1,721,230 research outputs found
Lagrangian–Eulerian methods for multiphase flows
Full text linkThis review article aims to provide a comprehensive and understandable account of the theoretical foundation, modeling issues, and numerical implementation of the Lagrangian–Eulerian (LE) approach for multiphase flows. The LE approach is based on a statistical description of the dispersed phase in terms of a stochastic point process that is coupled with a Eulerian statistical representation of the carrier fluid phase. A modeled transport equation for the particle distribution function — also known as Williams' spray equation in the case of sprays — is indirectly solved using a Lagrangian particle method. Interphase transfer of mass, momentum and energy are represented by coupling terms that appear in the Eulerian conservation equations for the fluid phase. This theoretical foundation is then used to develop LE sub-models for interphase interactions such as momentum transfer. Every LE model implies a corresponding closure in the Eulerian–Eulerian two-fluid theory, and these moment equations are derived. Approaches to incorporate multiscale interactions between particles (or spray droplets) and turbulent eddies in the carrier gas that result in better predictions of particle (or droplet) dispersion are described. Numerical convergence of LE implementations is shown to be crucial to the success of the LE modeling approach. It is shown how numerical convergence and accuracy of an LE implementation can be established using grid-free estimators and computational particle number density control algorithms. This review of recent advances establishes that LE methods can be used to solve multiphase flow problems of practical interest, provided sub-models are implemented using numerically convergent algorithms. These insights also provide the foundation for further development of Lagrangian methods for multiphase flows. Extensions to the LE method that can account for neighbor particle interactions and preferential concentration of particles in turbulence are outlined.This is a manuscript of an article published as Subramaniam, Shankar. "Lagrangian–Eulerian methods for multiphase flows." Progress in Energy and Combustion Science 39, no. 2-3 (2013): 215-245. DOI: 10.1016/j.pecs.2012.10.003. Posted with permission.</p
sj-docx-1-pie-10.1177_09544089221132446 - Supplemental material for Optimal development for a 3D-printed gripper for biomedical and micromanipulation applications by non-parametric regression-based metaheuristic technique
No full textSupplemental material, sj-docx-1-pie-10.1177_09544089221132446 for Optimal development for a 3D-printed gripper for biomedical and micromanipulation applications
by non-parametric regression-based metaheuristic technique by Ngoc Thoai Tran, Minh Phung Dang, Ngoc Le Chau, Subramaniam Shankar, Dharam Buddhi and Thanh-Phong Dao in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p
Structure and dynamics of antibody hapten complexes
No full textAntibody molecules are highly antigen-specific receptors of the immune system. In this thesis, structural and functional relationship in antibody molecules are studied.The nature of the \rm V\sb{L}-\rm V\sb{H} interface and the role of specific residues that are involved in the conserved packing interactions across the domains are analyzed. Specific residues in the framework and hypervariable regions are identified as key residues that contribute to the stability of the interface. Using the residues involved in conserved contacts across the domain, a list of constraints on C-C distances are obtained which can be used as additional constraints in computer modeling of antibody Fv structures.The high degree of sequence and structural homology among the antibodies make them good targets for homology modeling. Using computer-aided modeling, the structure of the variable domain fragment of two antibodies, NC6.8 and NC10.14, raised against sweet taste ligand NC174, are predicted and the key residues in the combining site of the antibodies identified. Experimental methods, like spectroscopy and competitive ligand binding have also supported these modeling studies. Molecular dynamics simulations of the uncomplexed antibody NC6.8 are performed to understand the nature of the correlated motions of the antibody, prior to hapten binding. The dynamics simulations show that the CDR loops adopt a variety of conformations and the dynamic structure could play a role in antigen binding. The effect of the mutation of in the antibody NC6.8, is evaluated in terms of free energy. The 'slow growth' method is used to determine the stability of the mutation in the native and the complexed antibody, as compared to the unfolded state of the protein. The net difference in the free energy for the change is 1.26 kcal/mol for the native antibody and 1.42 kcal/mol for the complexed antibody, with the Phe residue being more stable in the folded antibody as compared to its unfolded state.Made available in DSpace on 2011-05-07T13:53:03Z (GMT). No. of bitstreams: 2
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Computer simulation of titration behavior in proteins
No full textThe pH dependent properties of proteins can be successfully modeled using continuum electrostatic methods provided that a sufficiently detailed and accurate model of the protein system is used. In this work, the effects of conformational changes on calculated pK\rm\sb{a} values in peptide and protein systems are investigated, and it is found that an ensemble of structures may be the best representation of a protein to use for the calculation of pH-dependent properties. Studies of the effects of inclusion of explicit water molecules in the continuum electrostatic model show that this modification may improve results when an optimal number of water molecules is used. A modification to the continuum electrostatics-based multiple site titration theory based on solvent accessibility is proposed, and it is shown that this model results in improved agreement of calculated pK\rm\sb{a} values with experiment when a low protein interior dielectric constant is used. The techniques developed are applied to two large protein systems. A trio of antibody/lysozyme complexes which differ minimally in epitope and paratope structure, but have widely varying association constants, are studied. In addition to electrostatic techniques, results of contact mapping and molecular surface area and volume calculations aid in the understanding of the differing properties of these systems. Several modeled mutant structures of each antibody and lysozyme are also examined. Each of these is predicted to associate less strongly with its counterpart than in the wild type. The photosynthetic reaction center of Rb. sphaeroides is studied, with some success in reproducing its known pH-dependent proton uptake. Mutants in which two critical residues in the secondary quinone binding site of the reaction center are altered are shown to be proton uptake impaired, with respect to the wild type.Made available in DSpace on 2011-05-07T13:34:28Z (GMT). No. of bitstreams: 2
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Development and Integration of Bioinformatic and Electrostatic Techniques With Application to Biological Systems
No full text150 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2001.Proteins consist of a sequence of residues. These residues generate the electrostatic nature of the protein, thereby determines all other properties for the protein. Computational techniques for calculating electrostatic properties are well established. More recently, bioinformatics techniques, used to investigate and compare protein sequences, have come into prominence. Combining these separate methodologies should have a synergistic effect. In this work, electrostatic based techniques will be developed and applied to simulate the flux of ions through membrane channels. A procedure for the prediction of ion flow, based on full three dimensional charge distribution a specific pH, and containing no freely adjusted variables, is presented. This procedure is used to gain important insight on the mechanism for the regulation of potassium ion flow in KcsA and provides support for a possible method of channel opening. Additionally, bioinformatic based techniques are developed and applied to the Cytochrome P450 and Fe2S2 Ferredoxin families. Insights gained from these techniques are integrated into electrostatic investigation of the protein-protein association of a member from each protein family, P450 cam and Putidaredoxin. These two proteins function as an electron-transfer pair and bind using charge complementary sites on their surface. The specific binding site is unknown. Three dimensional Brownian dynamics methods were developed and applied to identify possible binding sites. Many such interacting residue pairs were determined and evaluated. Several exhibited a high reaction probability. These sites were shown to demonstrate properties in agreement with experimental observations and thus are predicted to be good candidates for P450cam-Putidaredoxin binding sites.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD
Conservation of Electrostatically Mediated Function Across Protein Families and Superfamilies
No full text136 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2000.The molecular association between an enzyme and substrate is highly specific. The overall association reaction is a delicate balance between attractive and repulsive forces. Association occurs at the cost of removing waters from the binding site and reducing side chain degrees of freedom, which is balanced by the energy gained due to non-covalent interactions, burying of hydrophobic surfaces, and other stabilizing factors (most of which are electrostatic in nature). At long distances, electrostatic forces orient and steer the incoming ligand to the receptor, providing the leit-motif for association. This work explores the conserved electrostatic properties across enzyme families and superfamilies that, despite sequence and structural differences, have maintained function. In order to completely understand how function is conserved across a enzyme family or superfamily, we investigate the conserved and varying features of the protein at both the structure and sequence level. First, methods based in statistical mechanics and biophysics have proven robust enough to probe enzyme function. Continuum electrostatics methods (based on the Poisson-Boltzmann equation) provide a cornputationally efficient method for calculating electrostatic properties of biological molecules. The insights from these methods show that the electrostatically mediated function within a variety of enzyme families and superfamilies is conserved. Second, bioinformatic methods are used to show how the sequence plasticity of the enzyme family or superfamily provides the evolutionary origins for the conserved electrostatic mechanism.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD
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Systems Level Analysis of Late Onset Alzheimer’s Disease Across Four Brain Regions
Full text linkAlzheimer’s Disease (AD) is a neurodegenerative disease that causes loss of memory, among other cognitive functions and is the 6th leading cause of death in the United States. Late Onset Sporadic Alzheimer’s Disease (LOAD) is the most common form of AD and yet still little is known about the disease and there is no cure. The disease is complex, with temporally distinct effects at different stages. Microarray data generated from 885 postmortem patient brain tissue samples (401 control samples, 484 AD samples) was used to study the effects of LOAD on four regions of the brain: the dorsolateral Prefrontal Cortex (PFC), Visual Cortex (VC), Cerebellum (CR) and the Middle Temporal Gyrus (MTG). Various methods were explored and selected to create an analysis pipeline. The use of gene set and transcription factor enrichment enabled functional class sorting that suggested key biological functions affected by AD in each region. Five main endotypes were identified and further analyzed: Cell Cycle (G1-S Phase), Inflammation, Dedifferentiation, Synaptic Signaling and Mitochondrial Metabolism. Custom gene sets were used to create protein-protein interaction networks in order to identify key TF and gene activity within each brain region, revealing that these five endotypes were preferentially enriched in the PFC and VC brain regions
Towards Cybernetic Modeling of Biological Processes in Mammalian Systems—Lipid Metabolism in the Murine Macrophage
No full textRegulation of metabolism in mammalian cells is achieved through a complex interplay between cellular signaling, metabolic reactions, and transcriptional changes. The modeling of metabolic fluxes in a cell requires the knowledge of all these mechanisms, some of which may be unknown. A cybernetic approach provides a framework to model these complex interactions through the implicit accounting of such regulatory mechanisms, assuming a biological “goal”. The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena ranging from complex substrate uptake patterns and dynamic metabolic flux distributions to the behavior of gene knockout strains. The premise underlying the cybernetic framework is that the regulatory processes affecting metabolism can be mathematically formulated as a cybernetic objective through variables that constrain the network to achieve a specified biological “goal”.Cybernetic theory builds on the perspective that regulation is organized towards achieving goals relevant to an organism’s survival or displaying a specific phenotype in response to a stimulus. While cybernetic models have been established by prior work carried out in bacterial systems, we show its applicability to more complex biological systems with a predefined goal. We have modeled eicosanoid, a well-characterized set of inflammatory lipids derived from arachidonic acid, metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA, a chemical analogue of Lipopolysaccharide found on the surface of bacterial cells) and adenosine triphosphate (ATP, a danger signal released in response to surrounding cell death) using cybernetic control variables. Here, the cybernetic goal is inflammation; the hallmark of inflammation is the expression of cytokines which act as autocrine signals to stimulate a pro-inflammatory response. Tumor necrosis factor (TNF)-α is an exemplary pro-inflammatory marker and can be designated as a cybernetic objective for modeling eicosanoid—prostaglandin (PG) and leukotriene (LK)—metabolism. Transcriptomic and lipidomic data for eicosanoid biosynthesis and conversion were obtained from the LIPID Maps database. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation using the treatment ATP stimulation. We then validated our model by predicting an independent data set, the PG response of KLA primed ATP stimulated BMDM cells.The process of inflammation is mediated by the production of multiple cytokines, chemokines, and lipid mediators each of which contribute to specific individual objectives. For such complex processes in mammalian systems, a cybernetic objective based on a single protein/component may not be sufficient to capture all the biological processes thereby necessitating the use of multiple objectives. The choice of the objective function has been made by intuitive considerations in this thesis. If objectives are conjectured, an argument can be made for numerous alternatives. Since regulatory effects are estimated from unregulated kinetics, one encounters the risk of multiplicity in this regard giving rise to multiple models. The best model is of course that which is able to predict a comprehensive set of perturbations. Here, we have extended our above model to also capture the dynamics of LKs. We have used migration as a biological goal for LK using the chemoattractant CCL2 as a key representative molecule describing cell activation leading to an inflammatory response where a goal composed of multiple cybernetic objectives is warranted
Modeling and simulation of hydrodynamics, heat and mass transfer of wet granular mixtures in agitated filter-dryers
No full textWet granular mixtures have a plethora of industrial applications such as agricultural production, pharmaceutical manufacturing, and cement mixing. Therefore, a good understanding of the rheology, hydrodynamics, and heat- and mass-transfer considerations of wet granular mixtures is paramount. One such application is the drying of a liquid solvent from a wet cake consisting of active pharmaceutical ingredient (API) in an agitated filter-dryer (AFD). It is a key but also challenging unit operation in the pharmaceutical industry. However, a good understanding of the drying mechanism involved in this operation is often lacking.
Agitated filter-dryers operate at high volume fractions of the granular material to increase the batch size of the product. One of the challenges in obtaining accurate predictions of the granular rheology and hydrodynamics in densely packed granular beds using constitutive models for the granular stress in continuum simulations is ensuring their realizable behavior near the maximum packing limit. In this work, we have developed a novel solution approach through a one-dimensional (1-D) canonical problem to obtain realizable results from continuum simulations of dense granular flow without any ad hoc modifications in constitutive models. This new solution approach is capable of capturing shock propagations and controlling postshock oscillations and is used to report solutions to a granular flow problem illustrating the transition from variable density to incompressible regime at the maximum packing limit. This bridges the gap between stationary and non-stationary CFD simulations of hydrodynamics, and heat and mass transfer of wet granular mixtures in AFD assemblies.
The principal challenges in the drying process of API in AFDs have been predicting and controlling particle agglomeration and/or particle attrition. The API bed is mostly static and an intermittent agitation protocol is followed to minimize any impact on particle size distribution (PSD) while improving homogeneity during agitation. Therefore, a thorough understanding of heat- and mass-transfer considerations in stationary wet granular mixtures is equally important. In this work, three-dimensional (3-D) analytical solutions and computational fluid dynamics (CFD) simulations are developed to gain a deeper understanding into the problem. A better understanding of the drying process of wet cakes in AFD assemblies is gained by integrating insights from three-dimensional analytical solutions and CFD simulations into a zero-dimensional model to explain experimental data.
A continuum solver to study multiphase coupled heat and mass transfer that can be self-consistent and does not require inputs from other models to study the problem under consideration has been developed in an OpenFOAM framework to obtain spatial variations of different fields and analyze the process in 3-D. A way forward to develop a coupled heat- and mass-transfer solver that provides faster solutions is also proposed. Integration of the aforementioned modeling techniques to study the drying of wet granular mixtures is valuable in devising an accurate drying protocol in agitated filter-dryers
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Transcriptomic Profiling of Sporadic Alzheimer’s Disease Patients
Full text linkAlzheimer’s Disease (AD) is a neurodegenerative disorder characterized physically by dementia and physiologically by senile plaques and neurofibrillary tangles in the brain. Mutations to the genes PSEN1, PSEN2 and APP result in the manifestation of the dominantly inherited form of AD, Familial AD. Though a number of risk factors, including genetic mutations, environmental factors, and aging, have been attributed to the sporadic form of AD, the underlying mechanistic basis of the disease is yet to be unearthed. Analysing sporadic AD RNA-seq samples together with non-demented controls allows us to uncover these molecular mechanisms and to this end, we have analysed a 50-sample RNA-Seq dataset, with 40 AD samples and 10 controls, and identified disease-associated endotypes that arise from gene expression changes between the AD cases and the controls. We have also described an adapted framework for analysing low-quality RNA-seq samples (RIN > 1, < 3), and applying this framework to our data results in the categorization of the samples into two groups which show different degrees of differential expression with respect to the controls. Endotypes such as Dedifferentiation and Synaptic Signalling are preferentially enriched in samples from one group, although a small subset of samples from this group exhibits a significantly higher enrichment of said endotypes compared to other group members. We hypothesize that these differences in endotype signatures manifest due to varying severity among the samples, and scrutinizing the similarities and dissimilarities among the groups can provide insights into the etiology of Sporadic AD
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