4 replies to this topic

[maestro949]

Petri Nets offer simple and relatively easy to learn modeling technique that capture the essense of state transistions of biochemical pathways. While they don't capture any of the forces or physical structures involved they may serve as one on of the many necessary topological layers that would make up the uber model we ultimately need to make the huge leaps forward.




Petri Nets - Good Example.

Why Petri nets to model biological pathways?

Note to self.... [thumb]
2nd Note to self : A coffee model probably isn't a good idea considering that you are trying to stay away from coffee! [ang]

[Brainbox]

Hey, this is nice.

I know this technique for modelling concurrency in information and data communication systems.
It's major advantage is that it's reasonable easy to model dependency in the time domain and conditions that are applied to it (or synchronisation would be a more practical term in IT technology).

Anyway, although it’s benefits, it did not find a very big user base, mainly in the research domain. Nice to see at back after these years.

[Brainbox]

A coffee model probably isn't a good idea considering that you are trying to stay away from coffee!  [ang]

Hèhè, maybe my petri knowledge became a bit rusty, but I don’t think you will have to worry.
It seems that this coffee machine is out of order since a token is missing … [sfty]

[maestro949]

Maybe that's why Petri modelling never gained a foothold for broader use. The coffee tasted like shit because they forgot to add coffee.

[maestro949]

Here's a group that used hybrid functional petri nets to build a modeling tool.

Genomic Objects Website - Used Petri Nets to build simulator tool

They have used this tool to model the p53 and related genes...

In Silico Biology - Simulation-based validation of the p53 transcriptional activity with hybrid functional Petri net

Though this is a simplification, with only 10 weeks of reading on microbiology and understanding how Petri Nets work (short learning curve), the below model is very easy to understand.




The ability to directly build simulations from these models rather than the models simply being communication tools is a big plus...


Most existing pathway databases present many biological maps of molecular interactions. However, in order to conduct simulations based on these maps, more biological facts such as reaction speeds of complex formation and protein degradations have to be included to these maps. This means that we have to reconstruct computational pathway maps for simulations after careful reading of papers of the interest. In other words, these pathway databases have not been constructed on the assumption that pathways included in them will be used for simulations.

The HFPN model of the complex p53-MDM2-p19ARF has been constructed based on biological knowledge being extracted by careful reading of the literature. Of course, with no proof by biological experiments, we could not conclude that the complex p53-MDM2-p19ARF has transcriptional activity for genes Bax and MDM2. However, without the help of simulations, it is hard to get insights into the systematic behavior of the genes and proteins forming the complex p53-MDM2-p19ARF, as demonstrated in this paper. Simulations can reduce the number of biological experiments and save the costs from both sides of expense and time.

As demonstrated in this paper, the description of biological pathways with HFPN allows the biological pathways to be simulated directly, since HFPN includes dynamic elements (transitions) at which reaction speeds are assigned as well as static elements (places) which represent the states of substances such as concentration. We have developed the automatic conversion systems of biological pathways in KEGG and TRANSPATH into HFPN models [Nagasaki et al., 2004]. By incorporating dynamic parameters such as reaction speeds of translation and complex formation from the knowledge of biologists and/or information in the literature into the converted HFPN models, the HFPN models become simulatable on Cell Illustrator (http://www.fqspl.com.pl/?a=product_view&id=20〈=en).

We have recently developed a new biological pathway description format in XML called Cell System Markup Language (CSML) (http://www.csml.org). By using the CSML and this conversion system, we are now working on the construction of a simulatable pathway database.