Geometrical nonlinear elasticity of axon under tension: A coarse-grained computational study.

Axon bundles cross-linked by microtubule (MT) associate proteins and bounded by a shell skeleton are critical for normal function of neurons. Understanding effects of the complexly geometrical parameters on their mechanical properties can help gain a biomechanical perspective on the neurological functions of axons and thus brain disorders caused by the structural failure of axons. Here, the tensile mechanical properties of MT bundles cross-linked by tau proteins are investigated by systematically tuning MT length, axonal cross-section radius, and tau protein spacing in a bead-spring coarse-grained model. Our results indicate that the stress-strain curves of axons can be divided into two regimes, a nonlinear elastic regime dominated by rigid-body like inter-MT sliding, and a linear elastic regime dominated by affine deformation of both tau proteins and MTs. From the energetic analyses, first, the tau proteins dominate the mechanical performance of axons under tension. In the nonlinear regime, tau proteins undergo a rigid-body like rotating motion rather than elongating, whereas in the nonlinear elastic regime, tau proteins undergo a flexible elongating deformation along the MT axis. Second, as the average spacing between adjacent tau proteins along the MT axial direction increases from 25 to 125 nm, the Young's modulus of axon experiences a linear decrease whereas with the average space varying from 125 to 175 nm, and later reaches a plateau value with a stable fluctuation. Third, the increment of the cross-section radius of the MT bundle leads to a decrease in Young's modulus of axon, which is possibly attributed to the decrease in MT numbers per cross section. Overall, our research findings offer a new perspective into understanding the effects of geometrical parameters on the mechanics of MT bundles as well as serving as a theoretical basis for the development of artificial MT complexes potentially toward medical applications.

A review of inflammatory mechanism in airway diseases.

BACKGROUND: Inflammation in the lung is the body's natural response to injury. It acts to remove harmful stimuli such as pathogens, irritants, and damaged cells and initiate the healing process. Acute and chronic pulmonary inflammation are seen in different respiratory diseases such as; acute respiratory distress syndrome, chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis (CF). FINDINGS: In this review, we found that inflammatory response in COPD is determined by the activation of epithelial cells and macrophages in the respiratory tract. Epithelial cells and macrophages discharge transforming growth factor-ß (TGF-ß), which trigger fibroblast proliferation and tissue remodeling. Asthma leads to airway hyper-responsiveness, obstruction, mucus hyper-production, and airway-wall remodeling. Cytokines, allergens, chemokines, and infectious agents are the main stimuli that activate signaling pathways in epithelial cells in asthma. Mutation of the CF transmembrane conductance regulator (CFTR) gene results in CF. Mutations in CFTR influence the lung epithelial innate immune function that leads to exaggerated and ineffective airway inflammation that fails to abolish pulmonary pathogens. We present mechanistic computational models (based on ordinary differential equations, partial differential equations and agent-based models) that have been applied in studying the complex physiological and pathological mechanisms of chronic inflammation in different airway diseases. CONCLUSION: The scope of the present review is to explore the inflammatory mechanism in airway diseases and highlight the influence of aging on airways' inflammation mechanism. The main goal of this review is to encourage research collaborations between experimentalist and modelers to promote our understanding of the physiological and pathological mechanisms that control inflammation in different airway diseases.

A coarse-grained model for mechanical behavior of phosphorene sheets.

The popularity of phosphorene (known as monolayer black phosphorus) in electronic devices relies on not only its superior electrical properties, but also its mechanical stability beyond the nanoscale. However, the mechanical performance of phosphorene beyond the nanoscale remains poorly explored owing to the spatiotemporal limitation of experimental observations, first-principles calculations, and atomistic simulations. To overcome this limitation, here a coarse-grained molecular dynamics (CG-MD) model is developed via a strain energy conservation approach to offer a new computational tool for the investigation of the mechanical properties of phosphorene beyond the nanoscale. The mechanical properties of a single phosphorene sheet are first characterized by all-atom molecular dynamics (AA-MD) simulations, followed by a force-field parameter optimization of the CG-MD model by matching these mechanical properties from AA-MD simulations. The intrinsic out-of-plane puckered feature is conserved in our CG-MD model, rendering mechanical anisotropy and heterogeneity in both the in-plane and out-of-plane directions preserved. The results indicate that our coarse-grained model is able to accurately capture the anisotropic in-plane mechanical performance of phosphorene and quantitatively reproduce Young's modulus, ultimate strength, and fracture strain under various environmental temperatures. Our CG-MD model can also capture the anisotropic out-of-plane bending stiffness of phosphorene. We demonstrate the applicability of our model in capturing the fracture toughness of phosphorene in both the armchair and zigzag directions by comparison with the results from AA-MD simulations. This CG-MD model proposed here offers greater capability to perform mechanical mesoscale simulations for phosphorene-based systems, allowing for a deeper understanding of the mechanical properties of phosphorene beyond the nanoscale, and the potential transferability of the developed force-field can help design hybrid phosphorene devices and structures.

Abnormal linear elasticity in polycrystalline phosphorene.

Phosphorene, also known as monolayer black phosphorous, has been widely used in electronic devices due to its superior electrical properties. However, its relatively low Young's modulus, low fracture strength and susceptibility to structural failure has limited its application in nano devices. Therefore, in order to design more mechanically reliable devices that utilize phosphorene, it is necessary to explore the mechanical properties of polycrystalline phosphorene. Here molecular dynamics simulations are performed to study the effect of grain size on the mechanical performance of polycrystalline phosphorene sheets. Unlike other two-dimension materials with planar crystalline structure, polycrystalline phosphorene sheets are almost linear elastic, resulting from its high bending stiffness due to its intrinsic buckled crystalline structure. Moreover, the percentage increase of stiffness for polycrystalline phosphorene associated with the increase of grain size from 2 to 12 nm is only 15.9%, much smaller than that for other two-dimension materials with planar crystalline structure. This insensitivity could be attributed to the small difference between the elastic modulus of the crystalline phase and amorphous phase of polycrystalline phosphorene. In addition, the strength deduction obeys well a logarithm relation of grain size, well explained by the dislocation pile-up theory analogous to that of polycrystalline graphene. Overall, our findings provide a better understanding of mechanical properties of polycrystalline phosphorene and establish a guideline for manufacturing and designing novel phosphorene-based nano devices and nano structures.

Fracture mechanisms in multilayer phosphorene assemblies: from brittle to ductile.

The outstanding mechanical performance of nacre has stimulated numerous studies on the design of artificial nacres. Phosphorene, a new two-dimensional (2D) material, has a crystalline in-plane structure and non-bonded interaction between adjacent flakes. Therefore, multi-layer phosphorene assemblies (MLPs), in which phosphorene flakes are piled up in a staggered manner, may exhibit outstanding mechanical performance, especially exceptional toughness. Therefore, molecular dynamics simulations are performed to study the dependence of the mechanical properties on the overlap distance between adjacent phosphorene layers and the number of phosphorene flakes per layer. The results indicate that when the flake number is equal to 1, a transition of fracture patterns is observed by increasing the overlap distance, from a ductile failure controlled by interfacial friction to a brittle failure dominated by the breakage of covalent bonds inside phosphorene flakes. Moreover, the failure pattern can be tuned by changing the number of flakes in each phosphorene layer. The results imply that the ultimate strength follows a power law with the exponent -0.5 in terms of the flake number, which is in good agreement with our analytical model. Furthermore, the flake number in each phosphorene layer is optimized as 2 when the temperature is 1 K in order to potentially achieve both high toughness and strength. Moreover, our results regarding the relations between mechanical performance and overlap distance can be explained well using a shear-lag model. However, it should be pointed out that increasing the temperature of MLPs could cause the transition of fracture patterns from ductile to brittle. Therefore, the optimal flake number depends heavily on temperature to achieve both its outstanding strength and toughness. Overall, our findings unveil the fundamental mechanism at the nanoscale for MLPs as well as provide a method to design phosphorene-based structures with targeted properties via tunable overlap distance and flake number in phosphorene layers.

A biomechanical model of wound contraction and scar formation.

We propose a biomechanical model for investigating wound contraction mechanism and resulting scarring. Extracellular matrix is modeled as fiber-reinforced anisotropic soft tissue, with its elastic properties dynamically changing with the density and orientation of collagen fibers. Collagen fibers are deposited by fibroblasts infiltrating the wound space, and are dynamically aligned with both migrating fibroblasts and tissue residing tension field. Our new 2D hybrid agent-based model provides a comprehensive platform for examining the mechanobiology in wound contraction and scar formation. Simulation results are consistent with experimental observations and are able to reveal the effects of wound morphology and mechanical environment on contraction patterns. Our model results support the hypothesis that scar formation is the product of collagen fiber synthesis and alignment in the presence of the tensile stress field generated by a wound contraction process.

Mechanical Ventilator Parameter Estimation for Lung Health through Machine Learning.

Patients whose lungs are compromised due to various respiratory health concerns require mechanical ventilation for support in breathing. Different mechanical ventilation settings are selected depending on the patient's lung condition, and the selection of these parameters depends on the observed patient response and experience of the clinicians involved. To support this decision-making process for clinicians, good prediction models are always beneficial in improving the setting accuracy, reducing treatment error, and quickly weaning patients off the ventilation support. In this study, we developed a machine learning model for estimation of the mechanical ventilation parameters for lung health. The model is based on inverse mapping of artificial neural networks with the Graded Particle Swarm Optimizer. In this new variant, we introduced grouping and hierarchy in the swarm in addition to the general rules of particle swarm optimization to further improve its prediction performance of the mechanical ventilation parameters. The machine learning model was trained and tested using clinical data from canine and feline patients at the University of Georgia College of Veterinary Medicine. Our model successfully generated a range of parameter values for the mechanical ventilation applied on test data, with the average prediction values over multiple trials close to the target values. Overall, the developed machine learning model should be able to predict the mechanical ventilation settings for various respiratory conditions for patient's survival once the relevant data are available.

Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model.

Traumatic brain injury (TBI) at a young age can lead to the development of long-term functional impairments. Severity of injury is well demonstrated to have a strong influence on the extent of functional impairments; however, identification of specific magnetic resonance imaging (MRI) biomarkers that are most reflective of injury severity and functional prognosis remain elusive. Therefore, the objective of this study was to utilize advanced statistical approaches to identify clinically relevant MRI biomarkers and predict functional outcomes using MRI metrics in a translational large animal piglet TBI model. TBI was induced via controlled cortical impact and multiparametric MRI was performed at 24 hours and 12 weeks post-TBI using T1-weighted, T2-weighted, T2-weighted fluid attenuated inversion recovery, diffusion-weighted imaging, and diffusion tensor imaging. Changes in spatiotemporal gait parameters were also assessed using an automated gait mat at 24 hours and 12 weeks post-TBI. Principal component analysis was performed to determine the MRI metrics and spatiotemporal gait parameters that explain the largest sources of variation within the datasets. We found that linear combinations of lesion size and midline shift acquired using T2-weighted imaging explained most of the variability of the data at both 24 hours and 12 weeks post-TBI. In addition, linear combinations of velocity, cadence, and stride length were found to explain most of the gait data variability at 24 hours and 12 weeks post-TBI. Linear regression analysis was performed to determine if MRI metrics are predictive of changes in gait. We found that both lesion size and midline shift are significantly correlated with decreases in stride and step length. These results from this study provide an important first step at identifying relevant MRI and functional biomarkers that are predictive of functional outcomes in a clinically relevant piglet TBI model. This study was approved by the University of Georgia Institutional Animal Care and Use Committee (AUP: A2015 11-001) on December 22, 2015.

Optimization of hydrogen bonds for combined DNA/collagen complex.

Many natural and biological systems including collagen and DNA polymers are formed by a process of molecular self-assembly. In this paper, we developed two novel structural models and built heterogeneous DNA/collagen complexes through a preferable arrangement of multiple hydrogen bonds (H-bonds) between DNA and collagen molecules. The simulation results based on three sets of criteria indicate that one of the models with five collagen molecules, which are positioned around each strand of DNA molecules emerged to form a suitable polymer complex with the maximum number of H-bonds. Our predictions quantitatively validated and agreed with the molecular structure reported by Mrevlishvili and Svintradze [2005. Int. J. Biol. Macromol. 36, 324-326].

An integrated parametric model for MT self-assembly formation analysis.

Self-assembly is a ubiquitous, naturally occurring, robust process in many living organisms. Microtubule (MT), a self-organization system assemble itself into functional units by attaching to cellular structures. Modeling microtubule self-organization is of interest as microtubule forms a network of protein filaments that is critical to many processes in eukaryotic cells. In this paper, we propose an optimization framework that considers MT self-assembly starting from alpha (α) and beta (ß) tubulins as basic building blocks in the self-organization of MT. Using this framework we present separate analysis of MT self-assembly strength by considering two aspects of MT self-assembly. First, the affinity factor distribution between neighboring tubulins of an MT is considered for the analysis. Second, this paper also present an analysis of structural stability considering geometric parameter distribution of tubulins within an MT. We present separate algorithms for the analysis in detail. The proposed models show convergence and robustness under random initialization and thus justify the effectiveness of the proposed convergence criteria for stability analysis of MT self-organization. The proposed algorithms show the ability to emulate MT self-assembly from random initial configurations.

Influence of Pathogens and Mechanical Stimuli in Inflammation.

Inflammation is a process driven by underlying cell-cell communication and many other factors. In this study, a model of cell-cell communications was proposed to study factors driving the inflammation time course. Analyses of inflammations that are driven by the combined effects of strain (mechanical stimuli) and/or pathogens are considered in this paper. An agent-based model was employed to simulate inflammation where macrophages and fibroblasts influence each other through cell signaling cytokines that diffuse and spread in the tissue space. The communication network of macrophages and fibroblasts was then inferred and its network model (termed TE network) was generated and analyzed. The results suggest that factors driving inflammation time course can be discriminated by the characteristics of TE networks. Inflammation driven only by pathogens has certain TE network characteristics indicating slower and lower information exchange among cells. Multiple stimuli can help to maintain sufficient information exchange among cells, which is beneficial for inflammation resolution. The TE network captures the unfolding of the innate immune system over time, and the history of pathogens invasion. The resulting network leads to an improved understanding of the resilience of the system to future pathogen invasion.

Aging Effects on Alveolar Sacs Under Mechanical Ventilation.

Alveolar sacs are primarily responsible for gas exchange in the human respiratory system and lose their functionality with aging. Three-dimensional (3D) models of young and old human alveolar sacs were constructed and fluid-solid interaction was employed to investigate the contribution of age-related changes to decline in alveolar sacs function under mechanical ventilation (MV). Simulation results illustrated that compliance and pressure reduced in the alveolar sacs of the elderly adults, and they have to work harder to breathe. Morphological changes were found to be mainly responsible for the decline in alveolar sacs function. Influence of individual differences on the alveolar sacs function was negligible and 95% confidence intervals for compliance and work of breathing (WOB) using measures from different individuals also support this finding. Moreover, higher mortality risk was recorded for elderly adults who undergo MV. Specifically, ventilator devices setting has been identified as a potential parameter for compromising respiratory function in the elderly adults. Volume-controlled ventilation applied less pressure, whereas, pressure-controlled ventilation resulted in higher compliance in the alveolar sacs and decreased WOB. Sensitivity of alveolar sacs to ventilator setting under the volume-controlled mode illustrated that increasing breathing frequency and decreasing the ratio of inhalation to exhalation times and TV caused an increase in alveolar sacs expansion and compliance in older patients. Results from this study can help clinicians to develop individualized and effective ventilator protocols and to improve respiratory function in the elderly adults.

Mimicking Sub-Structures Self-Organization in Microtubules.

Microtubules (MTs) are highly dynamic polymers distributed in the cytoplasm of a biological cell. Alpha and beta globular proteins constituting the heterodimer building blocks combine to form these tubules through polymerization, controlled by the concentration of Guanosine-triphosphate (GTPs) and other Microtubule Associated Proteins (MAPs). MTs play a crucial role in many intracellular processes, predominantly in mitosis, organelle transport and cell locomotion. Current research in this area is focused on understanding the exclusive behaviors of self-organization and their association with different MAPs through organized laboratory experiments. However, the intriguing intelligence behind these tiny machines resulting in complex self-organizing structures is mostly unexplored. In this study, we propose a novel swarm engineering framework in modeling rules for these systems, by combining the principles of design with swarm intelligence. The proposed framework was simulated on a game engine and these simulations demonstrated self-organization of rings and protofilaments in MTs. Analytics from these simulations assisted in understanding the influence of GTPs on protofilament formation. Also, results showed that the population density of GTPs rather than their bonding probabilities played a crucial role in polymerization in forming microtubule substructures.

Physiochemical Effects of Nanoparticles on Cell Nuclear Complex Pore Transport: A Coarse-Grained Computational Model.

Understanding and controlling the interaction between nanoparticles and cell nuclei is critical to the development of the biomedical applications such as gene delivery, cellular imaging, and tumor therapy. Recent years have witnessed growing evidence that the size, shape, and the grafting density of the karyopherins ligands of nanoparticles provide a significant influence on the uptake mechanism of nanoparticles into cells; however, there is a lack of investigation into how these physical factors play a role in cellular nuclear uptake and how the nanoparticle enters the nucleus. Here, we build a computational framework to parametrically evaluate the effects of the size, shape, and the grafting density of the karyopherins ligands of designed nanoparticles on their transport through the nuclear pore complex of a cell nucleus so as to provide a novel scheme for nanoparticle design and precise nucleus-targeted therapy. Simulation results indicate that smaller spherical nanoparticles need to overcome a lower energy barrier than larger ones and also that nanoparticles with large grafting density exhibited greatly altered dynamics during the active transport process. Moreover, we observed that the shape and morphology of nanoparticles unambiguously determined their nuclear uptake pathways. Nuclear uptake is determined by an intricate interplay between physicochemical particle properties and nucleus properties. Our work provides a systematic understanding for nuclear uptake of nanoparticles, viruses, and bacteria and opens up a controllable design strategy for manipulating nanoparticle-nucleus interaction, with numerous applications in medicine, bioimaging, and biosensing.

Prediction of peak forces for a shortening smooth muscle tissue subjected to vibration.

The objective of the present study is to investigate the peak forces for a tracheal smooth muscle tissue subjected to an applied longitudinal vibration following isotonic shortening. A non-linear finite element analysis was carried out to simulate the vibratory response under experimental conditions that corresponds to forced length oscillations at 33 Hz for 1 second. The stiffness change and hysteresis estimated from the experimental data was used in the analysis. The finite element results of peak forces are compared to the experimental data obtained. The comparison of results indicate that the approach and the vibratory response obtained may be useful for describing the cross-bridge de-attachments within the cells as well as connective tissue connections characteristic of tracheal smooth muscle tissue.

Simulation of Healing Threshold in Strain-Induced Inflammation Through a Discrete Informatics Model.

Respiratory diseases such as asthma and acute respiratory distress syndrome as well as acute lung injury involve inflammation at the cellular level. The inflammation process is very complex and is characterized by the emergence of cytokines along with other changes in cellular processes. Due to the complexity of the various constituents that makes up the inflammation dynamics, it is necessary to develop models that can complement experiments to fully understand inflammatory diseases. In this study, we developed a discrete informatics model based on cellular automata (CA) approach to investigate the influence of elastic field (stretch/strain) on the dynamics of inflammation and account for probabilistic adaptation based on statistical interpretation of existing experimental data. Our simulation model investigated the effects of low, medium, and high strain conditions on inflammation dynamics. Results suggest that the model is able to indicate the threshold of innate healing of tissue as a response to strain experienced by the tissue. When strain is under the threshold, the tissue is still capable of adapting its structure to heal the damaged part. However, there exists a strain threshold where healing capability breaks down. The results obtained demonstrate that the developed discrete informatics based CA model is capable of modeling and giving insights into inflammation dynamics parameters under various mechanical strain/stretch environments.

Evaluation of Ventilation-Induced Lung Inflammation Through Multi-Scale Simulations.

Ventilation-induced lung injury is a common problem faced by patients with respiratory problems who require mechanical ventilation (MV). This injury may lead to a greater chance of developing or exacerbating the acute respiratory distress syndrome which further complicates the therapeutic use of MV. The chain of events begins with the MV initiating an immune response that leads to inflammation induced tissue material alteration (stiffening) and eventually the loss of lung resistance. It is clear from this sequence of events that the phenomenon of ventilation induced injury is multi-scale by nature and, hence, requires holistic analysis involving simulations and informatics. An effective approach to this problem is to break it down into several major physical models. Each physical model is developed separately and can be seen as a component in a larger system that comprises the scale of the problem being investigated. In this paper, a multi-scale system consisting of breathing mechanics, tissue deformation, and cellular mechanics models is developed to assess the immune response. To demonstrate the potential of the model, a fluid-solid model is employed for breathing mechanics, a plane-strain elasticity model is applied to assess tissue deformation, and a cellular automata (CA) model is developed to account for immune response. A case study of three lower airways is presented. The CA model shows that this increased the immune response by five times, which correlates with alteration in the tissue microstructure. This alteration in turn is reflected in the material constant value obtained in the tissue mechanics model. However, the changes in strain rates in the airways after inflammation (and hence, lung compliance) were not as significant as the rates of change in immune response. Finally, results from the fluid-solid model demonstrate its potential for airflow characterization caused by tissue deformation that could lead to disease identification.

Lesion Volume Estimation from TBI-MRI.

Traumatic brain injury (TBI) is a major problem affecting millions of people around the world every year. Usually, TBI results from any direct or indirect physical impact, sudden jerks, or blunt impacts to the head, leading to damage to the brain. Current research in TBI is focused on analyzing the biological and behavioral states of patients prone to such injuries. This paper presents a technique applied on MRI images in estimation of lesion volumes in brain tissues of traumatic brain-injured laboratory rats that were subjected to controlled cortical impacts. The lesion region in the brain tissue is estimated using segmentation of the brain, diffusion, and the damage regions. After the segmentation, the area of the damaged portion is estimated across each slice of MRI and the combined volume of damage is estimated through 3D reconstruction.

Modeling and simulation of biological self-assembly structures from nanoscale entities.

Many natural and biological systems are formed by the process of molecular self-assembly. Molecular self-assembly is defined as the spontaneous organization of molecules under thermodynamic equilibrium conditions into structurally well defined and rather stable arrangements. In this paper, we developed a novel computational methodology to investigate the self-assembly process of simple 1-D structures representing protein monomers into long filaments, rings, pyramids, bundles, etc. Based on the preliminary results obtained, the methodology was extended to mimic the microtubule self-assembly, which occurs in all eukaryotic cells.

Bio-Inspired Multi-Functional Drug Transport Design Concept and Simulations.

In this study, we developed a microdevice concept for drug/fluidic transport taking an inspiration from supramolecular motor found in biological cells. Specifically, idealized multi-functional design geometry (nozzle/diffuser/nozzle) was developed for (i) fluidic/particle transport; (ii) particle separation; and (iii) droplet generation. Several design simulations were conducted to demonstrate the working principles of the multi-functional device. The design simulations illustrate that the proposed design concept is feasible for multi-functionality. However, further experimentation and optimization studies are needed to fully evaluate the multifunctional device concept for multiple applications.