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Smoldyn: Art & Features

Smoldyn is an open-source software application for cell-scale biochemical simulations. It uses particle-based simulation, meaning that it simulates each molecule of interest individually, in order to capture natural stochasticity and yield nanometer-scale spatial resolution. Simulated molecules diffuse, react, are confined by surfaces, and bind to…

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Smoldyn topic overview

The analysis highlights Art and Features as prominent areas in the source structure around Smoldyn.

Related topics
2
Source areas
1
Connected nodes
3
Extracted relationships
56
Concept neighborhoods
4
Bridge connections
3

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Features · 2 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

License
LGPL
Operating system
Linux, macOS and Windows
Original author
Steve Andrews
Release
July 1, 2003; 23 years ago (2003-07-01)
Repository
github.com/ssandrews/Smoldyn
Stable release
2.71 / February 6, 2023; 3 years ago (2023-02-06)

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Features

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Smoldyn connects Entity context

The extracted context around Smoldyn shows recurring relationship patterns in the source. For example, Smoldyn → Accuracy, All, Because, BioNetGen, BNGL, C/C, Gillespie, It, Model, Models, Multi-scale, Python API, Python APIs, Real-time, Rule-based, Simulated, Smoldyn's, Tests, The, These Another extracted example is Smoldyn → Adam Arkin, C/C, Computational Research Laboratories, Further, India, MITRE, NIH, NSF, Pune, Python APIs, Roger Brent, Simons Foundation, Steve Andrews, Upinder Bhalla, US DOE. Use these groups to spot repeated connection types before inspecting the individual relationships.

Smoldyn

Top relations

related to Features · 21
Smoldyn → Accuracy, All, Because, BioNetGen, BNGL, C/C, Gillespie, It, Model, Models, Multi-scale, Python API, Python APIs, Real-time, Rule-based, Simulated, Smoldyn's, Tests, The, These
related to history · 15
Smoldyn → Adam Arkin, C/C, Computational Research Laboratories, Further, India, MITRE, NIH, NSF, Pune, Python APIs, Roger Brent, Simons Foundation, Steve Andrews, Upinder Bhalla, US DOE
related to Development team · 5
Smoldyn → Diliwar Singh, Martin Robinson, Nathan Addy, Other, Steve Andrews
related to GPU acceleration · 4
Smoldyn → CPU, GPUs, However, They
related to External links · 2
Smoldyn → GitHub, Official
License · 1
Smoldyn → LGPL
Operating system · 1
Smoldyn → Linux, macOS and Windows
Original author · 1
Smoldyn → Steve Andrews
Release · 1
Smoldyn → July 1, 2003; 23 years ago (2003-07-01)
Repository · 1
Smoldyn → github.com/ssandrews/Smoldyn

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

simulation surfaces software development chemical reactions diffusion interactions supports biology spatial simulated molecules systems biochemical python features also steve andrews

Smoldyn relationships Subject–Predicate–Object triples

TTTA extracted 56 structured relationships around Smoldyn. Examples in this analysis include Smoldyn → License → LGPL and Smoldyn → Operating system → Linux, macOS and Windows. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
SmoldynLicenseLGPL1.00infobox
SmoldynOperating systemLinux, macOS and Windows1.00infobox
SmoldynOriginal authorSteve Andrews1.00infobox
SmoldynReleaseJuly 1, 2003; 23 years ago (2003-07-01)1.00infobox
SmoldynRepositorygithub.com/ssandrews/Smoldyn1.00infobox
SmoldynStable release2.71 / February 6, 2023; 3 years ago (2023-02-06)1.00infobox
SmoldynTypeSimulation software1.00infobox
SmoldynWebsitewww.smoldyn.org1.00infobox
SmoldynWritten inC, C++, Python1.00infobox
Smoldynrelated to Development teamSteve Andrews0.60section
Smoldynrelated to Development teamOther0.60section
Smoldynrelated to Development teamNathan Addy0.60section

Related concept clusters Concept neighborhoods

The concept neighborhoods around Smoldyn bring nearby vocabulary together. In this analysis, examples include Andrews, Research and Steve. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Smoldyn
    • Andrews
    • Research
    • Steve
    • Development
    • Software
    • Simulation
    • Github
    • Primarily
    • Website
    • Also
    • Features
    • Modeling
  • smoldyn
    • Andrews
    • Research
    • Steve
    • Development
    • Software
    • Simulation
    • Github
    • Primarily
    • Website
    • Also
    • Features
    • Modeling
  • rule-based modeling
    • Modeling
    • Rule-based
    • Volume
    • Website
    • Molecules
    • Python
    • Supports
    • Systems
    • Software
    • Surfaces
    • Smoldyn
    • Simulation
  • features
    • Github
    • Website
    • Python
    • Steve
    • System
    • Reactions
    • Software
    • Supports
    • Smoldyn
    • Simulation

Connections between topic areas Semantic bridges

Bridges highlight paths between different parts of the Smoldyn map and can reveal research angles that are easy to miss in a flat list.

Min side: 3

Map overview Semantic statistics

Smoldyn

Nodes4
Edges3
Triples56
Avg. degree1.5
Density0.5
Components1

Source & methodology

TTTA analyzes the structure around Smoldyn to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Art & Features, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Smoldyn · EN edition · Analysis: TopicsToTalkAbout

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