Computational models for multi-scale coupled dynamic problems

2004 ◽  
Vol 20 (3) ◽  
pp. 453-464 ◽  
Author(s):  
R.V.N. Melnik ◽  
A.H. Roberts
Keyword(s):  
Computational Models ◽  
Dynamic Problems ◽  
Multi Scale

scholarly journals Multi-scale dynamic mean field model (MDMF) relates resting-state brain dynamics with local cortical excitatory–inhibitory neurotransmitter homeostasis

2021 ◽  
pp. 1-55
Author(s):  
Amit Naskar ◽  
Anirudh Vattikonda ◽  
Gustavo Deco ◽  
Dipanjan Roy ◽  
Arpan Banerjee
Keyword(s):  
Resting State ◽  
Computational Models ◽  
Field Model ◽  
Diffusion Tensor ◽  
Brain Activity ◽  
Mean Field ◽  
Clustering Coefficient ◽  
Brain Dynamics ◽  
Mean Field Model ◽  
Multi Scale

Abstract Previous computational models have related spontaneous resting-state brain activity with local excitatory−inhibitory balance in neuronal populations. However, how underlying neurotransmitter kinetics associated with E-I balance governs resting state spontaneous brain dynamics remains unknown. Understanding the mechanisms by virtue of which fluctuations in neurotransmitter concentrations, a hallmark of a variety of clinical conditions relate to functional brain activity is of critical importance. We propose a multi-scale dynamic mean field model (MDMF) – a system of coupled differential equations for capturing the synaptic gating dynamics in excitatory and inhibitory neural populations as a function of neurotransmitter kinetics. Individual brain regions are modelled as population of MDMF and are connected by realistic connection topologies estimated from Diffusion Tensor Imaging data. First, MDMF successfully predicts resting-state functionalconnectivity. Second, our results show that optimal range of glutamate and GABA neurotransmitter concentrations subserve as the dynamic working point of the brain, that is, the state of heightened metastability observed in empirical blood-oxygen-level dependent signals. Third, for predictive validity the network measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) from existing healthy and pathological brain network studies could be captured by simulated functional connectivity from MDMF model.

scholarly journals Erratum: Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models

2014 ◽  
Vol 6 (4) ◽  
pp. 344-344
Author(s):  
Denise E. Kirschner ◽  
C. Anthony Hunt ◽  
Simeone Marino ◽  
Mohammad Fallahi-Sichani ◽  
Jennifer J. Linderman
Keyword(s):  
Systems Biology ◽  
Computational Models ◽  
Multi Scale

scholarly journals Comparison Between Human and Rodent Neurons for Persistent Activity Performance: A Biologically Plausible Computational Investigation

2021 ◽  
Vol 15 ◽  
Author(s):  
Qian Zhang ◽  
Yi Zeng ◽  
Tielin Zhang ◽  
Taoyi Yang
Keyword(s):  
Human Brain ◽  
Computational Models ◽  
Brain Cell ◽  
Membrane Properties ◽  
Accurate Information ◽  
Computational Investigation ◽  
Multi Scale ◽  
Neocortical Neurons ◽  
Activity Performance ◽  
Shed Light

Elucidating the multi-scale detailed differences between the human brain and other brains will help shed light on what makes us unique as a species. Computational models help link biochemical and anatomical properties to cognitive functions and predict key properties of the cortex. Here, we present a detailed human neocortex network, with all human neuron parameters derived from the newest Allen Brain human brain cell database. Compared with that of rodents, the human neural network maintains more complete and accurate information under the same graphic input. Unique membrane properties in human neocortical neurons enhance the human brain’s capacity for signal processing.

scholarly journals The Innate Immune Response to Invasive Pulmonary Aspergillosis: A Systems Modeling Approach

2021 ◽  
Author(s):  
Henrique AL Ribeiro ◽  
Luis Sordo Vieira ◽  
Yogesh Scindia ◽  
Bandita Adhikari ◽  
Matthew Wheeler ◽  
...  
Keyword(s):  
Immune Response ◽  
Time Course ◽  
Computational Models ◽  
Invasive Pulmonary Aspergillosis ◽  
Extensive Literature ◽  
Validation Data ◽  
Fungal Virulence ◽  
Multi Scale ◽  
Wide Range ◽  
Anti Fungal

Invasive aspergillosis is a fungal respiratory infection that poses an increasingly serious health risk with the rise in the number of immunocompromised patients and the emergence of fungal strains resistant to first-line anti-fungal drugs. Consequently, there is a pressing need for host-centric therapeutics for this infection, which motivated the work presented in this paper. Given the multi-scale nature of the immune response, computational models are a key technology for capturing the dynamics of the battle between the pathogen and the immune system. We describe such a multi-scale computational model, focused on the mechanisms for iron regulation, a key element for fungal virulence in the pathogen Aspergillus fumigatus. A key feature of the model is that its parameters have been derived from an extensive literature search rather than data fitting. The model is shown to reproduce a wide range of published time course data, as well as custom validation data generated for this purpose. It also accurately reproduces many qualitative features of the initial course of infection.

scholarly journals Uncertainty quantification patterns for multiscale models

2021 ◽  
Vol 379 (2197) ◽  
Author(s):  
D. Ye ◽  
L. Veen ◽  
A. Nikishova ◽  
J. Lakhlili ◽  
W. Edeling ◽  
...  
Keyword(s):  
Decision Making ◽  
Uncertainty Quantification ◽  
Computational Models ◽  
Building Blocks ◽  
Computational Science ◽  
Proof Of Concept ◽  
Multiscale Models ◽  
Theme Issue ◽  
Multi Scale ◽  
Speed Up

Uncertainty quantification (UQ) is a key component when using computational models that involve uncertainties, e.g. in decision-making scenarios. In this work, we present uncertainty quantification patterns (UQPs) that are designed to support the analysis of uncertainty in coupled multi-scale and multi-domain applications. UQPs provide the basic building blocks to create tailored UQ for multiscale models. The UQPs are implemented as generic templates, which can then be customized and aggregated to create a dedicated UQ procedure for multiscale applications. We present the implementation of the UQPs with multiscale coupling toolkit Multiscale Coupling Library and Environment 3. Potential speed-up for UQPs has been derived as well. As a proof of concept, two examples of multiscale applications using UQPs are presented. This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico ’.

Multi-scale finite element modeling of 3D printed structures subjected to mechanical loads

2018 ◽  
Vol 24 (1) ◽  
pp. 177-187 ◽  
Author(s):  
Dalia Calneryte ◽  
Rimantas Barauskas ◽  
Daiva Milasiene ◽  
Rytis Maskeliunas ◽  
Audrius Neciunas ◽  
...  
Keyword(s):  
Finite Element ◽  
Composite Structures ◽  
Computational Models ◽  
Three Dimensional ◽  
Structural Response ◽  
Content Type ◽  
Multi Scale ◽  
Macro Scale ◽  
Internal Constitution ◽  
3D Printed

Purpose The purpose of this paper is to investigate the influence of geometrical microstructure of items obtained by applying a three-dimensional (3D) printing technology on their mechanical strength. Design/methodology/approach Three-dimensional printed items (3DPI) are composite structures of complex internal constitution. The buildup of the finite element (FE) computational models of 3DPI is based on a multi-scale approach. At the micro-scale, the FE models of representative volume elements corresponding to different additive layer heights and different thicknesses of extruded fibers are investigated to obtain the equivalent non-linear nominal stress–strain curves. The obtained results are used for the creation of macro-scale FE models, which enable to simulate the overall structural response of 3D printed samples subjected to tensile and bending loads. Findings The validation of the models was performed by comparing the computed results against the experimental ones, where satisfactory agreement has been demonstrated within a marked range of thicknesses of additive layers. Certain inadequacies between computed against experimental results were observed in cases of thinnest and thickest additive layers. The principle explanation of the reasons of inadequacies takes into account the poorer quality of mutual adhesion in case of very thin extruded fibers and too-early solidification effect. Originality/value Flexural and tensile experiments are simulated by FE models that are created with consideration to microstructure of 3D printed samples.

scholarly journals Multi-scale computational models of familial hypertrophic cardiomyopathy: genotype to phenotype

2011 ◽  
Vol 8 (64) ◽  
pp. 1550-1561 ◽  
Author(s):  
Stuart G. Campbell ◽  
Andrew D. McCulloch
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Computational Models ◽  
Heart Function ◽  
Myocardial Strain ◽  
Cardiac Cells ◽  
Familial Hypertrophic Cardiomyopathy ◽  
Mutant Proteins ◽  
Multi Scale ◽  
Genes Encoding ◽  
Whole Heart

Familial hypertrophic cardiomyopathy (FHC) is an inherited disorder affecting roughly one in 500 people. Its hallmark is abnormal thickening of the ventricular wall, leading to serious complications that include heart failure and sudden cardiac death. Treatment is complicated by variation in the severity, symptoms and risks for sudden death within the patient population. Nearly all of the genetic lesions associated with FHC occur in genes encoding sarcomeric proteins, indicating that defects in cardiac muscle contraction underlie the condition. Detailed biophysical data are increasingly available for computational analyses that could be used to predict heart phenotypes based on genotype. These models must integrate the dynamic processes occurring in cardiac cells with properties of myocardial tissue, heart geometry and haemodynamic load in order to predict strain and stress in the ventricular walls and overall pump function. Recent advances have increased the biophysical detail in these models at the myofilament level, which will allow properties of FHC-linked mutant proteins to be accurately represented in simulations of whole heart function. The short-term impact of these models will be detailed descriptions of contractile dysfunction and altered myocardial strain patterns at the earliest stages of the disease—predictions that could be validated in genetically modified animals. Long term, these multi-scale models have the potential to improve clinical management of FHC through genotype-based risk stratification and personalized therapy.

scholarly journals Biomechanics of Additively Manufactured Metallic Scaffolds—A Review

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 6833
Author(s):  
Karim Elhattab ◽  
Mohamed Samir Hefzy ◽  
Zachary Hanf ◽  
Bailey Crosby ◽  
Alexander Enders ◽  
...  
Keyword(s):  
Computational Models ◽  
Industrial Revolution ◽  
Review Paper ◽  
Multi Scale ◽  
Cell Shapes ◽  
And Performance ◽  
Titanium Alloy Ti6al4v ◽  
Processing Properties

This review paper is related to the biomechanics of additively manufactured (AM) metallic scaffolds, in particular titanium alloy Ti6Al4V scaffolds. This is because Ti6Al4V has been identified as an ideal candidate for AM metallic scaffolds. The factors that affect the scaffold technology are the design, the material used to build the scaffold, and the fabrication process. This review paper includes thus a discussion on the design of Ti6A4V scaffolds in relation to how their behavior is affected by their cell shapes and porosities. This is followed by a discussion on the post treatment and mechanical characterization including in-vitro and in-vivo biomechanical studies. A review and discussion are also presented on the ongoing efforts to develop predictive tools to derive the relationships between structure, processing, properties and performance of powder-bed additive manufacturing of metals. This is a challenge when developing process computational models because the problem involves multi-physics and is of multi-scale in nature. Advantages, limitations, and future trends in AM scaffolds are finally discussed. AM is considered at the forefront of Industry 4.0, the fourth industrial revolution. The market of scaffold technology will continue to boom because of the high demand for human tissue repair.

scholarly journals Bridging Hierarchies in Multi-Scale Models of Neural Systems: Look-Up Tables Enable Computationally Efficient Simulations of Non-linear Synaptic Dynamics

2021 ◽  
Vol 15 ◽  
Author(s):  
Duy-Tan J. Pham ◽  
Gene J. Yu ◽  
Jean-Marie C. Bouteiller ◽  
Theodore W. Berger
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Computational Cost ◽  
Synaptic Activity ◽  
Emergent Properties ◽  
Linear Dynamics ◽  
Multi Scale ◽  
Scale Models ◽  
Non Linear ◽  
Synaptic Dynamics

Synapses are critical actors of neuronal transmission as they form the basis of chemical communication between neurons. Accurate computational models of synaptic dynamics may prove important in elucidating emergent properties across hierarchical scales. Yet, in large-scale neuronal network simulations, synapses are often modeled as highly simplified linear exponential functions due to their small computational footprint. However, these models cannot capture the complex non-linear dynamics that biological synapses exhibit and thus, are insufficient in representing synaptic behavior accurately. Existing detailed mechanistic synapse models can replicate these non-linear dynamics by modeling the underlying kinetics of biological synapses, but their high complexity prevents them from being a suitable option in large-scale models due to long simulation times. This motivates the development of more parsimonious models that can capture the complex non-linear dynamics of synapses accurately while maintaining a minimal computational cost. We propose a look-up table approach that stores precomputed values thereby circumventing most computations at runtime and enabling extremely fast simulations for glutamatergic receptors AMPAr and NMDAr. Our results demonstrate that this methodology is capable of replicating the dynamics of biological synapses as accurately as the mechanistic synapse models while offering up to a 56-fold increase in speed. This powerful approach allows for multi-scale neuronal networks to be simulated at large scales, enabling the investigation of how low-level synaptic activity may lead to changes in high-level phenomena, such as memory and learning.

Sign in / Sign up

Export Citation Format

Share Document