Interactive Visualization of Molecular Dynamics Simulation Data

Molecular Dynamics Simulations (MD) plays an essential role in the field of computational biology. The simulations produce extensive high-dimensional, spatio-temporal data describ-ing the motion of atoms and molecules. A central challenge in the field is the extraction and visualization of useful behavioral patterns from these simulations. Throughout this thesis, I collaborated with a computational biologist who works on Molecular Dynamics (MD) Simu-lation data. For the sake of exploration, I was provided with a large and complex membrane simulation. I contributed solutions to his data challenges by developing a set of novel visual-ization tools to help him get a better understanding of his simulation data. I employed both scientific and information visualization, and applied concepts of abstraction and dimensions projection in the proposed solutions. The first solution enables the user to interactively fil-ter and highlight dynamic and complex trajectory constituted by motions of molecules. The molecular dynamic trajectories are identified based on path length, edge length, curvature, and normalized curvature, and their combinations. The tool exploits new interactive visual-ization techniques and provides a combination of 2D-3D path rendering in a dual dimension representation to highlight differences arising from the 2D projection on a plane. The sec-ond solution introduces a novel abstract interaction space for Protein-Lipid interaction. The proposed solution addresses the challenge of visualizing complex, time-dependent interactions between protein and lipid molecules. It also proposes a fast GPU-based implementation that maps lipid-constituents involved in the interaction onto the abstract protein interaction space. I also introduced two abstract level-of-detail (LoD) representations with six levels of detail for lipid molecules and protein interaction. Finally, I proposed a novel framework consisting of four linked views: A time-dependent 3D view, a novel hybrid view, a clustering timeline, and a details-on-demand window. The framework exploits abstraction and projection to enable the user to study the molecular interaction and the behavior of the protein-protein interaction and clusters. I introduced a selection of visual designs to convey the behavior of protein-lipid interaction and protein-protein interaction through a unified coordinate system. Abstraction is used to present proteins in hybrid 2D space, and a projected tiled space is used to present both Protein-Lipid Interaction (PLI) and Protein-Protein Interaction (PPI) at the particle level in a heat-map style visual design. Glyphs are used to represent PPI at the molecular level. I coupled visually separable visual designs in a unified coordinate space. The result lets the user study both PLI and PPI separately, or together in a unified visual analysis framework

Towards Predictive Rendering in Virtual Reality

The strive for generating predictive images, i.e., images representing radiometrically correct renditions of reality, has been a longstanding problem in computer graphics. The exactness of such images is extremely important for Virtual Reality applications like Virtual Prototyping, where users need to make decisions impacting large investments based on the simulated images. Unfortunately, generation of predictive imagery is still an unsolved problem due to manifold reasons, especially if real-time restrictions apply. First, existing scenes used for rendering are not modeled accurately enough to create predictive images. Second, even with huge computational efforts existing rendering algorithms are not able to produce radiometrically correct images. Third, current display devices need to convert rendered images into some low-dimensional color space, which prohibits display of radiometrically correct images. Overcoming these limitations is the focus of current state-of-the-art research. This thesis also contributes to this task. First, it briefly introduces the necessary background and identifies the steps required for real-time predictive image generation. Then, existing techniques targeting these steps are presented and their limitations are pointed out. To solve some of the remaining problems, novel techniques are proposed. They cover various steps in the predictive image generation process, ranging from accurate scene modeling over efficient data representation to high-quality, real-time rendering. A special focus of this thesis lays on real-time generation of predictive images using bidirectional texture functions (BTFs), i.e., very accurate representations for spatially varying surface materials. The techniques proposed by this thesis enable efficient handling of BTFs by compressing the huge amount of data contained in this material representation, applying them to geometric surfaces using texture and BTF synthesis techniques, and rendering BTF covered objects in real-time. Further approaches proposed in this thesis target inclusion of real-time global illumination effects or more efficient rendering using novel level-of-detail representations for geometric objects. Finally, this thesis assesses the rendering quality achievable with BTF materials, indicating a significant increase in realism but also confirming the remainder of problems to be solved to achieve truly predictive image generation

Interactive molecular docking with haptics and advanced graphics

Biomolecular interactions underpin many of the processes that make up life. Molecular docking is the study of these interactions in silico. Interactive docking applications put the user in control of the docking process, allowing them to use their knowledge and intuition to determine how molecules bind together. Interactive molecular docking applications often use haptic devices as a method of controlling the docking process. These devices allow the user to easily manipulate the structures in 3D space, whilst feeling the forces that occur in response to their manipulations. As a result of the force refresh rate requirements of haptic devices, haptic assisted docking applications are often limited, in that they model the interacting proteins as rigid, use low fidelity visualisations or require expensive propriety equipment to use. The research in this thesis aims to address some of these limitations. Firstly, the development of a visualisation algorithm capable of rendering a depiction of a deforming protein at an interactive refresh rate, with per-pixel shadows and ambient occlusion, is discussed. Then, a novel approach to modelling molecular flexibility whilst maintaining a stable haptic refresh rate is developed. Together these algorithms are presented within Haptimol FlexiDock, the first haptic-assisted molecular docking application to support receptor flexibility with high fidelity graphics, whilst also maintaining interactive refresh rates on both the haptic device and visual display. Using Haptimol FlexiDock, docking experiments were performed between two protein-ligand pairs: Maltodextrin Binding Protein and Maltose, and glutamine Binding Protein and Glucose. When the ligand was placed in its approximate binding site, the direction of over 80% of the intra-molecular movement aligned with that seen in the experimental structures. Furthermore, over 50% of the expected backbone motion was present in the structures generated with FlexiDock. Calculating the deformation of a biomolecule in real time, whilst maintaining an interactive refresh rate on the haptic device (> 500Hz) is a breakthrough in the field of interactive molecular docking, as, previous approaches either model protein flexibility, but fail to achieve the required haptic refresh rate, or do not consider biomolecular flexibility at all