scholarly journals Fluidity: A fully unstructured anisotropic adaptive mesh computational modeling framework for geodynamics

2011 ◽  
Vol 12 (6) ◽  
pp. n/a-n/a ◽  
Author(s):  
D. Rhodri Davies ◽  
Cian R. Wilson ◽  
Stephan C. Kramer
Keyword(s):  
Computational Modeling ◽  
Adaptive Mesh ◽  
Modeling Framework

scholarly journals A practical guide to mechanistic systems modeling in biology using a logic-based approach

2020 ◽  
Author(s):  
Anna Niarakis ◽  
Tomáš Helikar
Keyword(s):  
Computational Modeling ◽  
Computational Models ◽  
Regulatory System ◽  
Mechanistic Modeling ◽  
Lac Operon ◽  
Modeling Framework ◽  
Validation Criteria ◽  
Hands On ◽  
Modeling Software ◽  
Logical Modeling

Abstract Mechanistic computational models enable the study of regulatory mechanisms implicated in various biological processes. These models provide a means to analyze the dynamics of the systems they describe, and to study and interrogate their properties, and provide insights about the emerging behavior of the system in the presence of single or combined perturbations. Aimed at those who are new to computational modeling, we present here a practical hands-on protocol breaking down the process of mechanistic modeling of biological systems in a succession of precise steps. The protocol provides a framework that includes defining the model scope, choosing validation criteria, selecting the appropriate modeling approach, constructing a model and simulating the model. To ensure broad accessibility of the protocol, we use a logical modeling framework, which presents a lower mathematical barrier of entry, and two easy-to-use and popular modeling software tools: Cell Collective and GINsim. The complete modeling workflow is applied to a well-studied and familiar biological process—the lac operon regulatory system. The protocol can be completed by users with little to no prior computational modeling experience approximately within 3 h.

scholarly journals A PEER REVIEWED ONLINE COMPUTATIONAL MODELING FRAMEWORK

2011 ◽  
Author(s):  
Pieter J. Mosterman ◽  
Don Bouldin ◽  
Andrzej Rucinski
Keyword(s):  
Intellectual Property ◽  
Computational Modeling ◽  
Scientific Discovery ◽  
Intrinsic Value ◽  
Computational Simulation ◽  
Modeling Framework ◽  
Quality Requirements ◽  
The Third ◽  
Model Based

Along with theory and experimentation, computational simulation has become the third pillar of scientific discovery. While in industry computational modeling has seen application at an enterprise-wide level in the context of Model-Based Design, in academia models are typically still limited to isolated use by specialists. Once a project is completed, the intellectual property embodied by the model is lost. To harness the effort spent, a networked repository is proposed that stores peer-reviewed models. These models are evaluated whether they adhere to a set of quality requirements so they capture intrinsic value. This would facilitate the type of multi-disciplinary collaboration that is required to engineer the systems that have emerged and that continue to gain in importance. This work puts forward an outline of such a peer-reviewed online repository.

Computational Modeling of the Mechanical Performance of a Magnesium Stent Undergoing Uniform and Pitting Corrosion in a Remodeling Artery

2017 ◽  
Vol 11 (2) ◽  
Author(s):  
Enda L. Boland ◽  
James A. Grogan ◽  
Peter E. McHugh
Keyword(s):  
Magnesium Alloy ◽  
Computational Modeling ◽  
Pitting Corrosion ◽  
Mechanical Performance ◽  
Formation Rate ◽  
The Body ◽  
Neointimal Formation ◽  
Modeling Framework ◽  
Element Analysis ◽  
Biodegradable Stents

Coronary stents made from degradable biomaterials such as magnesium alloy are an emerging technology in the treatment of coronary artery disease. Biodegradable stents provide mechanical support to the artery during the initial scaffolding period after which the artery will have remodeled. The subsequent resorption of the stent biomaterial by the body has potential to reduce the risk associated with long-term placement of these devices, such as in-stent restenosis, late stent thrombosis, and fatigue fracture. Computational modeling such as finite-element analysis has proven to be an extremely useful tool in the continued design and development of these medical devices. What is lacking in computational modeling literature is the representation of the active response of the arterial tissue in the weeks and months following stent implantation, i.e., neointimal remodeling. The phenomenon of neointimal remodeling is particularly interesting and significant in the case of biodegradable stents, when both stent degradation and neointimal remodeling can occur simultaneously, presenting the possibility of a mechanical interaction and transfer of load between the degrading stent and the remodeling artery. In this paper, a computational modeling framework is developed that combines magnesium alloy degradation and neointimal remodeling, which is capable of simulating both uniform (best case) and localized pitting (realistic) stent corrosion in a remodeling artery. The framework is used to evaluate the effects of the neointima on the mechanics of the stent, when the stent is undergoing uniform or pitting corrosion, and to assess the effects of the neointimal formation rate relative to the overall stent degradation rate (for both uniform and pitting conditions).

scholarly journals Personalized Computational Modeling Identifies Embolic Stroke of Undetermined Source Patients with Potential Arrhythmic Substrate

2020 ◽  
Author(s):  
Savannah F. Bifulco ◽  
Griffin D. Scott ◽  
Sakher Sarairah ◽  
Zeinab Birjandian ◽  
Caroline H. Roney ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Computational Modeling ◽  
Left Atrial ◽  
Embolic Stroke ◽  
Modeling Framework

AbstractLate-gadolinium enhanced (LGE)-MRI has revealed atrial fibrotic remodeling in embolic stroke of undetermined source (ESUS) patients comparable to that observed in atrial fibrillation (AFib) patients. Here, we use computational modeling to understand why fibrosis in ESUS does not cause arrhythmia. Left atrial (LA) models were reconstructed via a standardized process and simulations were conducted to probe the fibrotic substrate’s capacity to sustain reentrant drivers (RD). RD-perpetuated arrhythmia was observed in 23/45 (51%) ESUS and 28/45 (62%) AFib models. LA models in which RDs were inducible had significantly more fibrosis than those which were non-inducible (16.8±5.04% vs. 10.19±3.14%; P<0.0001); however, between the specific subsets of inducible ESUS and AFib models, there was no difference in fibrosis burden (P=0.068). Thus, within our modeling framework, pro-arrhythmic properties of fibrosis in ESUS and AFib are indistinguishable, suggesting many ESUS patients have latent fibrotic substrate that may be a potential future source of arrhythmogenicity.

scholarly journals From infection to immunity: understanding the response to SARS-CoV2 through in-silico modeling

2020 ◽  
Author(s):  
Filippo Castiglione ◽  
Debashrito Deb ◽  
Anurag P. Srivastava ◽  
Pietro Liò ◽  
Arcangelo Liso
Keyword(s):  
Immune Response ◽  
Viral Load ◽  
Computational Modeling ◽  
In Silico ◽  
Vaccine Development ◽  
Real Life ◽  
Humoral Response ◽  
Immune Competence ◽  
Modeling Framework ◽  
The Impact

AbstractBackgroundImmune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies.AimThe dynamics of the insurgence of immunity is at the core of the both SARS-CoV-2 vaccine development and therapies. This paper addresses a fundamental question in the management of the infection: can we describe the insurgence (and the span) of immunity in COVID-19? The in-silico model developed here answers this question at individual (personalized) and population levels.We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor and age in the artificially infected population on the course of the disease.MethodsWe use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degree of immune competence. We use a parameter setting to reproduce known inter-patient variability and general epidemiological statistics.ResultsWe reproduce in-silico a number of clinical observations and we identify critical factors in the statistical evolution of the infection. In particular we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection is a prognostic factor for determining the clinical outcome of the infection.Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of simulating the immune response at individual and population levels. The model developed is able to explain and interpret observed patterns of infection and makes verifiable temporal predictions.Within the limitations imposed by the simulated environment, this work proposes in a quantitative way that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population.In this work we i) show the power of model predictions, ii) identify the clinical end points that could be more suitable for computational modeling of COVID-19 immune response, iii) define the resolution and amount of data required to empower this class of models for translational medicine purposes and, iv) we exemplify how computational modeling of immune response provides an important light to discuss hypothesis and design new experiments.

scholarly journals A Sight on Single-Cell Transcriptomics in Plants Through the Prism of Cell-Based Computational Modeling Approaches: Benefits and Challenges for Data Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Aleksandr Bobrovskikh ◽  
Alexey Doroshkov ◽  
Stefano Mazzoleni ◽  
Fabrizio Cartenì ◽  
Francesco Giannino ◽  
...  
Keyword(s):  
Data Analysis ◽  
Computational Modeling ◽  
Single Cell ◽  
Imaging Techniques ◽  
Plant Tissues ◽  
Modeling Framework ◽  
Transcriptomic Data ◽  
Plant Morphogenesis ◽  
Modeling Approaches ◽  
Resolution Imaging

Single-cell technology is a relatively new and promising way to obtain high-resolution transcriptomic data mostly used for animals during the last decade. However, several scientific groups developed and applied the protocols for some plant tissues. Together with deeply-developed cell-resolution imaging techniques, this achievement opens up new horizons for studying the complex mechanisms of plant tissue architecture formation. While the opportunities for integrating data from transcriptomic to morphogenetic levels in a unified system still present several difficulties, plant tissues have some additional peculiarities. One of the plants’ features is that cell-to-cell communication topology through plasmodesmata forms during tissue growth and morphogenesis and results in mutual regulation of expression between neighboring cells affecting internal processes and cell domain development. Undoubtedly, we must take this fact into account when analyzing single-cell transcriptomic data. Cell-based computational modeling approaches successfully used in plant morphogenesis studies promise to be an efficient way to summarize such novel multiscale data. The inverse problem’s solutions for these models computed on the real tissue templates can shed light on the restoration of individual cells’ spatial localization in the initial plant organ—one of the most ambiguous and challenging stages in single-cell transcriptomic data analysis. This review summarizes new opportunities for advanced plant morphogenesis models, which become possible thanks to single-cell transcriptome data. Besides, we show the prospects of microscopy and cell-resolution imaging techniques to solve several spatial problems in single-cell transcriptomic data analysis and enhance the hybrid modeling framework opportunities.

scholarly journals A computational model of MGUS progression to Multiple Myeloma identifies optimum screening strategies and their effects on mortality

2017 ◽  
Author(s):  
Philipp M. Altrock ◽  
Jeremy Ferlic ◽  
Tobias Galla ◽  
Michael H. Tomasson ◽  
Franziska Michor
Keyword(s):  
Multiple Myeloma ◽  
Computational Modeling ◽  
Risk Reduction ◽  
Disease Prevalence ◽  
Modeling Framework ◽  
Populations At Risk ◽  
Screening Strategies ◽  
Specific Mortality ◽  
Progression Risk

ABSTRACTRecent advances uncovered therapeutic interventions that might reduce the risk of progression of premalignant diagnoses, such as from Monoclonal Gammopathy of Undetermined Significance (MGUS) to multiple myeloma (MM). It remains unclear how to best screen populations at risk and how to evaluate the ability of these interventions to reduce disease prevalence and mortality at the population level. To address these questions, we developed a computational modeling framework. We used individual-based computational modeling of MGUS incidence and progression across a population of diverse individuals, to determine best screening strategies in terms of screening start, intervals, and risk-group specificity. Inputs were life tables, MGUS incidence and baseline MM survival. We measured MM-specific mortality and MM prevalence following MGUS detection from simulations and mathematical precition modeling. We showed that our framework is applicable to a wide spectrum of screening and intervention scenarios, including variation of the baseline MGUS to MM progression rate and evolving MGUS, in which progression increases over time. Given the currently available progression risk-point estimate of 61% risk, starting screening at age 55 and follow-up screening every 6yrs reduced total MM prevalence by 19%. The same reduction could be achieved with starting age 65 and follow-up every 2yrs. A 40% progression risk reduction per MGUS patient per year would reduce MM-specific mortality by 40%. Generally, age of screening onset and frequency impact disease prevalence, progression risk reduction impacts both prevalence and disease-specific mortality, and screeenign would generally be favorable in high-risk individuals. Screening efforts should focus on specifically identified groups of high lifetime risk of MGUS, for which screening benefits can be significant. Screening low-risk MGUS individuals would require improved preventions.

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