Highly Parallel Geometric Characterization and Visualization of Volumetric Data Sets

Volumetric 3D data sets are being generated in many different application areas. Some examples are CAT scans and MRI data, 3D models of protein molecules represented by implicit surfaces, multi-dimensional numeric simulations of plasma turbulence, and stacks of confocal microscopy images of cells. The size of these data sets has been increasing, requiring the speed of analysis and visualization techniques to also increase to keep up. Recent advances in processor technology have stopped increasing clock speed and instead begun increasing parallelism, resulting in multi-core CPUS and many-core GPUs. To take advantage of these new parallel architectures, algorithms must be explicitly written to exploit parallelism. In this thesis we describe several algorithms and techniques for volumetric data set analysis and visualization that are amenable to these modern parallel architectures. We first discuss modeling volumetric data with Gaussian Radial Basis Functions (RBFs). RBF representation of a data set has several advantages, including lossy compression, analytic differentiability, and analytic application of Gaussian blur. We also describe a parallel volume rendering algorithm that can create images of the data directly from the RBF representation. Next we discuss a parallel, stochastic algorithm for measuring the surface area of volumetric representations of molecules. The algorithm is suitable for implementation on a GPU and is also progressive, allowing it to return a rough answer almost immediately and refine the answer over time to the desired level of accuracy. After this we discuss the concept of Confluent Visualization, which allows the visualization of the interaction between a pair of volumetric data sets. The interaction is visualized through volume rendering, which is well suited to implementation on parallel architectures. Finally we discuss a parallel, stochastic algorithm for classifying stem cells as having been grown on a surface that induces differentiation or on a surface that does not induce differentiation. The algorithm takes as input 3D volumetric models of the cells generated from confocal microscopy. This algorithm builds on our algorithm for surface area measurement and, like that algorithm, this algorithm is also suitable for implementation on a GPU and is progressive

Geometric algorithms for cavity detection on protein surfaces

Macromolecular structures such as proteins heavily empower cellular processes or functions. These biological functions result from interactions between proteins and peptides, catalytic substrates, nucleotides or even human-made chemicals. Thus, several interactions can be distinguished: protein-ligand, protein-protein, protein-DNA, and so on. Furthermore, those interactions only happen under chemical- and shapecomplementarity conditions, and usually take place in regions known as binding sites. Typically, a protein consists of four structural levels. The primary structure of a protein is made up of its amino acid sequences (or chains). Its secondary structure essentially comprises -helices and -sheets, which are sub-sequences (or sub-domains) of amino acids of the primary structure. Its tertiary structure results from the composition of sub-domains into domains, which represent the geometric shape of the protein. Finally, the quaternary structure of a protein results from the aggregate of two or more tertiary structures, usually known as a protein complex. This thesis fits in the scope of structure-based drug design and protein docking. Specifically, one addresses the fundamental problem of detecting and identifying protein cavities, which are often seen as tentative binding sites for ligands in protein-ligand interactions. In general, cavity prediction algorithms split into three main categories: energy-based, geometry-based, and evolution-based. Evolutionary methods build upon evolutionary sequence conservation estimates; that is, these methods allow us to detect functional sites through the computation of the evolutionary conservation of the positions of amino acids in proteins. Energy-based methods build upon the computation of interaction energies between protein and ligand atoms. In turn, geometry-based algorithms build upon the analysis of the geometric shape of the protein (i.e., its tertiary structure) to identify cavities. This thesis focuses on geometric methods. We introduce here three new geometric-based algorithms for protein cavity detection. The main contribution of this thesis lies in the use of computer graphics techniques in the analysis and recognition of cavities in proteins, much in the spirit of molecular graphics and modeling. As seen further ahead, these techniques include field-of-view (FoV), voxel ray casting, back-face culling, shape diameter functions, Morse theory, and critical points. The leading idea is to come up with protein shape segmentation, much like we commonly do in mesh segmentation in computer graphics. In practice, protein cavity algorithms are nothing more than segmentation algorithms designed for proteins.Estruturas macromoleculares tais como as proteínas potencializam processos ou funções celulares. Estas funções resultam das interações entre proteínas e peptídeos, substratos catalíticos, nucleótideos, ou até mesmo substâncias químicas produzidas pelo homem. Assim, há vários tipos de interacções: proteína-ligante, proteína-proteína, proteína-DNA e assim por diante. Além disso, estas interações geralmente ocorrem em regiões conhecidas como locais de ligação (binding sites, do inglês) e só acontecem sob condições de complementaridade química e de forma. É também importante referir que uma proteína pode ser estruturada em quatro níveis. A estrutura primária que consiste em sequências de aminoácidos (ou cadeias), a estrutura secundária que compreende essencialmente por hélices e folhas , que são subsequências (ou subdomínios) dos aminoácidos da estrutura primária, a estrutura terciária que resulta da composição de subdomínios em domínios, que por sua vez representa a forma geométrica da proteína, e por fim a estrutura quaternária que é o resultado da agregação de duas ou mais estruturas terciárias. Este último nível estrutural é frequentemente conhecido por um complexo proteico. Esta tese enquadra-se no âmbito da conceção de fármacos baseados em estrutura e no acoplamento de proteínas. Mais especificamente, aborda-se o problema fundamental da deteção e identificação de cavidades que são frequentemente vistos como possíveis locais de ligação (putative binding sites, do inglês) para os seus ligantes (ligands, do inglês). De forma geral, os algoritmos de identificação de cavidades dividem-se em três categorias principais: baseados em energia, geometria ou evolução. Os métodos evolutivos baseiam-se em estimativas de conservação das sequências evolucionárias. Isto é, estes métodos permitem detectar locais funcionais através do cálculo da conservação evolutiva das posições dos aminoácidos das proteínas. Em relação aos métodos baseados em energia estes baseiam-se no cálculo das energias de interação entre átomos da proteína e do ligante. Por fim, os algoritmos geométricos baseiam-se na análise da forma geométrica da proteína para identificar cavidades. Esta tese foca-se nos métodos geométricos. Apresentamos nesta tese três novos algoritmos geométricos para detecção de cavidades em proteínas. A principal contribuição desta tese está no uso de técnicas de computação gráfica na análise e reconhecimento de cavidades em proteínas, muito no espírito da modelação e visualização molecular. Como pode ser visto mais à frente, estas técnicas incluem o field-of-view (FoV), voxel ray casting, back-face culling, funções de diâmetro de forma, a teoria de Morse, e os pontos críticos. A ideia principal é segmentar a proteína, à semelhança do que acontece na segmentação de malhas em computação gráfica. Na prática, os algoritmos de detecção de cavidades não são nada mais que algoritmos de segmentação de proteínas

Geometric modeling, simulation, and visualization methods for plasmid DNA molecules

Plasmid DNA molecules are a special type of DNA molecules that are used, among other applications, in DNA vaccination and gene therapy. These molecules are characterized by, when in their natural state, presenting a closed-circular conformation and by being supercoiled. The production of plasmid DNA using bacteria as hosts implies a purification step where the plasmid DNA molecules are separated from the DNA of the host and other contaminants. This purification process, and all the physical and chemical variations involved, such as temperature changes, may affect the plasmid DNA molecules conformation by uncoiling or even by open them, which makes them useless for therapeutic applications. Because of that, researchers are always searching for new purification techniques that maximize the amount of supercoiled plasmid DNA that is produced. Computer simulations and 3D visualization of plasmid DNA can bring many advantages because they allow researchers to actually see what can happen to the molecules under certain conditions. In this sense, it was necessary to develop reliable and accurate geometric models specific for plasmid DNA simulations. This dissertation presents a new assembling algorithm for B-DNA specifically developed for plasmid DNA assembling. This new assembling algorithm is completely adaptive in the sense that it allows researchers to assemble any plasmid DNA base-pair sequence along any arbitrary conformation that fits the length of the plasmid DNA molecule. This is specially suitable for plasmid DNA simulations, where conformations are generated by simulation procedures and there is the need to assemble the given base-pair sequence over that conformation, what can not be done by conventional predictive DNA assembling methods. Unlike traditional molecular visualization methods that are based on the atomic structure, this new assembling algorithm uses color coded 3D molecular surfaces of the nucleotides as the building blocks for DNA assembling. This new approach, not only reduces the amount of graphical objects and, consequently, makes the rendering faster, but also makes it easier to visually identify the nucleotides in the DNA strands. The algorithm used to triangulate the molecular surfaces of the nucleotides building blocks is also a novelty presented as part of this dissertation. This new triangulation algorithm for Gaussian molecular surfaces introduces a new mechanism that divides the atomic structure of molecules into boxes and spheres. This new space division method is faster because it confines the local calculation of the molecular surface to a specific region of influence of the atomic structure, not taking into account atoms that do not influence the triangulation of the molecular surface in that region. This new method also guarantees the continuity of the molecular surface. Having in mind that the aim of this dissertation is to present a complete set of methods for plasmid DNA visualization and simulation, it is also proposed a new deformation algorithm to be used for plasmid DNA Monte Carlo simulations. This new deformation algorithm uses a 3D polyline to represent the plasmid DNA conformation and performs small deformations on that polyline, keeping the segments length and connectivity. Experiments have been performed in order to compare this new deformation method with deformation methods traditionally used by Monte Carlo plasmid DNA simulations These experiments shown that the new method is more efficient in the sense that its trial acceptance ratio is higher and it converges sooner and faster to the elastic energy equilibrium state of the plasmid DNA molecule. In sum, this dissertation successfully presents an end-to-end set of models and algorithms for plasmid DNA geometric modelling, visualization and simulation

Implicit muscle models for interactive character skinning

En animation de personnages 3D, la déformation de surface, ou skinning, est une étape cruciale. Son rôle est de déformer la représentation surfacique d'un personnage pour permettre son rendu dans une succession de poses spécifiées par un animateur. La plausibilité et la qualité visuelle du résultat dépendent directement de la méthode de skinning choisie. Sa rapidité d'exécution et sa simplicité d'utilisation sont également à prendre en compte pour rendre possible son usage interactif lors des sessions de production des artistes 3D. Les différentes méthodes de skinning actuelles se divisent en trois catégories. Les méthodes géométriques sont rapides et simples d'utilisation, mais leur résultats manquent de plausibilité. Les approches s'appuyant sur des exemples produisent des résultats réalistes, elles nécessitent en revanche une base de données d'exemples volumineuse, et le contrôle de leur résultat est fastidieux. Enfin, les algorithmes de simulation physique sont capables de modéliser les phénomènes dynamiques les plus complexes au prix d'un temps de calcul souvent prohibitif pour une utilisation interactive. Les travaux décrits dans cette thèse s'appuient sur Implicit Skinning, une méthode géométrique corrective utilisant une représentation implicite des surfaces, qui permet de résoudre de nombreux problèmes rencontrés avec les méthodes géométriques classiques, tout en gardant des performances permettant son usage interactif. La contribution principale de ces travaux est un modèle d'animation qui prend en compte les effets des muscles des personnages et de leur interactions avec d'autres éléments anatomiques, tout en bénéficiant des avantages apportés par Implicit Skinning. Les muscles sont représentés par une surface d'extrusion le long d'axes centraux. Les axes des muscles sont contrôlés par une méthode de simulation physique simplifiée. Cette représentation permet de modéliser les collisions des muscles entre eux et avec les os, d'introduire des effets dynamiques tels que rebonds et secousses, tout en garantissant la conservation du volume, afin de représenter le comportement réel des muscles. Ce modèle produit des déformations plus plausibles et dynamiques que les méthodes géométriques de l'état de l'art, tout en conservant des performances suffisantes pour permettre son usage dans une session d'édition interactive. Elle offre de plus aux infographistes un contrôle intuitif sur la forme des muscles pour que les déformations obtenues se conforment à leur vision artistique.Surface deformation, or skinning is a crucial step in 3D character animation. Its role is to deform the surface representation of a character to be rendered in the succession of poses specified by an animator. The quality and plausiblity of the displayed results directly depends on the properties of the skinning method. However, speed and simplicity are also important criteria to enable their use in interactive editing sessions. Current skinning methods can be divided in three categories. Geometric methods are fast and simple to use, but their results lack plausibility. Example-based approaches produce realistic results, yet they require a large database of examples while remaining tedious to edit. Finally, physical simulations can model the most complex dynamical phenomena, but at a very high computational cost, making their interactive use impractical. The work presented in this thesis are based on, Implicit Skinning, is a corrective geometric approach using implicit surfaces to solve many issues of standard geometric skinning methods, while remaining fast enough for interactive use. The main contribution of this work is an animation model that adds anatomical plausibility to a character by representing muscle deformations and their interactions with other anatomical features, while benefiting from the advantages of Implicit Skinning. Muscles are represented by an extrusion surface along a central axis. These axes are driven by a simplified physics simulation method, introducing dynamic effects, such as jiggling. The muscle model guarantees volume conservation, a property of real-life muscles. This model adds plausibility and dynamics lacking in state-of-the-art geometric methods at a moderate computational cost, which enables its interactive use. In addition, it offers intuitive shape control to animators, enabling them to match the results with their artistic vision

The use of molecular dynamics simulation for the study of polymeric and lipid based drug delivery systems

Systemic administration is the conventional method for administrating drugs. Following injection, ideally, we wish the drug only to locate to the target tissue, however, this is not what occurs; the drug molecules rather distribute throughout the entire body via the blood stream. Regarding some drugs, in particular chemotherapy agents, this often leads to severe dose limiting side effects and unsatisfactory therapeutic results. On the other hand, many drugs as is also the case for the chemotherapy agents, demonstrate low aqueous solubility and suboptimal pharmacokinetic properties. These problems all necessitate the use of drug delivery systems (DDSs) as they decrease the side effects of drugs while also improving drug bioavailability and pharmacokinetics. Although there are different varieties of DDSs, we have focused on those categorized as polymeric or lipidic. Depending on the drug to be delivered and site of action of the drug, polymeric DDSs can be used either locally or systemically. Hydrogels and electrospun polymer fibers are two examples of polymeric DDSs that are used for the local delivery of many drugs, including antibiotics and anticancer drugs. The other form of polymeric DDSs are nanoparticles that are capable of carrying and in some cases targeting drug molecules. These polymeric DDSs are generally injected into the blood stream to reach their target site. Lipidic DDSs mainly are used in the form of nanoparticles that, depending on their lipid composition and method of preparation, would have different characteristics. Liposomes and solid lipid nanoparticles are two examples of lipidic DDSs. Despite the huge number of publications regarding the use of nanoparticles as DDSs, the number of approved drug therapies that make use of nanoparticle-based delivery systems still remains small. One of the reasons for this problem is that formulations of DDSs are complicated and difficult to optimize. Drug delivery systems should be further redesigned and optimized, however, this has proved challenging due to intrinsic and practical experimental limitations. For example, it is difficult to experimentally elucidate the reason many DDSs show promise in vitro but fail in vivo. The limitations to the extent to which mechanistic insight can be gained from experiments regarding DDSs can be compensated by computational molecular modelling techniques that provide detailed information on molecular interactions of drugs and carriers. The insights obtained by the studies performed in this thesis can be used to improve the design of DDSs. In this thesis, two polymeric (studies I and IV) and two lipidic (studies II and III) DDSs were studied by all-atom molecular dynamics (MD) simulations. In each of these studies, a specific property of the DDS was evaluated in detail. These properties are drug release profile (study I), stability (study II), pH-sensitivity (study III) and size (study IV). We evaluated these properties through investigation of the three varieties of interactions DDSs have: interactions of DDSs with the loaded drug, interactions among the components of DDSs and interactions between the DDSs and the medium, namely water and ions. While it is difficult to directly determine an accurate picture of these interactions experimentally at atomic scale resolution, all- atom MD simulation can provide insight into this.Lääkeaineet annostellaan yleensä systeemisesti ja olisi ideaalista, että annostelun jälkeen lääkeaine vaikuttaisi vain paikallisesti kohdekudoksessa. Käytännössä näin ei kuitenkaan tapahdu, vaan pikemminkin lääkeainemolekyylit jakautuvat koko kehoon verenkierron mukana. Joidenkin lääkkeiden, erityisesti kemoterapeuttisten aineiden kohdalla, tämä johtaa usein vakaviin annosta rajoittaviin sivuvaikutuksiin ja näin ollen epätyydyttäviin terapeuttisiin tuloksiin. Toisaalta monilla lääkkeillä, kuten myös kemoterapia-aineilla, on myös alhainen vesiliukoisuus ja huonot farmakokineettiset ominaisuudet. Kaikki nämä ongelmat edellyttävät erilaisten lääkekuljetusjärjestelmien käyttöä, koska ne vähentävät esimerkiksi haitallisia sivuvaikutuksia ja parantavat lääkeaineiden biologista hyötyosuutta. Vaikka lääkekuljetusjärjestelmiä on erilaisia, olemme keskittyneet tässä väitöskirjassa vain niihin, jotka on luokiteltu polymeeri- tai lipidipohjaisiksi. Kuljetettavasta lääkeaineesta ja lääkkeen vaikutuspaikasta riippuen polymeeripohjaisia lääkekuljetusjärjestelmiä voidaan käyttää paikallisesti tai systeemisesti. Hydrogeelit ja sähkökehrätyt polymeerikalvot ovat esimerkkejä tällaisista lääkekuljetusjärjestelmistä ja niitä käytetään monien lääkkeiden, kuten antibioottien ja syöpälääkkeiden paikalliseen annosteluun. Polymeeripohjaiset nanohiukkaset pystyvät vuorostaan kuljettamaan ja joissakin tapauksissa myös kohdentamaan lääkeainemolekyylejä. Nanohiukkaset ruiskutetaan yleensä suoraan verenkiertoon, jotta ne saavuttaisivat terapeuttisen kohteen. Lipideistä koostuvat lääkekuljetusjärjestelmät ovat pääasiassa nanohiukkasia, joilla on lipidikoostumuksesta ja valmistusmenetelmästä johtuen erilaisia ominaisuuksia. Liposomit ja kiinteät lipidinanohiukkaset ovat esimerkkejä lääkeaineiden kuljetusjärjestelmistä, jotka pohjautuvat rasva-aineisiin eli lipideihin. Siitä huolimatta, että kirjallisuudesta löytyy valtava määrä tieteellisiä julkaisuja, jotka liittyvät nanohiukkasten käyttöön lääkekuljetusjärjestelminä, hyväksyttyjen nanohiukkaspohjaisten lääkehoitomuotojen määrä on edelleen pieni. Tämä johtuu siitä, että valmisteet ovat monimutkaisia, vaikeasti optimoitavissa. Nanohiukkasia tulisi edelleen suunnitella ja optimoida, mutta tämä on osoittautunut haastavaksi mittalaitteiden rajoituksien vuoksi. Esimerkiksi on erittäin vaikeaa selvittää kokeellisesti, miksi monet nanohiukkaset ovat lupaavia in vitro mittauksissa, mutta epäonnistuvat in vivo kokeissa. Kokeellisia mittauksia, joissa nanohiukkasista saadaan mekanistista tietoa, voidaan kompensoida erilaisilla in silico molekyylimallinnustekniikoilla, jotka tarjoavat yksityiskohtaista tietoa lääkeaineiden ja kantajien molekyylivuorovaikutuksista. Väitöskirjassa esitettyjä tuloksia voidaan hyödyntää lääkeaineiden kuljetusjärjestelmien suunnittelussa. Tässä väitöskirjassa tutkittiin kahta polymeereistä (tutkimukset I ja IV) ja kahta lipideistä (tutkimukset II ja III) koostuvaa lääkekuljetusjärjestelmää hyödyntäen atomistisia molekyylidynamiikka simulaatioita. Jokaisessa tutkimuksessa lääkekuljetusjärjestelmän tietty ominaisuus arvioitiin yksityiskohtaisesti. Näitä ominaisuuksia olivat lääkeaineen vapautumisprofiili (tutkimus I), stabiilius (tutkimus II), pH-herkkyys (tutkimus III) ja koko (tutkimus IV). Arvioimme näitä ominaisuuksia tutkimalla kolmea erilaista vuorovaikutusta lääkekuljetusjärjestelmissä: matriisin vuorovaikutus ladatun lääkkeen kanssa, matriisin eri komponenttien vuorovaikutus keskenään sekä vuorovaikutus lääkekuljetusjärjestelmän ja väliaineen (vesi ja ionit) välillä. Siitä huolimatta, että kokeellisilla mittauksilla on erittäin vaikeaa tuottaa atomitason kuva näistä vuorovaikutuksista, voidaan näistä saada laskennallisia molekyylidynamiikkasimulaatioita hyödyntäen hyvä käsitys

On deep generative modelling methods for protein-protein interaction

Proteins form the basis for almost all biological processes, identifying the interactions that proteins have with themselves, the environment, and each other are critical to understanding their biological function in an organism, and thus the impact of drugs designed to affect them. Consequently a significant body of research and development focuses on methods to analyse and predict protein structure and interactions. Due to the breadth of possible interactions and the complexity of structures, \textit{in sillico} methods are used to propose models of both interaction and structure that can then be verified experimentally. However the computational complexity of protein interaction means that full physical simulation of these processes requires exceptional computational resources and is often infeasible. Recent advances in deep generative modelling have shown promise in correctly capturing complex conditional distributions. These models derive their basic principles from statistical mechanics and thermodynamic modelling. While the learned functions of these methods are not guaranteed to be physically accurate, they result in a similar sampling process to that suggested by the thermodynamic principles of protein folding and interaction. However, limited research has been applied to extending these models to work over the space of 3D rotation, limiting their applicability to protein models. In this thesis we develop an accelerated sampling strategy for faster sampling of potential docking locations, we then address the rotational diffusion limitation by extending diffusion models to the space of $SO(3)$ and finally present a framework for the use of this rotational diffusion model to rigid docking of proteins

Facilitation of Nanoscale Thermal Transport by Hydrogen Bonds

Thermal transport performance at the nanoscale and/or of biomaterials is essential to the success of many new technologies including nanoelectronics, biomedical devices, and various nanocomposites. Due to complicated microstructures and chemical bonding, thermal transport process in these materials has not been well understood yet. In terms of chemical bonding, it is well known that the strength of atomic bonding can significantly affect thermal transport across materials or across interfaces between materials. Given the intrinsic high strength of hydrogen bonds, this dissertation explores the role of hydrogen bonds in nanoscale thermal transport in various materials, and investigates novel material designs incorporating hydrogen bonds for drastically enhanced thermal conduction. Molecular dynamics simulation is employed to study thermal transport processes in three representative hydrogen-bonded materials: (1) crystalline motifs of the spider silk, silkworm silk and synthetic silk, (2) crystalline polymer nanofibers, and (3) polymer nanocomposites incorporating graphene or functionalized graphene. Computational and theoretical investigations demonstrate that hydrogen bonds significantly facilitate thermal transport in all three material systems. The underlying molecular mechanisms are systematically investigated. The results will not only contribute new physical insights, but also provide novel concepts of materials design to improve thermal properties towards a wide range of applications

DOUBLE EMULSIONS RELEVANT TO FOOD SYSTEMS: FABRICATION, CHARACTERIZATION, AND APPLICATIONS

Systems of the W/O/W type continue to be of growing interest to food scientists and technologists for two main reasons. First, there is the possibility of preparing a reduced-fat emulsion product by the replacement of a conventional oil-in-water emulsion by the equivalent W/O/W emulsion having a lower actual oil content but a similar in-mouth perceived texture. Second, there is the capability to encapsulate and protect a sensitive hydrophilic bioactive compound. However, it is always a bottleneck to obtain robust formulations using economically viable ingredients and processing operations. To improve its performance for practical applications in food production, efforts have been made to overcome the difficulties. Herein, we examined the impact of crystallizing the oil phase on the ability of W/O/W emulsions to encapsulate and retain a model hydrophilic bioactive. The results showed that the texture and stability of the W/O/W emulsions prepared in this study were highly dependent on their composition. The concentrations of both hydrophilic and hydrophobic stabilizers had to be optimized to produce emulsions that contained relatively small droplets, (initially) had relatively low viscosities, and that were stable to coalescence and phase separation. Our results provide some useful insights into the formulation of double emulsions for the encapsulation of hydrophilic bioactive agents. In addition, two novel applications of double emulsion have been investigated to provide insight for food production market. One is to protect natural color (anthocyanin) from pH-induced color changes, the other one is to study the impact of fat crystallization on the resistance to osmotic stress, which could be a potential for temperature-triggered release