomb-mediated H3K27 methylation-we demonstrate how to construct and simulate a stochastic histone modification model. We provide a step-by-step guide to programming simulations for such a model and discuss how to analyze the simulation output.

Integration of Engineered Delivery with the Pharmacokinetics of Medical Candidates via Physiology-Based Pharmacokinetics.

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic computational model that can be used to predict a drug product's ADME (absorption, distribution, metabolism, and excretion) and pharmacokinetics (PK). In recent years, PBPK modeling and simulation has been used increasingly to address many biopharmaceutics and clinical pharmacology questions, such as the effect of formulations, intrinsic factors (age, organ dysfunction, etc.), and extrinsic factors (comedications, food) on the PK of an investigational drug product. In this chapter, we will briefly introduce various PBPK models for ADME prediction and general procedures for PBPK modeling and simulations. The readers are encouraged to read updated literature on new applications of PBPK modeling and simulation which is still an emerging area in pharmaceutical development.

Dynamic Multiscale Regulation of Perfusion Recovery in Experimental Peripheral Arterial Disease: A Mechanistic Computational Model

In peripheral arterial disease (PAD), the degree of endogenous capacity to modulate revascularization of limb muscle is central to the management of leg ischemia. To characterize the multiscale and multicellular nature of revascularization in PAD, we have developed the first computational systems biology model that mechanistically incorporates intracellular, cellular, and tissue-level features critical for the dynamic reconstitution of perfusion after occlusion-induced ischemia. The computational model was specifically formulated for a preclinical animal model of PAD (mouse hindlimb ischemia [HLI]), and it has gone through multilevel model calibration and validation against a comprehensive set of experimental data so that it accurately captures the complex cellular signaling, cell-cell communication, and function during post-HLI perfusion recovery. As an example, our model simulations generated a highly detailed description of the time-dependent spectrum-like macrophage phenotypes in HLI, and through model sensitivity analysis we identified key cellular processes withpotential therapeutic significance in the pathophysiology of PAD. Furthermore, we computationally evaluated the invivo effects of different targeted interventions on post-HLI tissue perfusion recovery in a model-based, data-driven, virtual mouse population and experimentally confirmed the therapeutic effect ofanovel model-predicted intervention in real HLI mice. This novel multiscale model opens up a new avenuetouse integrative systems biology modeling to facilitate translational research in PAD.

Genome and EnvironmentBased Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction.

Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G×E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G×E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G×E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G×E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G×E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G×E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G×E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G×E.

Constructing a two-level computational model of cross-ply fiberglass-reinforced plastic from micromechanical testing

This paper deals with solving the problem of constructing two-level computational models relating the stress-strain state at the microscale to the stress-strain state of the structural component at the macroscale for layered polymer composite materials with unidirectional fiber stacking in a monolayer. The paper presents a two-level computational mechanical model of a ten-layer cross-ply fiberglass-reinforced polymer composite with (0/90) laying patterns. The model is based on microindentation data. The model is constructed using the finite element method. The constructed model has allowed us to predict the elastic properties of the composite and to calculate stresses at the microscale and macroscale levels of a specimen prior to fracture under tensile conditions. The elastic properties of the polymer composite material predicted by the computational model are compared with the properties obtained from real experiments. Calculated for fiberglass, the elasticity modulus and Poisson’s ratio are 31 GPa and 0.11, respectively. Similar elastic properties obtained from real experiments are 28 GPa and 0.10. The technique used in the paper can be applied to computational experiments with an actual structural component made of a multilayer polymer composite in order to determine the stress-strain state at the micro- and macroscale under external mechanical impact on the structure.