Stochastic Simulation Framework
Stochastic Simulation Framework - Related Videos
07:12
A Gaze-Contingent Display Framework for Perceptual Learning Research with Simulated Central Vision Loss
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Cited by 1 •
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We present development of a gaze-contingent display framework designed for perceptual and oculomotor research simulating central vision loss. This framework is particularly adaptable for studying compensatory behavioral and oculomotor strategies in individuals experiencing both simulated and pathological central vision loss.
10:34
Designing and Implementing Nervous System Simulations on LEGO Robots
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Cited by 7 •
2013
An approach to neural network modeling on the LEGO Mindstorms robotics platform is presented. The method provides a simulation tool for invertebrate neuroscience research in both the research lab and the classroom. This technique enables the investigation of biomimetic robot control principles.
09:23
Integrating Automated Simulation Workflows with 3D Visualization for Virtual Experiments in the Metaverse
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2026
A generalized, FAIR-compliant method is presented for domain-expert researchers seeking to integrate simulation and data-processing tools into automated workflows for 3D virtual experiments. A neutronics example demonstrates setting up a local Galaxy instance, wrapping OpenMC and file-conversion tools, launching workflows from Omniverse, and visualizing the converted 3D outputs.
12:44
Watershed Planning within a Quantitative Scenario Analysis Framework
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Cited by 1 •
2016
There is a critical need for tools and methodologies capable of managing aquatic systems in the face of uncertain future conditions. We provide methods for conducting a targeted watershed assessment that enables resource managers to produce landscape-based cumulative effects models for use within a scenario analysis management framework.
09:36
Direct Stochastic Optical Reconstruction Microscopy of Extracellular Vesicles in Three Dimensions
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Cited by 8 •
2021
Direct stochastic optical reconstruction microscopy (dSTORM) is used to bypass the typical diffraction limit of light microscopy and to view exosomes at the nanometer scale. It can be employed in both two and three dimensions to characterize exosomes.
FAQs
Reaction propensities represent the likelihood associated with possible molecular events under the current system state. The framework uses these values together with random sampling to select the next reaction and determine its timing. As molecular species numbers change after each event, the propensities can also change, allowing enzyme, signaling, or metabolic behavior to evolve dynamically.
A deterministic model follows continuously changing average concentrations, whereas a stochastic simulation framework tracks fluctuations in molecular species numbers through individual reaction events. This distinction matters when random variation can alter pathway behavior, especially in systems with small molecular populations. Stochastic results therefore complement deterministic predictions by showing possible trajectories rather than only average behavior.
Molecular fluctuations can produce behavior that is not visible in an average concentration profile. A stochastic approach captures variation among possible system trajectories and can represent rare events that influence pathway dynamics. This is useful for examining how random molecular behavior affects enzyme catalysis, gene regulation, signaling pathways, or metabolic networks.
A typical setup identifies the biochemical species and reactions to be represented, assigns reaction propensities, and uses random sampling to choose which event occurs next and when. After the event changes molecular species numbers, the relevant propensities are updated and the process continues. Repeating this sequence produces trajectories for evaluating fluctuations and pathway behavior.
The framework can represent enzyme catalysis, gene regulation, signaling pathways, and metabolic networks. Its broader value comes from applying the same event-based treatment to systems where molecular variability influences outcomes. Researchers can examine pathway dynamics across these contexts while retaining information about random changes that concentration-based average models may not show.
Researchers can vary model parameters or changing conditions and evaluate how those choices affect simulated pathway dynamics and molecular variability. The resulting trajectories can reveal noise, rare events, or other behavior that merits experimental examination. In this way, simulations help assess parameter effects and guide experiments while complementing, rather than replacing, deterministic modeling.