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Journal articles on the topic 'Mechanistic Computational Model'

Author: Grafiati

Published: 7 September 2023

Last updated: 31 July 2025

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1

Martina Perez, Simon, Heba Sailem, and Ruth E. Baker. "Efficient Bayesian inference for mechanistic modelling with high-throughput data." PLOS Computational Biology 18, no. 6 (2022): e1010191. http://dx.doi.org/10.1371/journal.pcbi.1010191.

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Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. Moreover, while high-throughput data presents opportunities to calibrate sophisticated models, comparing large amounts of data with model simulations quickly becomes computationally prohibitive. Inspired by the method of Stochastic Gradient Descent, we propose a minibatch approach to approximate Bayesian computation. Through a case study of a high-

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Hearle, J. W. S., and A. H. Wilkins. "Mechanistic modelling of pilling. Part II: Individual-fibre computational model." Journal of the Textile Institute 97, no. 4 (2006): 369–76. http://dx.doi.org/10.1533/joti.2005.0164.

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Zabihi, Azam, John Tello, Sebastien Incerti, et al. "Determination of fast neutron RBE using a fully mechanistic computational model." Applied Radiation and Isotopes 156 (February 2020): 108952. http://dx.doi.org/10.1016/j.apradiso.2019.108952.

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Das, Shubhajit, and Swapan K. Pati. "Unravelling the mechanism of tin-based frustrated Lewis pair catalysed hydrogenation of carbonyl compounds." Catalysis Science & Technology 8, no. 20 (2018): 5178–89. http://dx.doi.org/10.1039/c8cy01227j.

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Pham, Duy-Tan J., and Jean-Marie C. Bouteiller. "Experimentally Constrained Mechanistic and Data-Driven Models for Simulating NMDA Receptor Dynamics." Biomedicines 13, no. 7 (2025): 1674. https://doi.org/10.3390/biomedicines13071674.

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Background: The N-methyl-d-aspartate receptor (NMDA-R) is a glutamate ionotropic receptor in the brain that is crucial for synaptic plasticity, which underlies learning and memory formation. Dysfunction of NMDA receptors is implicated in various neurological diseases due to their roles in both normal cognition and excitotoxicity. However, their dynamics are challenging to capture accurately due to their high complexity and non-linear behavior. Methods: This article presents the elaboration and calibration of experimentally constrained computational models of GluN1/GluN2A NMDA-R dynamics: (1) a

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Kraikivski, Pavel. "A Dynamic Mechanistic Model of Perceptual Binding." Mathematics 10, no. 7 (2022): 1135. http://dx.doi.org/10.3390/math10071135.

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The brain’s ability to create a unified conscious representation of an object by integrating information from multiple perception pathways is called perceptual binding. Binding is crucial for normal cognitive function. Some perceptual binding errors and disorders have been linked to certain neurological conditions, brain lesions, and conditions that give rise to illusory conjunctions. However, the mechanism of perceptual binding remains elusive. Here, I present a computational model of binding using two sets of coupled oscillatory processes that are assumed to occur in response to two differen

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Zhao, Chen, Adam C. Mirando, Richard J. Sové, Thalyta X. Medeiros, Brian H. Annex, and Aleksander S. Popel. "A mechanistic integrative computational model of macrophage polarization: Implications in human pathophysiology." PLOS Computational Biology 15, no. 11 (2019): e1007468. http://dx.doi.org/10.1371/journal.pcbi.1007468.

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Soroka, Gustavo, Marco Idiart, and Aline Villavicencio. "Mechanistic role of alpha oscillations in a computational model of working memory." PLOS ONE 19, no. 2 (2024): e0296217. http://dx.doi.org/10.1371/journal.pone.0296217.

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Brain oscillations are believed to be involved in the different operations necessary to manipulate information during working memory tasks. We propose a mechanistic role for the observed inhibition effect of the alpha rhythm based on its interference with the theta rhythm. Using the Lisman-Idiart model for multi-item working memory, we show that the interaction between these two oscillations is capable of creating a long lasting destructive interference that prevents the cyclic reactivation of neuronal ensembles and, as a consequence, memory maintenance. Additionally, to ensure robustness we p

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Ramatsoma, Mafeni S., and Evans M. N. Chirwa. "Computational simulation of flocculent sedimentation based on experimental results." Water Science and Technology 65, no. 6 (2012): 1007–13. http://dx.doi.org/10.2166/wst.2012.923.

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Computerised interpolation algorithms as well as the empirical model for analysing the flocculent settling data were developed. A mechanistic semi-empirical model developed from fundamental physical principles of a falling particle in a viscous fluid was tested against actual flocculation column data. The accuracy of the mechanistic model was evaluated using the sum of the squared errors between the interpolated values (real values) and the model predictions. Its fitting capabilities were compared with Özer's model using nine flocculent data sets of which four were obtained from literature and

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Darmon, David. "Discrete Information Dynamics with Confidence via the Computational Mechanics Bootstrap: Confidence Sets and Significance Tests for Information-Dynamic Measures." Entropy 22, no. 7 (2020): 782. http://dx.doi.org/10.3390/e22070782.

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Information dynamics and computational mechanics provide a suite of measures for assessing the information- and computation-theoretic properties of complex systems in the absence of mechanistic models. However, both approaches lack a core set of inferential tools needed to make them more broadly useful for analyzing real-world systems, namely reliable methods for constructing confidence sets and hypothesis tests for their underlying measures. We develop the computational mechanics bootstrap, a bootstrap method for constructing confidence sets and significance tests for information-dynamic meas

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Sharifi, Soroosh, and Arash Massoudieh. "A novel hybrid mechanistic-data-driven model identification framework using NSGA-II." Journal of Hydroinformatics 14, no. 3 (2012): 697–715. http://dx.doi.org/10.2166/hydro.2012.026.

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This paper describes a novel evolutionary data-driven model (DDM) identification framework using the NSGA-II multi-objective genetic algorithm. The central concept of this paper is the employment of evolutionary computation to search for model structures among a catalog of models, while honoring the physical principles and the constitutive theories commonly used to represent the system/processes being modeled. The presented framework provides high computational efficiency through connecting a series of NSGA-II runs which share results. Furthermore, the employment of a multi-objective optimizat

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Peterson, D. E., R. V. Lalla, R. Srivastava, and L. M. Loew. "Mucositis in cancer patients: Prototypic semi-mechanistic kinetic model." Journal of Clinical Oncology 25, no. 18_suppl (2007): 19617. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.19617.

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19617 Background: Recent research advances have helped (i) define pathobiology of alimentary tract mucosal injury secondary to cancer therapy and (ii) link molecular mechanisms with clinically important outcomes. Recently-developed computational biology modeling may further enhance these advances. Semi-mechanistic (SM) modeling allows one to approach quantitative analysis of a biochemical system that is incompletely determined. In this study, data from sequential oral mucosal biopsies in 3 patients developing oral mucositis secondary to hematopoietic stem cell transplantation (HSCT) conditioni

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Wright, A. Armean, Ghassan N. Fayad, James F. Selgrade, and Mette S. Olufsen. "Mechanistic model of hormonal contraception." PLOS Computational Biology 16, no. 6 (2020): e1007848. http://dx.doi.org/10.1371/journal.pcbi.1007848.

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Rożeń, Antoni. "A Mechanistic Model of a Passive Autocatalytic Hydrogen Recombiner." Chemical and Process Engineering 36, no. 1 (2015): 3–19. http://dx.doi.org/10.1515/cpe-2015-0001.

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Abstract : A passive autocatalytic hydrogen recombiner (PAR) is a self-starting device, without operator action or external power input, installed in nuclear power plants to remove hydrogen from the containment building of a nuclear reactor. A new mechanistic model of PAR has been presented and validated by experimental data and results of Computational Fluid Dynamics (CFD) simulations. The model allows to quickly and accurately predict gas temperature and composition, catalyst temperature and hydrogen recombination rate. It is assumed in the model that an exothermic recombination reaction of

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Mai, Tam V. T., and Lam K. Huynh. "Comment on “Atmospheric chemistry of oxazole: the mechanism and kinetic studies on oxidation reaction initiated by OH radicals” by A. Shiroudi, M. A. Abdel-Rahman, A. M. El-Nahas and M. Altarawneh, New J. Chem., 2021, 45, 2237." New Journal of Chemistry 45, no. 30 (2021): 13644–48. http://dx.doi.org/10.1039/d1nj01020d.

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The large kinetic discrepancy between computational and experimental studies is resolved using the rigorous stochastic RRKM-based master-equation rate model. Detailed mechanistic insights are also revealed to advance its related applications.

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Zheng, Sainan, Shiwei Xu, Jinghong Zhou, et al. "Insight into the Claisen condensation of methyl acetate and dimethyl carbonate to dimethyl malonate." New Journal of Chemistry 42, no. 9 (2018): 6689–94. http://dx.doi.org/10.1039/c7nj04958g.

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A mechanistic model for the Claisen condensation of methyl acetate and dimethyl carbonate in the presence of sodium methoxide to sodium malonate and further protonation to dimethyl malonate is proposed based on experimental and computational results.

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Mast, Yannic, Moritz Wild, and Ralf Takors. "Optimizing Mass Transfer in Multiphase Fermentation: The Role of Drag Models and Physical Conditions." Processes 12, no. 1 (2023): 45. http://dx.doi.org/10.3390/pr12010045.

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Detailed knowledge of the flow characteristics, bubble movement, and mass transfer is a prerequisite for the proper design of multiphase bioreactors. Often, mechanistic spatiotemporal models and computational fluid dynamics, which intrinsically require computationally demanding analysis of local interfacial forces, are applied. Typically, such approaches use volumetric mass-transfer coefficient (kLa) models, which have demonstrated their predictive power in water systems. However, are the related results transferrable to multiphase fermentations with different physicochemical properties? This

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Calmus, Ryan, Benjamin Wilson, Yukiko Kikuchi, and Christopher I. Petkov. "Structured sequence processing and combinatorial binding: neurobiologically and computationally informed hypotheses." Philosophical Transactions of the Royal Society B: Biological Sciences 375, no. 1791 (2019): 20190304. http://dx.doi.org/10.1098/rstb.2019.0304.

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Understanding how the brain forms representations of structured information distributed in time is a challenging endeavour for the neuroscientific community, requiring computationally and neurobiologically informed approaches. The neural mechanisms for segmenting continuous streams of sensory input and establishing representations of dependencies remain largely unknown, as do the transformations and computations occurring between the brain regions involved in these aspects of sequence processing. We propose a blueprint for a neurobiologically informed and informing computational model of seque

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Zhao, Chen, Thalyta X. Medeiros, Richard J. Sové, Brian H. Annex, and Aleksander S. Popel. "A data-driven computational model enables integrative and mechanistic characterization of dynamic macrophage polarization." iScience 24, no. 2 (2021): 102112. http://dx.doi.org/10.1016/j.isci.2021.102112.

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Liorni, Ilaria, Esra Neufeld, Sven Kühn, et al. "Novel mechanistic model and computational approximation for electromagnetic safety evaluations of electrically short implants." Physics in Medicine & Biology 63, no. 22 (2018): 225015. http://dx.doi.org/10.1088/1361-6560/aae94c.

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Breen, Michael S., Daniel L. Villeneuve, Miyuki Breen, Gerald T. Ankley, and Rory B. Conolly. "Mechanistic Computational Model of Ovarian Steroidogenesis to Predict Biochemical Responses to Endocrine Active Compounds." Annals of Biomedical Engineering 35, no. 6 (2007): 970–81. http://dx.doi.org/10.1007/s10439-007-9309-7.

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22

Ghose, Debraj, and Daniel Lew. "Mechanistic insights into actin-driven polarity site movement in yeast." Molecular Biology of the Cell 31, no. 10 (2020): 1085–102. http://dx.doi.org/10.1091/mbc.e20-01-0040.

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Cells dynamically orient their direction of growth or movement by moving a polarity site that defines the front. A bottom-up computational model is used to explore the mechanism of movement. Assumptions inspired by findings in the yeast system show that vesicle traffic directed to the polarity site would suffice to produce realistic movement.

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23

O'Reilly, Randall C., and Michael J. Frank. "Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia." Neural Computation 18, no. 2 (2006): 283–328. http://dx.doi.org/10.1162/089976606775093909.

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The prefrontal cortex has long been thought to subserve both working memory (the holding of information online for processing) and executive functions (deciding how to manipulate working memory and perform processing). Although many computational models of working memory have been developed, the mechanistic basis of executive function remains elusive, often amounting to a homunculus. This article presents an attempt to deconstruct this homunculus through powerful learning mechanisms that allow a computational model of the prefrontal cortex to control both itself and other brain areas in a stra

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Sturniolo, Simone, William Waites, Tim Colbourn, David Manheim, and Jasmina Panovska-Griffiths. "Testing, tracing and isolation in compartmental models." PLOS Computational Biology 17, no. 3 (2021): e1008633. http://dx.doi.org/10.1371/journal.pcbi.1008633.

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Existing compartmental mathematical modelling methods for epidemics, such as SEIR models, cannot accurately represent effects of contact tracing. This makes them inappropriate for evaluating testing and contact tracing strategies to contain an outbreak. An alternative used in practice is the application of agent- or individual-based models (ABM). However ABMs are complex, less well-understood and much more computationally expensive. This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models.

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Saqr, Khalid M. "Computational fluid dynamics simulations of cerebral aneurysm using Newtonian, power-law and quasi-mechanistic blood viscosity models." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 234, no. 7 (2020): 711–19. http://dx.doi.org/10.1177/0954411920917531.

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Cerebral aneurysm is a fatal neurovascular disorder. Computational fluid dynamics simulation of aneurysm haemodynamics is one of the most important research tools which provide increasing potential for clinical applications. However, computational fluid dynamics modelling of such delicate neurovascular disorder involves physical complexities that cannot be easily simplified. Recently, it was shown that the Newtonian simplification used to close the shear stress tensor of the Navier–Stokes equation is not sufficient to explore aneurysm haemodynamics. This article explores the differences betwee

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Wessel, Remziye E., and Sepideh Dolatshahi. "Quantitative mechanistic model reveals key determinants of placental IgG transfer and informs prenatal immunization strategies." PLOS Computational Biology 19, no. 11 (2023): e1011109. http://dx.doi.org/10.1371/journal.pcbi.1011109.

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Transplacental antibody transfer is crucially important in shaping neonatal immunity. Recently, prenatal maternal immunization has been employed to boost pathogen-specific immunoglobulin G (IgG) transfer to the fetus. Multiple factors have been implicated in antibody transfer, but how these key dynamic regulators work together to elicit the observed selectivity is pertinent to engineering vaccines for mothers to optimally immunize their newborns. Here, we present the first quantitative mechanistic model to uncover the determinants of placental antibody transfer and inform personalized immuniza

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Ruiz, Patricia, Claude Emond, Eva D. McLanahan, Shivanjali Joshi-Barr, and Moiz Mumtaz. "Exploring Mechanistic Toxicity of Mixtures Using PBPK Modeling and Computational Systems Biology." Toxicological Sciences 174, no. 1 (2019): 38–50. http://dx.doi.org/10.1093/toxsci/kfz243.

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Abstract Mixtures risk assessment needs an efficient integration of in vivo, in vitro, and in silico data with epidemiology and human studies data. This involves several approaches, some in current use and others under development. This work extends the Agency for Toxic Substances and Disease Registry physiologically based pharmacokinetic (PBPK) toolkit, available for risk assessors, to include a mixture PBPK model of benzene, toluene, ethylbenzene, and xylenes. The recoded model was evaluated and applied to exposure scenarios to evaluate the validity of dose additivity for mixtures. In the se

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Gomez, Hector. "How heterogeneity drives tumour growth: a computational study." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2171 (2020): 20190244. http://dx.doi.org/10.1098/rsta.2019.0244.

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Although cancerous tumours usually originate from a single cell, they normally evolve into a remarkably heterogeneous agglomeration of cells. Heterogeneity is a pervasive and almost universal feature of tumours, but its origin and consequences remain poorly understood. Tumour heterogeneity has been usually associated with poor prognosis, but a better understanding of it may lead to more personalized diagnosis and therapy. Here, we study tumour heterogeneity developing a computational model in which different cell subpopulations compete for space. The model suggests that aggressive tumour subpo

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Masison, J., J. Beezley, Y. Mei, et al. "A modular computational framework for medical digital twins." Proceedings of the National Academy of Sciences 118, no. 20 (2021): e2024287118. http://dx.doi.org/10.1073/pnas.2024287118.

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This paper presents a modular software design for the construction of computational modeling technology that will help implement precision medicine. In analogy to a common industrial strategy used for preventive maintenance of engineered products, medical digital twins are computational models of disease processes calibrated to individual patients using multiple heterogeneous data streams. They have the potential to help improve diagnosis, prognosis, and personalized treatment for a wide range of medical conditions. Their large-scale development relies on both mechanistic and data-driven techn

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Pinto, José, João R. C. Ramos, Rafael S. Costa, and Rui Oliveira. "A General Hybrid Modeling Framework for Systems Biology Applications: Combining Mechanistic Knowledge with Deep Neural Networks under the SBML Standard." AI 4, no. 1 (2023): 303–18. http://dx.doi.org/10.3390/ai4010014.

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In this paper, a computational framework is proposed that merges mechanistic modeling with deep neural networks obeying the Systems Biology Markup Language (SBML) standard. Over the last 20 years, the systems biology community has developed a large number of mechanistic models that are currently stored in public databases in SBML. With the proposed framework, existing SBML models may be redesigned into hybrid systems through the incorporation of deep neural networks into the model core, using a freely available python tool. The so-formed hybrid mechanistic/neural network models are trained wit

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Mamlouk, Michael S., and Magdy Y. Mikhail. "Concept for Mechanistic-Based Performance Model for Flexible Pavements." Transportation Research Record: Journal of the Transportation Research Board 1629, no. 1 (1998): 149–58. http://dx.doi.org/10.3141/1629-17.

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A concept for a mechanistic-based performance model for flexible pavement was developed that considers the interaction between vehicles and pavement. A dynamic vehicle model was used to estimate the dynamic wheel force, and a three-dimensional finite element nonlinear dynamic pavement model was used to determine the dynamic pavement response. The effect of pavement roughness on vehicle bouncing and the effect of vehicle bouncing on the progression of pavement roughness were investigated under different roughness levels, suspension types, and layer thicknesses. The increase in roughness after e

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Andreeva, Anna A., Mohan Anand, Alexey I. Lobanov, Andrey V. Nikolaev, Mikhail A. Panteleev, and Modepalli Susree. "Mathematical modelling of platelet rich plasma clotting. Pointwise unified model." Russian Journal of Numerical Analysis and Mathematical Modelling 33, no. 5 (2018): 265–76. http://dx.doi.org/10.1515/rnam-2018-0022.

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AbstractThe mechanistic modelling of blood clotting and fibrin-polymer mesh formation is of significant value for medical and biophysics applications. This paper presents a combination of two pointwise kinetic models represented by system of ODEs. One of them represents the reaction dynamics of clotting factors including the role of the platelet membranes. The second one describes the fibrin-polymer formation as a multistage polymerization process with a sol-gel transition at the final stage. Complex-value second order Rosenbrock method (CROS) is employed for the computational experiments. A s

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Erdős, Balázs, Bart van Sloun, Gijs H. Goossens, et al. "Quantifying postprandial glucose responses using a hybrid modeling approach: Combining mechanistic and data-driven models in The Maastricht Study." PLOS ONE 18, no. 7 (2023): e0285820. http://dx.doi.org/10.1371/journal.pone.0285820.

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Computational models of human glucose homeostasis can provide insight into the physiological processes underlying the observed inter-individual variability in glucose regulation. Modelling approaches ranging from “bottom-up” mechanistic models to “top-down” data-driven techniques have been applied to untangle the complex interactions underlying progressive disturbances in glucose homeostasis. While both approaches offer distinct benefits, a combined approach taking the best of both worlds has yet to be explored. Here, we propose a sequential combination of a mechanistic and a data-driven model

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Li, Michael, Jonathan Dushoff, and Benjamin M. Bolker. "Fitting mechanistic epidemic models to data: A comparison of simple Markov chain Monte Carlo approaches." Statistical Methods in Medical Research 27, no. 7 (2018): 1956–67. http://dx.doi.org/10.1177/0962280217747054.

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Simple mechanistic epidemic models are widely used for forecasting and parameter estimation of infectious diseases based on noisy case reporting data. Despite the widespread application of models to emerging infectious diseases, we know little about the comparative performance of standard computational-statistical frameworks in these contexts. Here we build a simple stochastic, discrete-time, discrete-state epidemic model with both process and observation error and use it to characterize the effectiveness of different flavours of Bayesian Markov chain Monte Carlo (MCMC) techniques. We use fits

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Chipman, Antony, Brian F. Yates, Allan J. Canty, and Alireza Ariafard. "Reduction of a platinum(iv) prodrug model by sulfur containing biological reductants: computational mechanistic elucidation." Chemical Communications 54, no. 74 (2018): 10491–94. http://dx.doi.org/10.1039/c8cc05682j.

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Naghipoor, Jahed, and Timon Rabczuk. "A mechanistic model for drug release from PLGA-based drug eluting stent: A computational study." Computers in Biology and Medicine 90 (November 2017): 15–22. http://dx.doi.org/10.1016/j.compbiomed.2017.09.001.

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37

Mitrakos, D., A. Vouros, H. Bougioukou, and G. Giustini. "Computational fluid dynamics prediction of subcooled boiling of water using a mechanistic bubble-departure model." Nuclear Engineering and Design 412 (October 2023): 112465. http://dx.doi.org/10.1016/j.nucengdes.2023.112465.

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Duruisseaux, Michaël, Perrine Masson, Arnaud Nativel, et al. "Prospective validation of in silico clinical trials prediction using an EGFR-mutated NSCLC mechanistic computational disease model." Journal of Clinical Oncology 42, no. 16_suppl (2024): 8614. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.8614.

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8614 Background: Advanced lung adenocarcinoma (aLUAD) is divided into multiple molecularly defined subsets, each with specific biological characteristics and responses to targeted therapy. Consequently, conducting practice-changing and cost-effective randomized trials is challenging. In silico clinical trials, utilizing mechanistic computational models, offer a potential solution, yet their ability to predict clinical trial results has not been proven. Methods: We updated a previously published version of the computational mechanistic model of EGFR-mutated aLUAD, notably by incorporating metas

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Rathnayaka, Suresh C., Shahidul M. Islam, Ida M. DiMucci, Samantha N. MacMillan, Kyle M. Lancaster та Neal P. Mankad. "Probing the electronic and mechanistic roles of the μ4-sulfur atom in a synthetic CuZ model system". Chemical Science 11, № 13 (2020): 3441–47. http://dx.doi.org/10.1039/c9sc06251c.

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Zhang, Hongbin. "Stochastic EM Algorithm for Joint Model of Logistic Regression and Mechanistic Nonlinear Model in Longitudinal Studies." Mathematics 11, no. 10 (2023): 2317. http://dx.doi.org/10.3390/math11102317.

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We study a joint model where logistic regression is applied to binary longitudinal data with a mismeasured time-varying covariate that is modeled using a mechanistic nonlinear model. Multiple random effects are necessary to characterize the trajectories of the covariate and the response variable, leading to a high dimensional integral in the likelihood. To account for the computational challenge, we propose a stochastic expectation-maximization (StEM) algorithm with a Gibbs sampler coupled with Metropolis–Hastings sampling for the inference. In contrast with previous developments, this algorit

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Shvartsman, Stanislav Y., Cyrill B. Muratov, and Douglas A. Lauffenburger. "Modeling and computational analysis of EGF receptor-mediated cell communication in Drosophila oogenesis." Development 129, no. 11 (2002): 2577–89. http://dx.doi.org/10.1242/dev.129.11.2577.

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Autocrine signaling through the Epidermal Growth Factor Receptor (EGFR) operates at various stages of development across species. A recent hypothesis suggested that a distributed network of EGFR autocrine loops was capable of spatially modulating a simple single-peaked input into a more complex two-peaked signaling pattern, specifying the formation of a pair organ in Drosophila oogenesis (two respiratory appendages on the eggshell). To test this hypothesis, we have integrated genetic and biochemical information about the EGFR network into a mechanistic model of transport and signaling. The mod

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Feito, Norberto, José Antonio Loya, Ana Muñoz-Sánchez, and Raj Das. "Numerical Modelling of Ballistic Impact Response at Low Velocity in Aramid Fabrics." Materials 12, no. 13 (2019): 2087. http://dx.doi.org/10.3390/ma12132087.

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In this study, the effect of the impact angle of a projectile during low-velocity impact on Kevlar fabrics has been investigated using a simplified numerical model. The implementation of mesoscale models is complex and usually involves long computation time, in contrast to the practical industry needs to obtain accurate results rapidly. In addition, when the simulation includes more than one layer of composite ply, the computational time increases even in the case of hybrid models. With the goal of providing useful and rapid prediction tools to the industry, a simplified model has been develop

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Hazy, Thomas E., Michael J. Frank, and Randall C. O'Reilly. "Towards an executive without a homunculus: computational models of the prefrontal cortex/basal ganglia system." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1485 (2007): 1601–13. http://dx.doi.org/10.1098/rstb.2007.2055.

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The prefrontal cortex (PFC) has long been thought to serve as an ‘executive’ that controls the selection of actions and cognitive functions more generally. However, the mechanistic basis of this executive function has not been clearly specified often amounting to a homunculus. This paper reviews recent attempts to deconstruct this homunculus by elucidating the precise computational and neural mechanisms underlying the executive functions of the PFC. The overall approach builds upon existing mechanistic models of the basal ganglia (BG) and frontal systems known to play a critical role in motor

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Ray, Christina M. P., Huilin Yang, Jamie B. Spangler, and Feilim Mac Gabhann. "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors." PLOS Computational Biology 20, no. 6 (2024): e1012157. http://dx.doi.org/10.1371/journal.pcbi.1012157.

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The spread of cancer from organ to organ (metastasis) is responsible for the vast majority of cancer deaths; however, most current anti-cancer drugs are designed to arrest or reverse tumor growth without directly addressing disease spread. It was recently discovered that tumor cell-secreted interleukin-6 (IL-6) and interleukin-8 (IL-8) synergize to enhance cancer metastasis in a cell-density dependent manner, and blockade of the IL-6 and IL-8 receptors (IL-6R and IL-8R) with a novel bispecific antibody, BS1, significantly reduced metastatic burden in multiple preclinical mouse models of cancer

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Yang, Pei-Chi, Kevin R. DeMarco, Parya Aghasafari, et al. "A Computational Pipeline to Predict Cardiotoxicity." Circulation Research 126, no. 8 (2020): 947–64. http://dx.doi.org/10.1161/circresaha.119.316404.

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Rationale: Drug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation is an accepted surrogate marker for arrhythmia. But QT interval is too sensitive a marker and not selective, resulting in many useful drugs eliminated in drug discovery. Objective: To predict the impact of a drug from the drug chemistry on the cardiac rhythm. Methods and Results: In a new linkage, we connected atomistic scale information to protein, cell, and tissue scales by predicting drug-binding affinities and rates from simulation of ion channel and drug structure inter

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Sheehan, Robert, Abhishek Garg, Sarah Gaffen, and James Faeder. "A computational model for mechanistic investigation of negative feedback in interleukin-17 receptor signaling (CCR3P.201)." Journal of Immunology 194, no. 1_Supplement (2015): 49.2. http://dx.doi.org/10.4049/jimmunol.194.supp.49.2.

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Abstract IL-17 is a pro-inflammatory cytokine that promotes autoimmunity and protects against certain pathogens. Here, we present a computational model of signaling downstream of the IL-17 receptor. To manage the complexity of the system we use a rule-based modeling approach, in which signaling proteins are modeled as structured objects and rules describe their biochemical interactions, allowing us to consider all possible complexes and phosphoforms generated from a set of basic molecules. This model encompasses major signaling components downstream of IL-17R, which activate NF-κB and in turn

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Sedlack, Andrew J. H., Rozhin Penjweini, Katie A. Link, et al. "Computational Modeling and Imaging of the Intracellular Oxygen Gradient." International Journal of Molecular Sciences 23, no. 20 (2022): 12597. http://dx.doi.org/10.3390/ijms232012597.

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Computational modeling can provide a mechanistic and quantitative framework for describing intracellular spatial heterogeneity of solutes such as oxygen partial pressure (pO2). This study develops and evaluates a finite-element model of oxygen-consuming mitochondrial bioenergetics using the COMSOL Multiphysics program. The model derives steady-state oxygen (O2) distributions from Fickian diffusion and Michaelis–Menten consumption kinetics in the mitochondria and cytoplasm. Intrinsic model parameters such as diffusivity and maximum consumption rate were estimated from previously published value

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Albers, David J., Matthew E. Levine, Andrew Stuart, Lena Mamykina, Bruce Gluckman, and George Hripcsak. "Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype." Journal of the American Medical Informatics Association 25, no. 10 (2018): 1392–401. http://dx.doi.org/10.1093/jamia/ocy106.

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Abstract We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how data assimilation can be used to forecast future glucose values, to impute previously missing glucose values, and to infer type 2 diabetes p

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Raff, David A., and Jorge A. Ramírez. "A physical, mechanistic and fully coupled hillslope hydrology model." International Journal for Numerical Methods in Fluids 49, no. 11 (2005): 1193–212. http://dx.doi.org/10.1002/fld.1016.

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Lynn, Bryan K., Patrick De Leenheer, and Martin Schuster. "Putting theory to the test: An integrated computational/experimental chemostat model of the tragedy of the commons." PLOS ONE 19, no. 4 (2024): e0300887. http://dx.doi.org/10.1371/journal.pone.0300887.

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Cooperation via shared public goods is ubiquitous in nature, however, noncontributing social cheaters can exploit the public goods provided by cooperating individuals to gain a fitness advantage. Theory predicts that this dynamic can cause a Tragedy of the Commons, and in particular, a ‘Collapsing’ Tragedy defined as the extinction of the entire population if the public good is essential. However, there is little empirical evidence of the Collapsing Tragedy in evolutionary biology. Here, we experimentally demonstrate this outcome in a microbial model system, the public good-producing bacterium

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