COMPUTATIONAL STUDY OF MICROSTRUCTURE-PROPERTYMECHANISM RELATIONS IN FERROIC COMPOSITES

Ferroic materials, as notable members of smart materials, have been widely used in applications that perform sensing, actuation and control. The macroscopic property change of ferroic materials may become remarkably large during ferroic phase transition, leading to the fact that the macroscopic properties can be tuned by carefully applying a suitable external field (electric, magnetic, stress). To obtain an enhancement in physical and/or mechanical properties, different kinds of ferroic composites have been fabricated. The properties of a ferroic composite are determined not only by the properties and relative amounts of the constituent phases, but also by the microstructure of individual phase such as the phase connectivity, phase size, shape and spatial arrangement. This dissertation mainly focuses on the computational study of microstructure – property – mechanism relations in two representative ferroic composites, i.e., two-phase particulate magnetoelectric (ME) composite and polymer matrix ferroelectric composite. The former is a great example of ferroic composite exhibiting a new property and functionality that neither of the constituent phases possesses individually. The latter well represents the kind of ferroic composites having property combinations that are better than the existing materials. Phase field modeling was employed as the computing tool, and the required models for ferroic composites were developed based on existing models for monolithic materials. Extensive computational simulations were performed to investigate the microstructure-property relations and the underlying mechanism in ferroic composites. In particulate, it is found that for ME composite 0-3 connectivity (isolated magnetostrictive phase) is necessary to exhibit ME effect, and small but finite electrical conductivity of isolated magnetic phase can beneficially enhance ME effect. It is revealed that longitudinal and transverse ME coefficients of isotropic 0-3 particulate composites can be effectively tailored by controlling magnetic domain structures without resort to anisotropic two-phase microstructures. Simulations also show that the macroscopic properties of the ferroelectricpolymer composites critically depend on the ferroelectric phase connectivity while are not sensitive to the sizes and internal grain structures of the ceramic particles. Texturing is found critical to exploit the paraelectric«ferroelectric phase transition and nonlinear polarization behavior in paraelectric polycrystal and its polymer matrix composite. Additionally, a Diffuse Interface Field model was developed to simulate packing and motion in liquid phase which is promising for studying the fabrication of particulatepolymer composites.

Introduction to Realistic Neural Modeling

This document corresponds to the tutorial on realistic neural modeling given by David Beeman at WAM-BAMM*05, the first annual meeting of the World Association of Modelers (WAM) Biologically Accurate Modeling Meeting (BAMM) on March 31, 2005 in San Antonio, TX. Part I - Introduction to Realistic Neural Modeling for the Beginner: This is a general overview and introduction to compartmental cell modeling and realistic network simulation for the beginner. Although examples are drawn from GENESIS simulations, the tutorial emphasizes the general modeling approach, rather than the details of using any particular simulator. Part II - Getting Started with Modeling Using GENESIS: This builds upon the background of Part I to describe some details of how this approach is used to construct cell and network simulations in GENESIS. It serves as an introduction and roadmap to the extended hands-on GENESIS Modeling Tutorial.

GENESIS Modeling Tutorial

This tutorial is intended to be a "quick start" to creating simulations with GENESIS. It should give you the tools and enough information to let you quickly begin creating cells and networks with GENESIS, making use of the provided example simulations. Advanced topics are covered by appropriate links to the Advanced Tutorials on Realistic Neural Modeling.

Realistic Single Cell Modeling – from Experiment to Simulation

This tutorial gives a step by step explanation of how one uses experimental data to construct a biologically realistic multicompartmental model. Special emphasis is given on the many ways that this process can be imprecise. The tutorial is intended for both experimentalists who want to get into computer modeling and for computer scientists who use abstract neural network models but are curious about biological realistic modeling. The tutorial is not dependent on the use of a specific simulation engine, but rather covers the kind of data needed for constructing a model, how they are used, and potential pitfalls in the process.

Teaching Cognitive Modeling Using PDP++

PDP++ is a freely available, open source software package designed to support the development, simulation, and analysis of research-grade connectionist models of cognitive processes. It supports most popular parallel distributed processing paradigms and artificial neural network architectures, and it also provides an implementation of the LEABRA computational cognitive neuroscience framework. Models are typically constructed and examined using the PDP++ graphical user interface, but the system may also be extended through the incorporation of user-written C++ code. This article briefly reviews the features of PDP++, focusing on its utility for teaching cognitive modeling concepts and skills to university undergraduate and graduate students. An informal evaluation of the software as a pedagogical tool is provided, based on the author’s classroom experiences at three research universities and several conference-hosted tutorials.

Kinetic modeling of sodium in the lunar exosphere

Our knowledge about the lunar environment is based on a large volume of ground-based, remote, and in situ observations. These observations have been conducted at different times and sampled different pieces of such a complex system as the surface-bound exosphere of the Moon. Numerical modeling is the tool that can link results of these separate observations into a single picture. Being validated against previous measurements, models can be used for predictions and interpretation of future observations results. In this paper we present a kinetic model of the sodium exosphere of the Moon as well as results of its validation against a set of ground-based and remote observations. The unique characteristic of the model is that it takes the orbital motion of the Moon and the Earth into consideration and simulates both the exosphere as well as the sodium tail self-consistently. The extended computational domain covers the part of the Earth’s orbit at new Moon, which allows us to study the effect of Earth’s gravity on the lunar sodium tail. The model is fitted to a set of ground-based and remote observations by tuning sodium source rate as well as values of sticking, and accommodation coefficients. The best agreement of the model results with the observations is reached when all sodium atoms returning from the exosphere stick to the surface and the net sodium escape rate is about 5.3 × 1022 s−1.

From transient to steady state deformation and grain size: A thermodynamic approach using elasto-visco-plastic numerical modeling

Numerical simulation experiments give insight into the evolving energy partitioning during high-strain torsion experiments of calcite. Our numerical experiments are designed to derive a generic macroscopic grain size sensitive flow law capable of describing the full evolution from the transient regime to steady state. The transient regime is crucial for understanding the importance of micro structural processes that may lead to strain localization phenomena in deforming materials. This is particularly important in geological and geodynamic applications where the phenomenon of strain localization happens outside the time frame that can be observed under controlled laboratory conditions. Ourmethod is based on an extension of the paleowattmeter approach to the transient regime. We add an empirical hardening law using the Ramberg-Osgood approximation and assess the experiments by an evolution test function of stored over dissipated energy (lambda factor). Parameter studies of, strain hardening, dislocation creep parameter, strain rates, temperature, and lambda factor as well asmesh sensitivity are presented to explore the sensitivity of the newly derived transient/steady state flow law. Our analysis can be seen as one of the first steps in a hybrid computational-laboratory-field modeling workflow. The analysis could be improved through independent verifications by thermographic analysis in physical laboratory experiments to independently assess lambda factor evolution under laboratory conditions.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Benchmark problems for reactive transport modeling of the generation and attenuation of acid rock drainage

Acid rock drainage (ARD) is a problem of international relevance with substantial environmental and economic implications. Reactive transport modeling has proven a powerful tool for the process-based assessment of metal release and attenuation at ARD sites. Although a variety of models has been used to investigate ARD, a systematic model intercomparison has not been conducted to date. This contribution presents such a model intercomparison involving three synthetic benchmark problems designed to evaluate model results for the most relevant processes at ARD sites. The first benchmark (ARD-B1) focuses on the oxidation of sulfide minerals in an unsaturated tailing impoundment, affected by the ingress of atmospheric oxygen. ARD-B2 extends the first problem to include pH buffering by primary mineral dissolution and secondary mineral precipitation. The third problem (ARD-B3) in addition considers the kinetic and pH-dependent dissolution of silicate minerals under low pH conditions. The set of benchmarks was solved by four reactive transport codes, namely CrunchFlow, Flotran, HP1, and MIN3P. The results comparison focused on spatial profiles of dissolved concentrations, pH and pE, pore gas composition, and mineral assemblages. In addition, results of transient profiles for selected elements and cumulative mass loadings were considered in the intercomparison. Despite substantial differences in model formulations, very good agreement was obtained between the various codes. Residual deviations between the results are analyzed and discussed in terms of their implications for capturing system evolution and long-term mass loading predictions.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

Modeling asteroid collisions and impact processes

As a complement to experimental and theoretical approaches, numerical modeling has become an important component to study asteroid collisions and impact processes. In the last decade, there have been significant advances in both computational resources and numerical methods. We discuss the present state-of-the-art numerical methods and material models used in "shock physics codes" to simulate impacts and collisions and give some examples of those codes. Finally, recent modeling studies are presented, focussing on the effects of various material properties and target structures on the outcome of a collision.

An integrated molecular biology and computational approach to CYP1A1 expression in human brain

The cytochromes P450 comprise a superfamily of heme-containing mono-oxygenases. These enzymes metabolize numerous xenobiotics, but also play a role in metabolism of endogenous compounds. The P450 1A1 enzyme generally metabolizes polycyclic aromatic hydrocarbons, and its expression can be induced by aryl hydrocarbon receptor (AhR) activation. CYP1A1 is an exception to the generality that the majority of CYPs demonstrate highest expression in liver; CYP1Al is present in numerous extrahepatic tissues, including brain. This P450 has been observed in two forms, wildtype (WT) and brain variant (BV), arising from alternatively spliced mRNA transcripts. The CYP1A1 BV mRNA presented an exon deletion and was detected in human brain but not liver tissue of the same individuals. ^ Quantitative PCR analyses were performed to determine CYP1A1 WT and BV transcript expression levels in normal, bipolar disorder or schizophrenic groups. In our samples, we show that CYP1A1 BV mRNA, when present, is found alongside the full-length form. Furthermore, we demonstrate a significant decrease in expression of CYP1A1 in patients with bipolar disorder or schizophrenia. The expression level was not influenced by post-mortem interval, tissue pH, age, tobacco use, or lifetime antipsychotic medication load. ^ There is no indication of increased brain CYP1A1 expression in normal smokers versus non-smokers in these samples. We observed slightly increased CYP1A1 expression only in bipolar and schizophrenic smokers versus non-smokers. This may be indicative of complex interactions between neuronal chemical environments and AhR-mediated CYP1A1 induction in brain. ^ Structural homology modeling demonstrated that P450 1A1 BV has several alterations to positions/orientations of substrate recognition site residues compared to the WT isoform. Automated substrate docking was employed to investigate the potential binding of neurological signaling molecules and neurotropic drugs, as well as to differentiate specificities of the two P450 1A1 isoforms. We consistently observed that the BV isoform produced energetically favorable substrate dockings in orientations not observed for the same substrate in the WT isoform. These results demonstrated that structural differences, namely an expanded substrate access channel and active site, confer greater capacity for unique compound docking positions suggesting a metabolic profile distinct from the wildtype form for these test compounds. ^

Modeling Techniques for the High-Resolution Interpretation of Cryo-Electron Microscopy Reconstructions

Essential biological processes are governed by organized, dynamic interactions between multiple biomolecular systems. Complexes are thus formed to enable the biological function and get dissembled as the process is completed. Examples of such processes include the translation of the messenger RNA into protein by the ribosome, the folding of proteins by chaperonins or the entry of viruses in host cells. Understanding these fundamental processes by characterizing the molecular mechanisms that enable then, would allow the (better) design of therapies and drugs. Such molecular mechanisms may be revealed trough the structural elucidation of the biomolecular assemblies at the core of these processes. Various experimental techniques may be applied to investigate the molecular architecture of biomolecular assemblies. High-resolution techniques, such as X-ray crystallography, may solve the atomic structure of the system, but are typically constrained to biomolecules of reduced flexibility and dimensions. In particular, X-ray crystallography requires the sample to form a three dimensional (3D) crystal lattice which is technically di‑cult, if not impossible, to obtain, especially for large, dynamic systems. Often these techniques solve the structure of the different constituent components within the assembly, but encounter difficulties when investigating the entire system. On the other hand, imaging techniques, such as cryo-electron microscopy (cryo-EM), are able to depict large systems in near-native environment, without requiring the formation of crystals. The structures solved by cryo-EM cover a wide range of resolutions, from very low level of detail where only the overall shape of the system is visible, to high-resolution that approach, but not yet reach, atomic level of detail. In this dissertation, several modeling methods are introduced to either integrate cryo-EM datasets with structural data from X-ray crystallography, or to directly interpret the cryo-EM reconstruction. Such computational techniques were developed with the goal of creating an atomic model for the cryo-EM data. The low-resolution reconstructions lack the level of detail to permit a direct atomic interpretation, i.e. one cannot reliably locate the atoms or amino-acid residues within the structure obtained by cryo-EM. Thereby one needs to consider additional information, for example, structural data from other sources such as X-ray crystallography, in order to enable such a high-resolution interpretation. Modeling techniques are thus developed to integrate the structural data from the different biophysical sources, examples including the work described in the manuscript I and II of this dissertation. At intermediate and high-resolution, cryo-EM reconstructions depict consistent 3D folds such as tubular features which in general correspond to alpha-helices. Such features can be annotated and later on used to build the atomic model of the system, see manuscript III as alternative. Three manuscripts are presented as part of the PhD dissertation, each introducing a computational technique that facilitates the interpretation of cryo-EM reconstructions. The first manuscript is an application paper that describes a heuristics to generate the atomic model for the protein envelope of the Rift Valley fever virus. The second manuscript introduces the evolutionary tabu search strategies to enable the integration of multiple component atomic structures with the cryo-EM map of their assembly. Finally, the third manuscript develops further the latter technique and apply it to annotate consistent 3D patterns in intermediate-resolution cryo-EM reconstructions. The first manuscript, titled An assembly model for Rift Valley fever virus, was submitted for publication in the Journal of Molecular Biology. The cryo-EM structure of the Rift Valley fever virus was previously solved at 27Å-resolution by Dr. Freiberg and collaborators. Such reconstruction shows the overall shape of the virus envelope, yet the reduced level of detail prevents the direct atomic interpretation. High-resolution structures are not yet available for the entire virus nor for the two different component glycoproteins that form its envelope. However, homology models may be generated for these glycoproteins based on similar structures that are available at atomic resolutions. The manuscript presents the steps required to identify an atomic model of the entire virus envelope, based on the low-resolution cryo-EM map of the envelope and the homology models of the two glycoproteins. Starting with the results of the exhaustive search to place the two glycoproteins, the model is built iterative by running multiple multi-body refinements to hierarchically generate models for the different regions of the envelope. The generated atomic model is supported by prior knowledge regarding virus biology and contains valuable information about the molecular architecture of the system. It provides the basis for further investigations seeking to reveal different processes in which the virus is involved such as assembly or fusion. The second manuscript was recently published in the of Journal of Structural Biology (doi:10.1016/j.jsb.2009.12.028) under the title Evolutionary tabu search strategies for the simultaneous registration of multiple atomic structures in cryo-EM reconstructions. This manuscript introduces the evolutionary tabu search strategies applied to enable a multi-body registration. This technique is a hybrid approach that combines a genetic algorithm with a tabu search strategy to promote the proper exploration of the high-dimensional search space. Similar to the Rift Valley fever virus, it is common that the structure of a large multi-component assembly is available at low-resolution from cryo-EM, while high-resolution structures are solved for the different components but lack for the entire system. Evolutionary tabu search strategies enable the building of an atomic model for the entire system by considering simultaneously the different components. Such registration indirectly introduces spatial constrains as all components need to be placed within the assembly, enabling the proper docked in the low-resolution map of the entire assembly. Along with the method description, the manuscript covers the validation, presenting the benefit of the technique in both synthetic and experimental test cases. Such approach successfully docked multiple components up to resolutions of 40Å. The third manuscript is entitled Evolutionary Bidirectional Expansion for the Annotation of Alpha Helices in Electron Cryo-Microscopy Reconstructions and was submitted for publication in the Journal of Structural Biology. The modeling approach described in this manuscript applies the evolutionary tabu search strategies in combination with the bidirectional expansion to annotate secondary structure elements in intermediate resolution cryo-EM reconstructions. In particular, secondary structure elements such as alpha helices show consistent patterns in cryo-EM data, and are visible as rod-like patterns of high density. The evolutionary tabu search strategy is applied to identify the placement of the different alpha helices, while the bidirectional expansion characterizes their length and curvature. The manuscript presents the validation of the approach at resolutions ranging between 6 and 14Å, a level of detail where alpha helices are visible. Up to resolution of 12 Å, the method measures sensitivities between 70-100% as estimated in experimental test cases, i.e. 70-100% of the alpha-helices were correctly predicted in an automatic manner in the experimental data. The three manuscripts presented in this PhD dissertation cover different computation methods for the integration and interpretation of cryo-EM reconstructions. The methods were developed in the molecular modeling software Sculptor (http://sculptor.biomachina.org) and are available for the scientific community interested in the multi-resolution modeling of cryo-EM data. The work spans a wide range of resolution covering multi-body refinement and registration at low-resolution along with annotation of consistent patterns at high-resolution. Such methods are essential for the modeling of cryo-EM data, and may be applied in other fields where similar spatial problems are encountered, such as medical imaging.