50,864 research outputs found
Visinets: A Web-Based Pathway Modeling and Dynamic Visualization Tool
In this report we describe a novel graphically oriented method for pathway modeling and a software package that allows for both modeling and visualization of biological networks in a user-friendly format. The Visinets mathematical approach is based on causal mapping (CMAP) that has been fully integrated with graphical interface. Such integration allows for fully graphical and interactive process of modeling, from building the network to simulation of the finished model. To test the performance of Visinets software we have applied it to: a) create executable EGFR-MAPK pathway model using an intuitive graphical way of modeling based on biological data, and b) translate existing ordinary differential equation (ODE) based insulin signaling model into CMAP formalism and compare the results. Our testing fully confirmed the potential of the CMAP method for broad application for pathway modeling and visualization and, additionally, showed significant advantage in computational efficiency. Furthermore, we showed that Visinets web-based graphical platform, along with standardized method of pathway analysis, may offer a novel and attractive alternative for dynamic simulation in real time for broader use in biomedical research. Since Visinets uses graphical elements with mathematical formulas hidden from the users, we believe that this tool may be particularly suited for those who are new to pathway modeling and without the in-depth modeling skills often required when using other software packages
BioSimulator.jl: Stochastic simulation in Julia
Biological systems with intertwined feedback loops pose a challenge to
mathematical modeling efforts. Moreover, rare events, such as mutation and
extinction, complicate system dynamics. Stochastic simulation algorithms are
useful in generating time-evolution trajectories for these systems because they
can adequately capture the influence of random fluctuations and quantify rare
events. We present a simple and flexible package, BioSimulator.jl, for
implementing the Gillespie algorithm, -leaping, and related stochastic
simulation algorithms. The objective of this work is to provide scientists
across domains with fast, user-friendly simulation tools. We used the
high-performance programming language Julia because of its emphasis on
scientific computing. Our software package implements a suite of stochastic
simulation algorithms based on Markov chain theory. We provide the ability to
(a) diagram Petri Nets describing interactions, (b) plot average trajectories
and attached standard deviations of each participating species over time, and
(c) generate frequency distributions of each species at a specified time.
BioSimulator.jl's interface allows users to build models programmatically
within Julia. A model is then passed to the simulate routine to generate
simulation data. The built-in tools allow one to visualize results and compute
summary statistics. Our examples highlight the broad applicability of our
software to systems of varying complexity from ecology, systems biology,
chemistry, and genetics. The user-friendly nature of BioSimulator.jl encourages
the use of stochastic simulation, minimizes tedious programming efforts, and
reduces errors during model specification.Comment: 27 pages, 5 figures, 3 table
Modelling and simulation framework for reactive transport of organic contaminants in bed-sediments using a pure java object - oriented paradigm
Numerical modelling and simulation of organic contaminant reactive transport in the environment is being increasingly
relied upon for a wide range of tasks associated with risk-based decision-making, such as prediction of contaminant
profiles, optimisation of remediation methods, and monitoring of changes resulting from an implemented remediation
scheme. The lack of integration of multiple mechanistic models to a single modelling framework, however, has
prevented the field of reactive transport modelling in bed-sediments from developing a cohesive understanding of
contaminant fate and behaviour in the aquatic sediment environment. This paper will investigate the problems involved
in the model integration process, discuss modelling and software development approaches, and present preliminary
results from use of CORETRANS, a predictive modelling framework that simulates 1-dimensional organic contaminant
reaction and transport in bed-sediments
A review of data visualization: opportunities in manufacturing sequence management.
Data visualization now benefits from developments in technologies that offer innovative ways of presenting complex data. Potentially these have widespread application in communicating the complex information domains typical of manufacturing sequence management environments for global enterprises. In this paper the authors review the visualization functionalities, techniques and applications reported in literature, map these to manufacturing sequence information presentation requirements and identify the opportunities available and likely development paths. Current leading-edge practice in dynamic updating and communication with suppliers is not being exploited in manufacturing sequence management; it could provide significant benefits to manufacturing business. In the context of global manufacturing operations and broad-based user communities with differing needs served by common data sets, tool functionality is generally ahead of user application
Simulation modelling and visualisation: toolkits for building artificial worlds
Simulations users at all levels make heavy use of compute resources to drive computational
simulations for greatly varying applications areas of research using different simulation
paradigms. Simulations are implemented in many software forms, ranging from highly standardised
and general models that run in proprietary software packages to ad hoc hand-crafted
simulations codes for very specific applications. Visualisation of the workings or results of a
simulation is another highly valuable capability for simulation developers and practitioners.
There are many different software libraries and methods available for creating a visualisation
layer for simulations, and it is often a difficult and time-consuming process to assemble a
toolkit of these libraries and other resources that best suits a particular simulation model. We
present here a break-down of the main simulation paradigms, and discuss differing toolkits and
approaches that different researchers have taken to tackle coupled simulation and visualisation
in each paradigm
Army-NASA aircrew/aircraft integration program. Phase 5: A3I Man-Machine Integration Design and Analysis System (MIDAS) software concept document
This is the Software Concept Document for the Man-machine Integration Design and Analysis System (MIDAS) being developed as part of Phase V of the Army-NASA Aircrew/Aircraft Integration (A3I) Progam. The approach taken in this program since its inception in 1984 is that of incremental development with clearly defined phases. Phase 1 began in 1984 and subsequent phases have progressed at approximately 10-16 month intervals. Each phase of development consists of planning, setting requirements, preliminary design, detailed design, implementation, testing, demonstration and documentation. Phase 5 began with an off-site planning meeting in November, 1990. It is expected that Phase 5 development will be complete and ready for demonstration to invited visitors from industry, government and academia in May, 1992. This document, produced during the preliminary design period of Phase 5, is intended to record the top level design concept for MIDAS as it is currently conceived. This document has two main objectives: (1) to inform interested readers of the goals of the MIDAS Phase 5 development period, and (2) to serve as the initial version of the MIDAS design document which will be continuously updated as the design evolves. Since this document is written fairly early in the design period, many design issues still remain unresolved. Some of the unresolved issues are mentioned later in this document in the sections on specific components. Readers are cautioned that this is not a final design document and that, as the design of MIDAS matures, some of the design ideas recorded in this document will change. The final design will be documented in a detailed design document published after the demonstrations
ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization
ROOT is an object-oriented C++ framework conceived in the high-energy physics
(HEP) community, designed for storing and analyzing petabytes of data in an
efficient way. Any instance of a C++ class can be stored into a ROOT file in a
machine-independent compressed binary format. In ROOT the TTree object
container is optimized for statistical data analysis over very large data sets
by using vertical data storage techniques. These containers can span a large
number of files on local disks, the web, or a number of different shared file
systems. In order to analyze this data, the user can chose out of a wide set of
mathematical and statistical functions, including linear algebra classes,
numerical algorithms such as integration and minimization, and various methods
for performing regression analysis (fitting). In particular, ROOT offers
packages for complex data modeling and fitting, as well as multivariate
classification based on machine learning techniques. A central piece in these
analysis tools are the histogram classes which provide binning of one- and
multi-dimensional data. Results can be saved in high-quality graphical formats
like Postscript and PDF or in bitmap formats like JPG or GIF. The result can
also be stored into ROOT macros that allow a full recreation and rework of the
graphics. Users typically create their analysis macros step by step, making use
of the interactive C++ interpreter CINT, while running over small data samples.
Once the development is finished, they can run these macros at full compiled
speed over large data sets, using on-the-fly compilation, or by creating a
stand-alone batch program. Finally, if processing farms are available, the user
can reduce the execution time of intrinsically parallel tasks - e.g. data
mining in HEP - by using PROOF, which will take care of optimally distributing
the work over the available resources in a transparent way
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