<p>Both formats have <a href="https://en.wikipedia.org/wiki/Formal_grammar" rel="nofollow noreferrer">formal grammars</a> for describing similar concepts, however they are different and to my knowledge one is not a proper subset of the other. While one could do a comparison of a syntactic nature, a simple Google search of just both acronyms reveals <a href="https://arxiv.org/pdf/1507.03572.pdf" rel="nofollow noreferrer">BioNetGen 2.2: Advances in Rule-Based Modeling</a> </p>

<p>In here can be found:</p>

<blockquote>
  <p>2.4 SBML-to-BNGL translation<br>
  SBML is a widely-used model exchange format in systems biology (Hucka et al., 2003). Models encoded in SBML
  are ﬂat, i.e., their species do not have internal structure, which
  limits their utility for rule-based modeling. BioNetGen 2.2 includes
  an SBML-to-BNGL translator, called Atomizer (also available as a web
  tool at ratomizer.appspot.com), that can extract implicit molecular
  structure from ﬂat species (Tapia and Faeder, 2013). A full report on
  Atomizer and its application to the BioModels database (Li et al.,
  2010) is currently in preparation. However, Tapia and Faeder (2013)
  reported that an early version of the tool could recover implicit
  structure for about 60% of species in models within the database that
  contain ≥20 species. Thus, Atomizer makes available a large set of
  pre-existing models in a rule-based format, facilitating their
  visualization (Wenskovitch et al., 2014) and extension (Chylek et al.,
  2015).</p>
</blockquote>

<p>While not directly related to the question this also helped in understanding more about BioNetGen</p>

<p>Wikipedia: <a href="https://en.wikipedia.org/wiki/Multi-state_modeling_of_biomolecules" rel="nofollow noreferrer">Multi-state modeling of biomolecules</a></p>

<blockquote>
  <p>Biological signaling systems often rely on complexes of biological
  macromolecules that can undergo several functionally significant
  modifications that are mutually compatible. Thus, they can exist in a
  very large number of functionally different states. Modeling such
  multi-state systems poses two problems: The problem of how to describe
  and specify a multi-state system (the "specification problem") and the
  problem of how to use a computer to simulate the progress of the
  system over time (the "computation problem"). To address the
  specification problem, modelers have in recent years moved away from
  explicit specification of all possible states, and towards rule-based
  formalisms that allow for implicit model specification, including the
  κ-calculus, BioNetGen, the Allosteric Network Compiler and others. To
  tackle the computation problem, they have turned to particle-based
  methods that have in many cases proved more computationally efficient
  than population-based methods based on ordinary differential
  equations, partial differential equations, or the Gillespie stochastic
  simulation algorithm. Given current computing technology,
  particle-based methods are sometimes the only possible option.
  Particle-based simulators further fall into two categories:
  Non-spatial simulators such as StochSim, DYNSTOC, RuleMonkey, and
  NFSim and spatial simulators, including Meredys, SRSim and MCell.
  Modelers can thus choose from a variety of tools; the best choice
  depending on the particular problem. Development of faster and more
  powerful methods is ongoing, promising the ability to simulate ever
  more complex signaling processes in the future.</p>
</blockquote>

CC BY-SA 3.0
2018-02-21T15:48:52.370Z
Answering your own question immediately is not frowned upon. you have the option since you ask! But is usually recommended to not accept an answer between minutes of asking it.
llrs
CC BY-SA 3.0
2018-02-21T15:50:08.733Z
It would help tremendously if this were written and formatted as a proper answer rather than a meta-discussion.
Konrad Rudolph
CC BY-SA 3.0
2018-02-21T15:50:58.863Z
@KonradRudolph I edited both Q and A, but you can improve them further :D in case I missed something
llrs
CC BY-SA 3.0
2018-02-21T16:04:08.350Z
I have still to read the sources, but I hope so. Thanks (+1) for sharing your knowledge. One more thing, in case you cite a paper, usually is better to cite the peer-reviewed and accepted paper if possible.
llrs
CC BY-SA 3.0
2018-02-21T15:45:42.517Z
2018-02-21T15:50:10.623Z
2018-02-21T15:50:10.623Z
Guy Coder
<p>Per request from meta <a href="https://bioinformatics.meta.stackexchange.com/questions/165/would-questions-on-whole-cell-modeling-get-answers-at-this-site?noredirect=1#comment1383_165">comment</a>.</p>

<p>I am self-learning about whole cell modeling, specifically <a href="http://intro-to-wc-modeling.readthedocs.io/en/latest/index.html#" rel="nofollow noreferrer">An introduction to whole-cell modeling</a> and <a href="https://rads.stackoverflow.com/amzn/click/1420084100" rel="nofollow noreferrer">Fundamentals of Systems Biology: From Synthetic Circuits to Whole-cell Models</a>. While my background is decades of programming, for cellular biology it is only skimming through a few books. </p>

<p>In the process of learning I soon discovered <a href="http://sbml.org/SBML_Software_Guide/SBML_Software_Matrix" rel="nofollow noreferrer">software/apps</a> that use <a href="http://sbml.org/Main_Page" rel="nofollow noreferrer">SBML</a> (System Biology Markup language) but in <a href="http://intro-to-wc-modeling.readthedocs.io/en/latest/cell_modeling/bio_net_gen/bio_net_gen.html" rel="nofollow noreferrer">referring</a> back to "An introduction to whole-cell modeling" see that they use <a href="http://www.csb.pitt.edu/Faculty/Faeder/?page_id=409" rel="nofollow noreferrer">BioNetGen</a> (<a href="https://github.com/RuleWorld/bionetgen" rel="nofollow noreferrer">GitHub</a>) which has its own language BioNetGen language (BNGL)</p>

<p>What are the differences between BNGL (BioNetGen Language) and SBML (System Biology Markup Langague) formats?</p>

<file-formats>
What are the differences between BNGL (BioNetGen Language) and SBML (System Biology Markup Langague) formats?
# What are the differences between BNGL (BioNetGen Language) and SBML (System Biology Markup Langague) formats?

(Asked by: username_0 on 2018-02-21)

Per request from meta [comment](https://bioinformatics.meta.stackexchange.com/questions/165/would-questions-on-whole-cell-modeling-get-answers-at-this-site?noredirect=1#comment1383_165).

I am self-learning about whole cell modeling, specifically [An introduction to whole-cell modeling](http://intro-to-wc-modeling.readthedocs.io/en/latest/index.html#) and [Fundamentals of Systems Biology: From Synthetic Circuits to Whole-cell Models](https://rads.stackoverflow.com/amzn/click/1420084100). While my background is decades of programming, for cellular biology it is only skimming through a few books.

In the process of learning I soon discovered [software/apps](http://sbml.org/SBML_Software_Guide/SBML_Software_Matrix) that use [SBML](http://sbml.org/Main_Page) (System Biology Markup language) but in [referring](http://intro-to-wc-modeling.readthedocs.io/en/latest/cell_modeling/bio_net_gen/bio_net_gen.html) back to "An introduction to whole-cell modeling" see that they use [BioNetGen](http://www.csb.pitt.edu/Faculty/Faeder/?page_id=409) ([GitHub](https://github.com/RuleWorld/bionetgen)) which has its own language BioNetGen language (BNGL)

What are the differences between BNGL (BioNetGen Language) and SBML (System Biology Markup Langague) formats?

**Tags:** file-formats

---

## 1 answers

### Answer by username_0 on 2018-02-21. Score: 3 | Accepted answer

Both formats have [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) for describing similar concepts, however they are different and to my knowledge one is not a proper subset of the other. While one could do a comparison of a syntactic nature, a simple Google search of just both acronyms reveals [BioNetGen 2.2: Advances in Rule-Based Modeling](https://arxiv.org/pdf/1507.03572.pdf)

In here can be found:

> 2.4 SBML-to-BNGL translation  
> SBML is a widely-used model exchange format in systems biology (Hucka et al., 2003). Models encoded in SBML
> are ﬂat, i.e., their species do not have internal structure, which
> limits their utility for rule-based modeling. BioNetGen 2.2 includes
> an SBML-to-BNGL translator, called Atomizer (also available as a web
> tool at ratomizer.appspot.com), that can extract implicit molecular
> structure from ﬂat species (Tapia and Faeder, 2013). A full report on
> Atomizer and its application to the BioModels database (Li et al.,
> 2010) is currently in preparation. However, Tapia and Faeder (2013)
> reported that an early version of the tool could recover implicit
> structure for about 60% of species in models within the database that
> contain ≥20 species. Thus, Atomizer makes available a large set of
> pre-existing models in a rule-based format, facilitating their
> visualization (Wenskovitch et al., 2014) and extension (Chylek et al.,
> 2015).

While not directly related to the question this also helped in understanding more about BioNetGen

Wikipedia: [Multi-state modeling of biomolecules](https://en.wikipedia.org/wiki/Multi-state_modeling_of_biomolecules)

> Biological signaling systems often rely on complexes of biological
> macromolecules that can undergo several functionally significant
> modifications that are mutually compatible. Thus, they can exist in a
> very large number of functionally different states. Modeling such
> multi-state systems poses two problems: The problem of how to describe
> and specify a multi-state system (the "specification problem") and the
> problem of how to use a computer to simulate the progress of the
> system over time (the "computation problem"). To address the
> specification problem, modelers have in recent years moved away from
> explicit specification of all possible states, and towards rule-based
> formalisms that allow for implicit model specification, including the
> κ-calculus, BioNetGen, the Allosteric Network Compiler and others. To
> tackle the computation problem, they have turned to particle-based
> methods that have in many cases proved more computationally efficient
> than population-based methods based on ordinary differential
> equations, partial differential equations, or the Gillespie stochastic
> simulation algorithm. Given current computing technology,
> particle-based methods are sometimes the only possible option.
> Particle-based simulators further fall into two categories:
> Non-spatial simulators such as StochSim, DYNSTOC, RuleMonkey, and
> NFSim and spatial simulators, including Meredys, SRSim and MCell.
> Modelers can thus choose from a variety of tools; the best choice
> depending on the particular problem. Development of faster and more
> powerful methods is ongoing, promising the ability to simulate ever
> more complex signaling processes in the future.

> **Comments:**
> **username_1**: Answering your own question immediately is not frowned upon. you have the option since you ask! But is usually recommended to not accept an answer between minutes of asking it.  
> **username_2**: It would help tremendously if this were written and formatted as a proper answer rather than a meta-discussion.  
> **username_1**: @KonradRudolph I edited both Q and A, but you can improve them further :D in case I missed something  
> **username_1**: I have still to read the sources, but I hope so. Thanks (+1) for sharing your knowledge. One more thing, in case you cite a paper, usually is better to cite the peer-reviewed and accepted paper if possible.  

---

