Journal articles: 'Mechanistic Computational Model'

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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 (June 21, 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-throughput imaging scratch assay experiment, we show that reliable inference can be performed at a fraction of the computational cost of a traditional Bayesian inference scheme. By applying a detailed mathematical model of single cell motility, proliferation and death to a data set of 118 gene knockdowns, we characterise functional subgroups of gene knockdowns, each displaying its own typical combination of local cell density-dependent and -independent motility and proliferation patterns. By comparing these patterns to experimental measurements of cell counts and wound closure, we find that density-dependent interactions play a crucial role in the process of wound healing.

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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 (July 2006): 369–76. http://dx.doi.org/10.1533/joti.2005.0164.

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Zabihi, Azam, John Tello, Sebastien Incerti, Ziad Francis, Ghasem Forozani, Farid Semsarha, Amir Moslehi, and Mario A. Bernal. "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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Kraikivski, Pavel. "A Dynamic Mechanistic Model of Perceptual Binding." Mathematics 10, no. 7 (April 1, 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 different percepts. I use the model to study the dynamic behavior of coupled processes to characterize how these processes can modulate each other and reach a temporal synchrony. I identify different oscillatory dynamic regimes that depend on coupling mechanisms and parameter values. The model can also discriminate different combinations of initial inputs that are set by initial states of coupled processes. Decoding brain signals that are formed through perceptual binding is a challenging task, but my modeling results demonstrate how crosstalk between two systems of processes can possibly modulate their outputs. Therefore, my mechanistic model can help one gain a better understanding of how crosstalk between perception pathways can affect the dynamic behavior of the systems that involve perceptual binding.

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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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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 (November 18, 2019): e1007468. http://dx.doi.org/10.1371/journal.pcbi.1007468.

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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 (June 29, 2020): e1007848. http://dx.doi.org/10.1371/journal.pcbi.1007848.

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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 (March 6, 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 optimization algorithm enables a unique way of incorporating different aspects of model goodness in the model selection process, and also, at the end of the search procedure, provides a number of potential optimal model structures, making it possible for the modeler to make a choice based on the goal of the modeling. As an illustration, the framework is used for modeling wash-off and build-up of suspended solids (TSS) in highway runoff. The performance of the discovered model confirms the potential of the proposed evolutionary DDM framework for modeling environmental processes.

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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 (March 1, 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 the rest were actual data from the performed experiments. The developed model consistently simulated the flocculation behaviour of particles in settling columns better than Özer's model in eight of the nine data sets considered. It is recommended that the model's performance be further compared with other models like the Rule based and San's model. The errors due to the use of interpolated values when determining the performance of the empirical models need to be investigated. Furthermore, a three-way rather than two-way interpolation should now be achievable using the interpolation algorithm developed in this study thereby reducing the effects of interpolation bias. The above work opens the way to full automation of design of flocculation sedimentation basins and other gravitational particle separation systems which at present are designed manually and are susceptible to a wide range of human and random errors.

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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 (June 20, 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) conditioning were utilized to establish a prototypic computational model for this toxicity. Methods: Plasma and oral mucosal biopsy specimens were obtained from 3 autologous HSCT patients before and after administration of conditioning chemotherapy: Day -10, +10, +28 and +100; Day 0 was day of transplant. Full-thickness tissue samples were measured by RT- PCR for COX-1, COX-2, IL-1β and TNF-a. Plasma samples were measured by ELISA for PGE2 and PGI2, markers of COX-2 activity. The SM model was implemented as a system of 6 ordinary differential equations with 15 parameters. Parameter estimation and simulations were conducted based on experimental results, using a combination of Mathematica, Berkeley Madonna and Virtual Cell software packages. Results: The SM model captured the behavior of COX-1, IL-1β and PGE2 dynamics, predicting an exponential decay for each of these species. Half-lives relative to average steady-state values were found to be 9.7 days, 8.7 days and 9.3 days for COX-1, IL-1β and PGE2 respectively. Correlation ratios for each of these species were calculated to be 0.62, 0.61 and 0.90 respectively. Conclusions: This prototypic model provides a basis for development of a detailed mathematical model for quantifying relevant components of the mucositis pathway. This combination of modeling and experiment could also identify gaps in the pathway that would be important targets for new hypotheses, including possible feedback mechanisms relative to inflammatory cytokines. No significant financial relationships to disclose.

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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 (July 17, 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 measures via confidence distributions using estimates of ϵ -machines inferred via the Causal State Splitting Reconstruction (CSSR) algorithm. Via Monte Carlo simulation, we compare the inferential properties of the computational mechanics bootstrap to a Markov model bootstrap. The computational mechanics bootstrap is shown to have desirable inferential properties for a collection of model systems and generally outperforms the Markov model bootstrap. Finally, we perform an in silico experiment to assess the computational mechanics bootstrap’s performance on a corpus of ϵ -machines derived from the activity patterns of fifteen-thousand Twitter users.

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Rożeń, Antoni. "A Mechanistic Model of a Passive Autocatalytic Hydrogen Recombiner." Chemical and Process Engineering 36, no. 1 (March 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 hydrogen and oxygen proceeds at the catalyst surface only, while processes of heat and mass transport occur by assisted natural and forced convection in non-isothermal and laminar gas flow conditions in vertical channels between catalyst plates. The model accounts for heat radiation from a hot catalyst surface and has no adjustable parameters. It can be combined with an equation of chimney draft and become a useful engineering tool for selection and optimisation of catalytic recombiner geometry.

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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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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 (February 2021): 102112. http://dx.doi.org/10.1016/j.isci.2021.102112.

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Liorni, Ilaria, Esra Neufeld, Sven Kühn, Manuel Murbach, Earl Zastrow, Wolfgang Kainz, and Niels Kuster. "Novel mechanistic model and computational approximation for electromagnetic safety evaluations of electrically short implants." Physics in Medicine & Biology 63, no. 22 (November 12, 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 (April 13, 2007): 970–81. http://dx.doi.org/10.1007/s10439-007-9309-7.

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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 (December 16, 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 sequence processing (entitled: Vector-symbolic Sequencing of Binding INstantiating Dependencies, or VS-BIND). This model is designed to support the transformation of serially ordered elements in sensory sequences into structured representations of bound dependencies, readily operates on multiple timescales, and encodes or decodes sequences with respect to chunked items wherever dependencies occur in time. The model integrates established vector symbolic additive and conjunctive binding operators with neurobiologically plausible oscillatory dynamics, and is compatible with modern spiking neural network simulation methods. We show that the model is capable of simulating previous findings from structured sequence processing tasks that engage fronto-temporal regions, specifying mechanistic roles for regions such as prefrontal areas 44/45 and the frontal operculum during interactions with sensory representations in temporal cortex. Finally, we are able to make predictions based on the configuration of the model alone that underscore the importance of serial position information, which requires input from time-sensitive cells, known to reside in the hippocampus and dorsolateral prefrontal cortex. This article is part of the theme issue ‘Towards mechanistic models of meaning composition’.

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Zheng, Sainan, Shiwei Xu, Jinghong Zhou, Rongchun Shen, Yang Ji, Ming Shen, and Wei Li. "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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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 (February 1, 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 strategic, task-appropriate manner. These learning mechanisms are based on subcortical structures in the midbrain, basal ganglia, and amygdala, which together form an actor-critic architecture. The critic system learns which prefrontal representations are task relevant and trains the actor, which in turn provides a dynamic gating mechanism for controlling working memory updating. Computationally, the learning mechanism is designed to simultaneously solve the temporal and structural credit assignment problems. The model's performance compares favorably with standard backpropagation-based temporal learning mechanisms on the challenging 1-2-AX working memory task and other benchmark working memory tasks.

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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 (January 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 each load repetition can be calculated using basic material properties from which the pavement service life can be estimated. The number of equivalent 80-kN single axle load repetitions to failure was estimated under different conditions without the need for empirical observations. It was found that the number of load repetitions to go from one level of present serviceability index (PSI) to the next largely decreases as the PSI level decreases. The air bag suspension results in the longest pavement life, while the walking beam suspension results in the shortest pavement life. The total number of load repetitions to reach failure for thick pavement sections is 14 percent higher than that for medium-thick sections, and 63 percent greater than that for thin sections. The reverse of this analysis can be used to design the pavement section so that it would sustain a certain number of load repetitions before failure using a mechanistic procedure. The proposed concept for a mechanistic-based performance model developed in this study can be refined to increase the mechanistic portion of the model, reduce empirical involvement, and improve computational procedure.

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Ghose, Debraj, and Daniel Lew. "Mechanistic insights into actin-driven polarity site movement in yeast." Molecular Biology of the Cell 31, no. 10 (May 1, 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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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 (December 18, 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 second part of this work, we studied toluene, ethylbenzene, and xylene (TEX)-gene-disease associations using Comparative Toxicogenomics Database, pathway analysis and published microarray data from human gene expression changes in blood samples after short- and long-term exposures. Collectively, this information was used to establish hypotheses on potential linkages between TEX exposures and human health. The results show that 236 genes expressed were common between the short- and long-term exposures. These genes could be central for the interconnecting biological pathways potentially stimulated by TEX exposure, likely related to respiratory and neuro diseases. Using publicly available data we propose a conceptual framework to study pathway perturbations leading to toxicity of chemical mixtures. This proposed methodology lends mechanistic insights of the toxicity of mixtures and when experimentally validated will allow data gaps filling for mixtures’ toxicity assessment. This work proposes an approach using current knowledge, available multiple stream data and applying computational methods to advance mixtures risk assessment.

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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 (April 13, 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 subpopulations may become even more aggressive when they grow with a non-aggressive subpopulation. The model also provides a mechanistic explanation of how heterogeneity drives growth. In particular, we observed that even a mild heterogeneity in the proliferation rates of different cell subpopulations leads to a much faster overall tumour growth when compared to a homogeneous tumour. The proposed model may be a starting point to study tumour heterogeneity computationally and to suggest new hypotheses to be tested experimentally. This article is part of the theme issue ‘Patterns in soft and biological matters’.

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Masison, J., J. Beezley, Y. Mei, HAL Ribeiro, A. C. Knapp, L. Sordo Vieira, B. Adhikari, et al. "A modular computational framework for medical digital twins." Proceedings of the National Academy of Sciences 118, no. 20 (May 10, 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 techniques and requires the integration and ongoing update of multiple component models developed across many different laboratories. Distributed model building and integration requires an open-source modular software platform for the integration and simulation of models that is scalable and supports a decentralized, community-based model building process. This paper presents such a platform, including a case study in an animal model of a respiratory fungal infection.

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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 (November 27, 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 sensitivity analysis method built into the computational scheme helps clarify non-evident dependencies in the exhaustive system of ODEs. The unified model was primarily verified using conditions of factor VII deficiency. The model, however requires a significant effort to be tested against experimental data available.

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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 (March 4, 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. We derive our method using a careful probabilistic argument to show how contact tracing at the individual level is reflected in aggregate on the population level. We show that the resultant SEIR-TTI model accurately approximates the behaviour of a mechanistic agent-based model at far less computational cost. The computational efficiency is such that it can be easily and cheaply used for exploratory modelling to quantify the required levels of testing and tracing, alone and with other interventions, to assist adaptive planning for managing disease outbreaks.

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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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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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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 (May 19, 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 between the latter simplification, non-Newtonian power-law model and a newly proposed quasi-mechanistic model. The modified Krieger model, which treats blood as a suspension of plasma and particles, was implemented in computational fluid dynamics context here for the first time and is made available to the readers in a C# code in the supplementary material of this article. Two middle-cerebral artery and two anterior-communicating artery aneurysms, all ruptured, were utilized here as case studies. It was shown that the modified Krieger model had higher sensitivity for wall shear stress calculations in comparison with the other two models. The modified Krieger model yielded lower wall shear stress values consistently in comparison with the other two models. Moreover, the modified Krieger model has generally predicted higher pressure in the aneurysm models. Based on published aneurysm rupture studies, it is believed that ruptured aneurysms are usually correlated with lower wall shear stress values than unruptured ones. Therefore, this work concludes that the modified Krieger model is a potential candidate for providing better clinical relevance to aneurysm computational fluid dynamics simulations.

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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 (March 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 with a deep learning algorithm based on the adaptive moment estimation method (ADAM), stochastic regularization and semidirect sensitivity equations. The trained hybrid models are encoded in SBML and uploaded in model databases, where they may be further analyzed as regular SBML models. This approach is illustrated with three well-known case studies: the Escherichia coli threonine synthesis model, the P58IPK signal transduction model, and the Yeast glycolytic oscillations model. The proposed framework is expected to greatly facilitate the widespread use of hybrid modeling techniques for systems biology applications.

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Erdős, Balázs, Bart van Sloun, Gijs H. Goossens, Shauna D. O’Donovan, Bastiaan E. de Galan, Marleen M. J. van Greevenbroek, Coen D. A. Stehouwer, 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 (July 27, 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 modeling approach to quantify individuals’ glucose and insulin responses to an oral glucose tolerance test, using cross sectional data from 2968 individuals from a large observational prospective population-based cohort, the Maastricht Study. The best predictive performance, measured by R2 and mean squared error of prediction, was achieved with personalized mechanistic models alone. The addition of a data-driven model did not improve predictive performance. The personalized mechanistic models consistently outperformed the data-driven and the combined model approaches, demonstrating the strength and suitability of bottom-up mechanistic models in describing the dynamic glucose and insulin response to oral glucose tolerance tests.

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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 (May 16, 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 algorithm uses single imputation of the missing data during the Monte Carlo procedure, substantially increasing the computing speed. Through simulation, we assess the algorithm’s convergence and compare the algorithm with more classical approaches for handling measurement errors. We also conduct a real-world data analysis to gain insights into the association between CD4 count and viral load during HIV treatment.

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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 (May 30, 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 to simulated data, where parameters (and future behaviour) are known, to explore the limitations of different platforms and quantify parameter estimation accuracy, forecasting accuracy, and computational efficiency across combinations of modeling decisions (e.g. discrete vs. continuous latent states, levels of stochasticity) and computational platforms (JAGS, NIMBLE, Stan).

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

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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 (June 1, 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 model allows us to estimate the relative spatial ranges and time scales of the relevant feedback loops, to interpret the phenotypic transitions in eggshell morphology and to predict the effects of new genetic manipulations. We have found that the proposed mechanism with a single diffusing inhibitor is sufficient to convert a single-peaked extracellular input into a two-peaked pattern of intracellular signaling. Based on extensive computational analysis, we predict that the same mechanism is capable of generating more complex patterns. At least indirectly, this can be used to account for more complex eggshell morphologies observed in related fly species. We propose that versatility in signaling mediated by autocrine loops can be systematically explored using experiment-based mechanistic models and their analysis.

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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 (April 11, 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 control and action selection, where the BG provide a ‘Go’ versus ‘NoGo’ modulation of frontal action representations. In our model, the BG modulate working memory representations in prefrontal areas to support more abstract executive functions. We have developed a computational model of this system that is capable of developing human-like performance on working memory and executive control tasks through trial-and-error learning. This learning is based on reinforcement learning mechanisms associated with the midbrain dopaminergic system and its activation via the BG and amygdala. Finally, we briefly describe various empirical tests of this framework.

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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 (May 1, 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 promote the production of pro-inflammatory cytokines. NF-κB also promotes production of A20, a deubiquitinating enzyme that inhibits NF-κB activation in multiple settings. We recently showed that A20 acts as a negative regulator of the IL-17 signaling pathway. By modeling A20 inhibition of TRAF6 and IKK, we are able to recapitulate experimentally-observed oscillations in NF-κB activity and A20 expression. Novel experimental data identifying the dynamics of signaling intermediates has further constrained model behavior and informed model development. Modeling the depletion of IκB, followed by its rebound above baseline levels, required the addition of a stochastic model of transcription factor binding, paired with a deterministic model of signaling events. We are currently using the hybrid model to identify which of several potential mechanisms A20 uses to attenuate IL-17 signaling.

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Yang, Pei-Chi, Kevin R. DeMarco, Parya Aghasafari, Mao-Tsuen Jeng, John R. D. Dawson, Slava Bekker, Sergei Y. Noskov, Vladimir Yarov-Yarovoy, Igor Vorobyov, and Colleen E. Clancy. "A Computational Pipeline to Predict Cardiotoxicity." Circulation Research 126, no. 8 (April 10, 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 interactions and then used these values to model drug effects on the hERG channel. Model components were integrated into predictive models at the cell and tissue scales to expose fundamental arrhythmia vulnerability mechanisms and complex interactions underlying emergent behaviors. Human clinical data were used for model framework validation and showed excellent agreement, demonstrating feasibility of a new approach for cardiotoxicity prediction. Conclusions: We present a multiscale model framework to predict electrotoxicity in the heart from the atom to the rhythm. Novel mechanistic insights emerged at all scales of the system, from the specific nature of proarrhythmic drug interaction with the hERG channel, to the fundamental cellular and tissue-level arrhythmia mechanisms. Applications of machine learning indicate necessary and sufficient parameters that predict arrhythmia vulnerability. We expect that the model framework may be expanded to make an impact in drug discovery, drug safety screening for a variety of compounds and targets, and in a variety of regulatory processes.

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Zhao, Chen, and Aleksander S. Popel. "Protocol for simulating macrophage signal transduction and phenotype polarization using a large-scale mechanistic computational model." STAR Protocols 2, no. 3 (September 2021): 100739. http://dx.doi.org/10.1016/j.xpro.2021.100739.

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Heusinkveld, Maarten H. G., Tammo Delhaas, Joost Lumens, Wouter Huberts, Bart Spronck, Alun D. Hughes, and Koen D. Reesink. "Augmentation index is not a proxy for wave reflection magnitude: mechanistic analysis using a computational model." Journal of Applied Physiology 127, no. 2 (August 1, 2019): 491–500. http://dx.doi.org/10.1152/japplphysiol.00769.2018.

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The augmentation index (AIx) is deemed to capture the deleterious effect on left ventricular (LV) work of increased wave reflection associated with stiffer arteries. However, its validity as a proxy for wave reflection magnitude has been questioned. We hypothesized that, in addition to increased wave reflection due to increased pulse wave velocity, LV myocardial shortening velocity influences AIx. Using a computational model of the circulation, we investigated the isolated and combined influences of myocardial shortening velocity vs,LV and arterial stiffness on AIx. Aortic blood pressure waveforms were characterized using AIx and the reflected wave pressure amplitude ([Formula: see text], obtained using wave separation analysis). Our reference simulation (normal vs,LV and arterial stiffness) was characterized by an AIx of 21%. A realistic reduction in vs,LV caused AIx to increase from 21 to 42%. An arterial stiffness increase, characterized by a relevant 1.0 m/s increase in carotid-femoral pulse wave velocity, caused AIx to increase from 21 to 41%. Combining the reduced vs,LV and increased arterial stiffness resulted in an AIx of 54%. In a multistep parametric analysis, both vs,LV and arterial stiffness were about equal determinants of AIx, whereas [Formula: see text] was only determined by arterial stiffness. Furthermore, the relation between increased AIx and LV stroke work was only ≈50% explained by an increase in arterial stiffness, the other factor being vs,LV. The [Formula: see text], on the other hand, related less ambiguously to LV stroke work. We conclude that the AIx reflects both cardiac and vascular properties and should not be considered an exclusively vascular parameter. NEW & NOTEWORTHY We used a state-of-the-art computational model to mechanistically investigate the validity of the augmentation index (AIx) as a proxy for (changes in) wave reflection. In contrary to current belief, we found that LV contraction velocity influences AIx as much as increased arterial stiffness, and increased AIx does not necessarily relate to an increase in LV stroke work. Wave reflection magnitude derived from considering pressure, as well as flow, does qualify as a determinant of LV stroke work.

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Sheehan, Robert, Abhishek Garg, Sarah Gaffen, and James Faeder. "A computational model for mechanistic investigation of negative feedback in Inerleukin-17 receptor signaling (CCR6P.274)." Journal of Immunology 192, no. 1_Supplement (May 1, 2014): 182.6. http://dx.doi.org/10.4049/jimmunol.192.supp.182.6.

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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. The model includes 19 molecules and 46 reaction rules, which expand to a reaction network with 168 distinct chemical species - complexes and phosphoforms of the basic molecules - which are connected through a network of 5,145 unidirectional reactions. The model is defined and simulated using the BioNetGen software. This model encompasses major signaling components downstream of IL-17R, which activate NF-κB and in turn promote the production of pro-inflammatory cytokines. NF-κB also promotes production of A20, a deubiquitinating enzyme that inhibits NF-κB activation in multiple settings. We recently showed that A20 is a negative regulator of the IL-17 signaling pathway. By modeling A20 interactions with TRAF6 and IKK, we are able to recapitulate experimentally-observed oscillations in NF-κB-dependent A20 expression at both the mRNA and protein levels. Further expansion of the model, along with additional experimental work tracking the dynamics of signaling intermediates, will enable us to explore novel hypotheses about the regulatory mechanisms of A20 and other potential regulators of IL-17 signaling.

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Sedlack, Andrew J. H., Rozhin Penjweini, Katie A. Link, Alexandra Brown, Jeonghan Kim, Sung-Jun Park, Jay H. Chung, Nicole Y. Morgan, and Jay R. Knutson. "Computational Modeling and Imaging of the Intracellular Oxygen Gradient." International Journal of Molecular Sciences 23, no. 20 (October 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 values for isolated and intact mitochondria. The model was compared with experimental data collected for the intracellular and mitochondrial pO2 levels in human cervical cancer cells (HeLa) in different respiratory states and under different levels of imposed pO2. Experimental pO2 gradients were measured using lifetime imaging of a Förster resonance energy transfer (FRET)-based O2 sensor, Myoglobin-mCherry, which offers in situ real-time and noninvasive measurements of subcellular pO2 in living cells. On the basis of these results, the model qualitatively predicted (1) the integrated experimental data from mitochondria under diverse experimental conditions, and (2) the impact of changes in one or more mitochondrial processes on overall bioenergetics.

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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 (June 28, 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 developed in this work. The model offers an advantage in the reduced computational time compared to a full 3D model (around a 90% faster). The proposed model has been validated against equivalent experimental and numerical results reported in the literature with acceptable deviations and accuracies for design requirements. The proposed numerical model allows the study of the influence of the geometry on the impact response of the composite. Finally, after a parametric study related to the number of layers and angle of impact, using a response surface methodology, a mechanistic model and a surface diagram have been presented in order to help with the calculation of the ballistic limit.

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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 (October 1, 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 phenotypes. At the heart of data assimilation is the mechanistic model, here an endocrine model. Such models can vary in complexity, contain testable hypotheses about important mechanics that govern the system (eg, nutrition’s effect on glucose), and, as such, constrain the model space, allowing for accurate estimation using very little data.

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Motiwale, Shruti, Adhitya Subramani, Reuben H. Kraft, and Xianlian Zhou. "A non-linear multiaxial fatigue damage model for the cervical intervertebral disc annulus." Advances in Mechanical Engineering 10, no. 6 (June 2018): 168781401877949. http://dx.doi.org/10.1177/1687814018779494.

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A significant portion of the military population develops severe neck pain in the course of their duties. It has been hypothesized that neck pain is a consequence of accelerated degeneration of the intervertebral discs in the cervical spine, but more occupational and mechanistic-based tools and research are needed to positively confirm the link between neck pain and accelerated disc degeneration. Heavy head-supported mass including helmets and accessories worn by military personnel may subject the intervertebral discs of the cervical spine to complex cyclic loading profiles. In addition, some military operational travel which includes riding on high speed planing boats has also been reported to result in high magnitude cyclic loading on cervical spine discs. In this article, we present a methodology to computationally predict fatigue damage to cervical intervertebral discs over extended periods of time, by integrating kinematics-based biomechanical models with a continuum damage mechanics-based theory of disc degeneration. Through this computational approach, we can gain insights into the relationship between these military activities and possible accelerated fatigue degeneration of cervical intervertebral discs and provide a quantitative prediction tool for decade-long time ranges. The four significant improvements this computational framework adds to the area of modeling intervertebral disc degeneration are the following: (a) it addresses the non-linear nature of fatigue damage evolution, (b) it includes the effect of aging and damage recovery to accurately simulate biological phenomena, (c) it computes fatigue damage taking into account the multiaxial stress state in the disc, and (d) it correlates the computational damage parameter with established clinical grading systems for disc degeneration.

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Yeoh, Guan Heng, and Xiaobin Zhang. "Computational fluid dynamics and population balance modelling of nucleate boiling of cryogenic liquids: Theoretical developments." Journal of Computational Multiphase Flows 8, no. 4 (November 22, 2016): 178–200. http://dx.doi.org/10.1177/1757482x16674217.

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The main focus in the analysis of pool or flow boiling in saturated or subcooled conditions is the basic understanding of the phase change process through the heat transfer and wall heat flux partitioning at the heated wall and the two-phase bubble behaviours in the bulk liquid as they migrate away from the heated wall. This paper reviews the work in this rapid developing area with special reference to modelling nucleate boiling of cryogenic liquids in the context of computational fluid dynamics and associated theoretical developments. The partitioning of the wall heat flux at the heated wall into three components – single-phase convection, transient conduction and evaporation – remains the most popular mechanistic approach in predicting the heat transfer process during boiling. Nevertheless, the respective wall heat flux components generally require the determination of the active nucleation site density, bubble departure diameter and nucleation frequency, which are crucial to the proper prediction of the heat transfer process. Numerous empirical correlations presented in this paper have been developed to ascertain these three important parameters with some degree of success. Albeit the simplicity of empirical correlations, they remain applicable to only a narrow range of flow conditions. In order to extend the wall heat flux partitioning approach to a wider range of flow conditions, the fractal model proposed for the active nucleation site density, force balance model for bubble departing from the cavity and bubble lifting off from the heated wall and evaluation of nucleation frequency based on fundamental theory depict the many enhancements that can improve the mechanistic model predictions. The macroscopic consideration of the two-phase boiling in the bulk liquid via the two-fluid model represents the most effective continuum approach in predicting the volume fraction and velocity distributions of each phase. Nevertheless, the interfacial mass, momentum and energy exchange terms that appear in the transport equations generally require the determination of the Sauter mean diameter or interfacial area concentration, which strongly governs the fluid flow and heat transfer in the bulk liquid. In order to accommodate the dynamically changing bubble sizes that are prevalent in the bulk liquid, the mechanistic approach based on the population balance model allows the appropriate prediction of local distributions of Sauter mean diameter or interfacial area concentration, which in turn can improve the predictions of the interfacial mass, momentum and energy exchanges that occur across the interface between the phases. Need for further developments are discussed.

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Lagergren, John, Amanda Reeder, Franz Hamilton, Ralph C. Smith, and Kevin B. Flores. "Forecasting and Uncertainty Quantification Using a Hybrid of Mechanistic and Non-mechanistic Models for an Age-Structured Population Model." Bulletin of Mathematical Biology 80, no. 6 (April 2, 2018): 1578–95. http://dx.doi.org/10.1007/s11538-018-0421-7.

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Savage, G. J., and Young Kap Son. "Second-Moment-Based Design of Dynamic Systems with Both Uncertain Excitations and Parameters Via Differentiable Meta-Models." International Journal of Reliability, Quality and Safety Engineering 26, no. 04 (June 2, 2019): 1950019. http://dx.doi.org/10.1142/s0218539319500190.

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Design using second-moments is readily understood by engineers. The output means (first-moments) and covariances (second-moments) are expressed through the means and covariances of the inputs. Further, various performance indexes can be formulated in terms of the second-moments and used to measure the “goodness” of the system’s performance. This paper addresses the design of nonlinear dynamic systems with uncertainty in both the component parameters and the excitations. In order to reduce the computational effort needed for design iterations on the mechanistic model, meta-models are introduced as computationally efficient surrogates. Herein, a novel, differentiable, meta-model that finds the response of dynamic systems with simultaneous component and excitation uncertainty is presented. Operationally, a family of training excitations and sets of training parameters are chosen and stored in respective matrices. Both types of inputs must have some realistic bounds. The corresponding responses, produced by the mechanistic model, make use of all of the training parameter sets interleafed with the training excitations: the time-sampled results are stored in the response matrix. An application of singular value decomposition on the response matrix reveals a repeating pattern of sub-vectors in the left singular vectors. Each sub-vector (viewed as the output) is replaced by a least-squares meta-model that links in the parameter matrix. The result is a parameter-response matrix with the same number of rows as the excitation matrix. Finally, to complete the meta-model, another application of the least-squares paradigm links the excitation matrix to the columns of the parameter-response matrix. Performance indexes, and approximations of their means and covariances through Taylor series, provide cogent optimization measures. The required derivatives are easily obtained from the explicit form of the meta-model. The efficacy of the meta-model is shown through the design of a nonlinear, quarter automobile, system. The accuracy, increased computation speed and robustness of the methodology provide the impact of the work herein. The sources of errors are identified and ways to mitigate them are discussed.

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Journal articles on the topic 'Mechanistic Computational Model'

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