12,461 research outputs found
COEL: A Web-based Chemistry Simulation Framework
The chemical reaction network (CRN) is a widely used formalism to describe
macroscopic behavior of chemical systems. Available tools for CRN modelling and
simulation require local access, installation, and often involve local file
storage, which is susceptible to loss, lacks searchable structure, and does not
support concurrency. Furthermore, simulations are often single-threaded, and
user interfaces are non-trivial to use. Therefore there are significant hurdles
to conducting efficient and collaborative chemical research. In this paper, we
introduce a new enterprise chemistry simulation framework, COEL, which
addresses these issues. COEL is the first web-based framework of its kind. A
visually pleasing and intuitive user interface, simulations that run on a large
computational grid, reliable database storage, and transactional services make
COEL ideal for collaborative research and education. COEL's most prominent
features include ODE-based simulations of chemical reaction networks and
multicompartment reaction networks, with rich options for user interactions
with those networks. COEL provides DNA-strand displacement transformations and
visualization (and is to our knowledge the first CRN framework to do so), GA
optimization of rate constants, expression validation, an application-wide
plotting engine, and SBML/Octave/Matlab export. We also present an overview of
the underlying software and technologies employed and describe the main
architectural decisions driving our development. COEL is available at
http://coel-sim.org for selected research teams only. We plan to provide a part
of COEL's functionality to the general public in the near future.Comment: 23 pages, 12 figures, 1 tabl
Adaptive GPU-accelerated force calculation for interactive rigid molecular docking using haptics
Molecular docking systems model and simulate in silico the interactions of intermolecular binding. Haptics-assisted docking enables the user to interact with the simulation via their sense of touch but a stringent time constraint on the computation of forces is imposed due to the sensitivity of the human haptic system. To simulate high fidelity smooth and stable feedback the haptic feedback loop should run at rates of 500 Hz to 1 kHz. We present an adaptive force calculation approach that can be executed in parallel on a wide range of Graphics Processing Units (GPUs) for interactive haptics-assisted docking with wider applicability to molecular simulations. Prior to the interactive session either a regular grid or an octree is selected according to the available GPU memory to determine the set of interatomic interactions within a cutoff distance. The total force is then calculated from this set. The approach can achieve force updates in less than 2 ms for molecular structures comprising hundreds of thousands of atoms each, with performance improvements of up to 90 times the speed of current CPU-based force calculation approaches used in interactive docking. Furthermore, it overcomes several computational limitations of previous approaches such as pre-computed force grids, and could potentially be used to model receptor flexibility at haptic refresh rates
Tangible user interfaces : past, present and future directions
In the last two decades, Tangible User Interfaces (TUIs) have emerged as a new interface type that interlinks the digital and physical worlds. Drawing upon users' knowledge and skills of interaction with the real non-digital world, TUIs show a potential to enhance the way in which people interact with and leverage digital information. However, TUI research is still in its infancy and extensive research is required in or- der to fully understand the implications of tangible user interfaces, to develop technologies that further bridge the digital and the physical, and to guide TUI design with empirical knowledge. This paper examines the existing body of work on Tangible User In- terfaces. We start by sketching the history of tangible user interfaces, examining the intellectual origins of this field. We then present TUIs in a broader context, survey application domains, and review frame- works and taxonomies. We also discuss conceptual foundations of TUIs including perspectives from cognitive sciences, phycology, and philoso- phy. Methods and technologies for designing, building, and evaluating TUIs are also addressed. Finally, we discuss the strengths and limita- tions of TUIs and chart directions for future research
CancerLinker: Explorations of Cancer Study Network
Interactive visualization tools are highly desirable to biologist and cancer
researchers to explore the complex structures, detect patterns and find out the
relationships among bio-molecules responsible for a cancer type. A pathway
contains various bio-molecules in different layers of the cell which is
responsible for specific cancer type. Researchers are highly interested in
understanding the relationships among the proteins of different pathways and
furthermore want to know how those proteins are interacting in different
pathways for various cancer types. Biologists find it useful to merge the data
of different cancer studies in a single network and see the relationships among
the different proteins which can help them detect the common proteins in cancer
studies and hence reveal the pattern of interactions of those proteins. We
introduce the CancerLinker, a visual analytic tool that helps researchers
explore cancer study interaction network. Twenty-six cancer studies are merged
to explore pathway data and bio-molecules relationships that can provide the
answers to some significant questions which are helpful in cancer research. The
CancerLinker also helps biologists explore the critical mutated proteins in
multiple cancer studies. A bubble graph is constructed to visualize common
protein based on its frequency and biological assemblies. Parallel coordinates
highlight patterns of patient profiles (obtained from cBioportal by WebAPI
services) on different attributes for a specified cancer studyComment: 7 pages, 9 figure
A signaling visualization toolkit to support rational design of combination therapies and biomarker discovery: SiViT
Targeted cancer therapy aims to disrupt aberrant cellular signalling pathways. Biomarkers are surrogates of pathway state, but there is limited success in translating candidate biomarkers to clinical practice due to the intrinsic complexity of pathway networks. Systems biology approaches afford better understanding of complex, dynamical interactions in signalling pathways targeted by anticancer drugs. However, adoption of dynamical modelling by clinicians and biologists is impeded by model inaccessibility. Drawing on computer games technology, we present a novel visualisation toolkit, SiViT, that converts systems biology models of cancer cell signalling into interactive simulations that can be used without specialist computational expertise. SiViT allows clinicians and biologists to directly introduce for example loss of function mutations and specific inhibitors. SiViT animates the effects of these introductions on pathway dynamics, suggesting further experiments and assessing candidate biomarker effectiveness. In a systems biology model of Her2 signalling we experimentally validated predictions using SiViT, revealing the dynamics of biomarkers of drug resistance and highlighting the role of pathway crosstalk. No model is ever complete: the iteration of real data and simulation facilitates continued evolution of more accurate, useful models. SiViT will make accessible libraries of models to support preclinical research, combinatorial strategy design and biomarker discovery
Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing
In the past decade, tracking health trends using social media data has shown
great promise, due to a powerful combination of massive adoption of social
media around the world, and increasingly potent hardware and software that
enables us to work with these new big data streams. At the same time, many
challenging problems have been identified. First, there is often a mismatch
between how rapidly online data can change, and how rapidly algorithms are
updated, which means that there is limited reusability for algorithms trained
on past data as their performance decreases over time. Second, much of the work
is focusing on specific issues during a specific past period in time, even
though public health institutions would need flexible tools to assess multiple
evolving situations in real time. Third, most tools providing such capabilities
are proprietary systems with little algorithmic or data transparency, and thus
little buy-in from the global public health and research community. Here, we
introduce Crowdbreaks, an open platform which allows tracking of health trends
by making use of continuous crowdsourced labelling of public social media
content. The system is built in a way which automatizes the typical workflow
from data collection, filtering, labelling and training of machine learning
classifiers and therefore can greatly accelerate the research process in the
public health domain. This work introduces the technical aspects of the
platform and explores its future use cases
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