Metabolic Pathways

[Michael Hunger]

Dear Thilo,

Very interesting the MiltonGraph.pptx model :), 

Actually I was wondering if it would be possible to change the relations instead the path: experiment -> gene -> pathway, could be experiment -> pathway -> gene, but I don't know if I will can write the queries to compare the pathways between the experiments (like match), the main idea is to compare the pathways (with gene nodes values) between the experiments, or even search for an "active" path in a specific experiment and pathway

You are right about the proteomics or transcriptomics, they will use the same structure and the input will be the expression level, will change only the scale and the number of elements in the experiment.

What kind of questions are you proposing to do in the graph database ? and what platform are you using for ?

Best regards,

Milton

Anfang der weitergeleiteten Nachricht:

Von: Thilo Muth <mu...@mpi-magdeburg.mpg.de>

Betreff: Metabolic Pathways

Datum: 29. August 2014 09:02:28 MESZ

An: yuta...@gmail.com

Kopie: Michael Hunger <michael...@neotechnology.com>

Dear Milton,

send you my ideas to the graph db structure, you are planning to realize.

However, I was not sure if I got it right from you explanations... so I draw a quick model (the "MiltonGraph.pptx" attached. :-)

... Is that similar to what you are up to?

Otherwise you can just change the model and sent it back to me -

in order to discuss more on it.

I am doing similar things with metaproteomics data since 2012... we have proteins instead of genes, but

that should not be the big difference as we have even more different node types. ;-)

Best regards,

Thilo Muth

MPI Magdeburg

[Milton Yutaka Nishiyama Junior]

Hi everyone,

I'm constructing my graphdb model for metabolic pathways and gene expression profile, and I would like your opinion about the following implementation, if they make sense and what's the best model.

The main questions will be to look, for the genes around, longest, shortest and all possible paths in a Metabolic Pathway, with some restrictions, and make graph pattern match, basides more basic queries

I have created three models and I would like your opinion and suggestion:

In my first model, I created three types of nodes: Metabolic Pathways (MP), genes (G) and MicroarrayExperiment (E), with relation between the Gs and MP, relation between Gs and G, and relations between Gs and E, with intensity.values

In my second model,  I created  two types of nodes: genes (G), MicroarrayExperiments (E). For each Metabolic Pathway(MP),I create a set of gene (nodes) with 2 properties (gene_name and pathway_name), with relation between Gs and G, and finally a relation (with intensity.values ) of G to E

My  third model is similar to the second, but instead to connect all genes (each gene node) to one MicroarrayExperiment(node), I created for each gene(node), a rel type with each MicroarrayExperiment(node) with intensity.value 

I starting to create some queries but I would like the standpoint of experts.

Thanks and best regards,

Milton

[Martin Preusse]

Hi,

the first model seems to be the more "natural" one. In my experience it's usually the best approach to explicitly model biological entities as nodes (in your case have a node for each metabolic pathway). You are more flexible this way.

When modeling pathways you always have the problem that interactions between genes are pathway specific, i.e. even if two genes are part of two different pathways, the interaction could only be part of one of those pathways.

I usually model interactions as nodes to get around this issue. A pathway is then a bipartite graph of gene nodes and interaction nodes.

In general this heavily depends on your queries. But I think you have more options if you don't hide information (such as pathway membership) on relationship properties.

Cheers
Martin

Am Montag, 17. November 2014 um 23:16 schrieb Milton Yutaka Nishiyama Junior:

> Hi everyone,
>
> I'm constructing my graphdb model for metabolic pathways and gene expression profile, and I would like your opinion about the following implementation, if they make sense and what's the best model.
>
> The main questions will be to look, for the genes around, longest, shortest and all possible paths in a Metabolic Pathway, with some restrictions, and make graph pattern match, basides more basic queries
>
> I have created three models and I would like your opinion and suggestion:
>
> In my first model, I created three types of nodes: Metabolic Pathways (MP), genes (G) and MicroarrayExperiment (E), with relation between the Gs and MP, relation between Gs and G, and relations between Gs and E, with intensity.values
>
> In my second model, I created two types of nodes: genes (G), MicroarrayExperiments (E). For each Metabolic Pathway(MP),I create a set of gene (nodes) with 2 properties (gene_name and pathway_name), with relation between Gs and G, and finally a relation (with intensity.values ) of G to E
>
> My third model is similar to the second, but instead to connect all genes (each gene node) to one MicroarrayExperiment(node), I created for each gene(node), a rel type with each MicroarrayExperiment(node) with intensity.value
>
> I starting to create some queries but I would like the standpoint of experts.
>
> Thanks and best regards,
>
> Milton
>
> On Wednesday, September 3, 2014 7:35:47 AM UTC-3, Michael Hunger wrote:
> > Dear Thilo,
> >
> > Very interesting the MiltonGraph.pptx model :),
> >
> > Actually I was wondering if it would be possible to change the relations instead the path: experiment -> gene -> pathway, could be experiment -> pathway -> gene, but I don't know if I will can write the queries to compare the pathways between the experiments (like match), the main idea is to compare the pathways (with gene nodes values) between the experiments, or even search for an "active" path in a specific experiment and pathway
> >
> > You are right about the proteomics or transcriptomics, they will use the same structure and the input will be the expression level, will change only the scale and the number of elements in the experiment.
> >
> > What kind of questions are you proposing to do in the graph database ? and what platform are you using for ?
> >
> > Best regards,
> >
> > Milton
> >
> > Anfang der weitergeleiteten Nachricht:

> > > Von: Thilo Muth <mu...@mpi-magdeburg.mpg.de (javascript:)>

> > > Betreff: Metabolic Pathways
> > > Datum: 29. August 2014 09:02:28 MESZ

> > > An: yuta...@gmail.com (javascript:)
> > > Kopie: Michael Hunger <michael...@neotechnology.com (javascript:)>

> > >
> > > Dear Milton,
> > >
> > > send you my ideas to the graph db structure, you are planning to realize.
> > > However, I was not sure if I got it right from you explanations... so I draw a quick model (the "MiltonGraph.pptx" attached. :-)
> > > ... Is that similar to what you are up to?
> > > Otherwise you can just change the model and sent it back to me -
> > > in order to discuss more on it.
> > >
> > > I am doing similar things with metaproteomics data since 2012... we have proteins instead of genes, but
> > > that should not be the big difference as we have even more different node types. ;-)
> > >
> > > Best regards,
> > > Thilo Muth
> > > MPI Magdeburg
> >
>

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[Milton Y. Nishiyama Jr]

Hi Martin,

I had the same feeling when I modeled and created the three model examples, but now I'm implementing more complex queries to test them and try to figure out the pros and cons.

I will probably post new questions and problems.

Thanks,

Milton

--

=================================================
 Milton Yutaka Nishiyama Jr. 
 PhD Student
 Bioinformatics Group - IME/IQ - USP
 Biochemistry Department, University of Sao Paulo
 http://sucest-fun.org/
 Brazil
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