Interactive, multiscale navigation of large and complicated biological networks

Motivation: Many types of omics data are compiled as lists of connections between elements and visualized as networks or graphs where the nodes and edges correspond to the elements and the connections, respectively. However, these networks often appear as ‘hair-balls’—with a large number of extremely tangled edges—and cannot be visually interpreted

Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex

Neural Networks and Dynamic Complex Systems

We describe the use of neural networks for optimization and inference associated with a variety of complex systems. We show how a string formalism can be used for parallel computer decomposition, message routing and sequential optimizing compilers. We extend these ideas to a general treatment of spatial assessment and distributed artificial intelligence

SciTech News Volume 70, No. 4 (2016)

Columns and Reports From the Editor 3 Division News Science-Technology Division 4 SLA Annual Meeting 2016 Report (S. Kirk Cabeen Travel Stipend Award recipient) 6 Reflections on SLA Annual Meeting (Diane K. Foster International Student Travel Award recipient) 8 SLA Annual Meeting Report (Bonnie Hilditch International Librarian Award recipient)10 Chemistry Division 12 Engineering Division 15 Reflections from the 2016 SLA Conference (SPIE Digital Library Student Travel Stipend recipient)15 Fundamentals of Knowledge Management and Knowledge Services (IEEE Continuing Education Stipend recipient) 17 Makerspaces in Libraries: The Big Table, the Art Studio or Something Else? (by Jeremy Cusker) 19 Aerospace Section of the Engineering Division 21 Reviews Sci-Tech Book News Reviews 22 Advertisements IEEE 17 WeBuyBooks.net 2

Advancing computational biophysics with Virtual Reality

Modelos computacionais são ferramentas poderosas para explorar as propriedades de sistemas biológicos complexos. Na neurociência computacional, permitir fácil exploração e visualização computacional desses modelos é crucial para o progresso do campo. Nos últimos anos, os sistemas de visualização 3D e o hardware de realidade virtual tornaram-se mais acessíveis e isso abre uma janela de oportunidade para os serviços de visualização. O principal problema atual da visualização 3D diz respeito à usabilidade (ou seja, navegação e seleção). Durante esta dissertação, hipotetizaremos que a substituição do 3D por VR irá (1) superar os problemas de usabilidade mencionados e, eventualmente, (2) aumentar a eficácia dos utilizadores em relação às questões do campo de estudo (neurociência). Para avaliar os resultados do trabalho desenvolvido nesta dissertação, será realizada uma experiência de duas partes, em que um grupo de indivíduos deverá executar um conjunto de tarefas pré-determinadas e avaliar sua experiência usando 3D na primeira e VR na última parte. Além da autoavaliação da experiência, dados como tempo de conclusão e correção da tarefa também serão usados para quantificar a eficácia do método de visualização. Dada a experiência mencionada, um protótipo de uma aplicação (baseada na Web) com visualização de Realidade Virtual deve ser desenvolvido. A visualização 3D será fornecida por uma framework de código aberto baseada na Web, chamada Geppetto. Cada uma das decisões tomadas no desenvolvimento do protótipo será analisada adequadamente neste documento, bem como a literatura científica que servirá de base quando necessário. Além do estudo da Realidade Virtual propriamente dita, também serão analisados métodos padronizados para a visualização de informações (neuro) científicas. A solução proposta procurará constituir uma base de trabalho sólida e suficientemente genérica a ser aplicada, não apenas no contexto da neurociência, mas também em vários outros contextos onde a visualização de modelos através de Realidade Virtual poderá ser bem-sucedida.Computational models are powerful tools for exploring the properties of complex biological systems. In computational neuroscience, allowing easy computational exploration and visualization of this models is crucial for the progress of the field. In recent years, Virtual Reality hardware and visualization systems have become more affordable and this opens a window of opportunity for visualization services. The current major problem of 3D visualization concerns usability (i.e., navigation and selection). During this dissertation, we will hypothesize that the replacement of 3D for VR will (1) overcome the usability issues mentioned and eventually (2) boost user effectiveness regarding field of study (neuroscience) concerns. In order to evaluate the results of the work developed under this dissertation, a two-part experiment will be carried out where a group of individuals must perform a set of predetermined tasks and evaluate their experience using 3D in the first and VR in the last part. Besides the self-evaluation of the experiment, data such as completion time and task correctness will also be used to quantify the effectiveness of the visualization method. Given the aforementioned experiment, a prototype of a (web-based) application with Virtual Reality visualization shall be developed. The 3D visualization will be provided by a web-based open-sourced framework called Geppetto. Each of the decisions made in the development of the prototype will be properly analyzed in this document, as well as the scientific literature that will serve as a basis when necessary. Besides the study of Virtual Reality itself, standard methods with respect to the visualization of (neuro)scientific information will also be analyzed. The proposed solution will seek to constitute a solid and sufficiently generic work base to be applied, not only in the scope of neuroscience, but also in several other contexts where visualization through VR might be successful

Knife-Edge Scanning Microscope Mouse Brain Atlas In Vector Graphics For Enhanced Performance

The microstructure of the brain at the cellular level provides crucial information for the understanding of the function of the brain. A large volume of high-resolution brain image data from 3D microscopy is an essential resource to study detailed microstructures of the brain. Accordingly, we have worked on obtaining high-resolution image data of entire mouse brains using the Knife-Edge Scanning Microscope (KESM). Furthermore, to disseminate these high-resolution whole mouse brain data sets to the neuroscience research community, we developed a web-based brain atlas, the KESM Brain Atlas (KESMBA). To visualize the data sets in 3D while using only a standard web browser, we employed distance attenuation and Google Maps API. The KESMBA is a powerful tool to analyze and share the KESM mouse brain data sets, but the image loading was slow because of the number of raster image (PNG) tiles and the file size. Moreover, since Google Maps API is governed by a commercial license, it does not provide enough flexibility for customization, extension, and mirroring. To solve these issues, we designed and developed a new KESM mouse brain atlas that uses a vector graphics format called Scalable Vector Graphics (SVG) instead of PNG, and OpenLayers API instead of Google Maps API. The SVG-based KESMBA using OpenLayers allows faster navigation and exploration of the KESM data, and more overlay of layers with the 4 times reduced file size compared to PNG tiles. Due to the reduced file size, the SVG-based KESMBA using OpenLayers is 2.45 times faster than the original atlas. By enhancing the performance, the users can more easily access the KESM data. We expect the SVG-based KESMBA to accelerate new discoveries in neuroscience

Integrated web visualizations for protein-protein interaction databases

BACKGROUND: Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks. RESULTS: We selected M =10 out of N =53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015. CONCLUSIONS: Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing