Software and standards poster presented at NISB 2012

This is a poster I presented at the NISB meeting in 2012: NISB 2012 Poster

Generic annotation in constraint based models

During COMBINE 2013 Frank and myself tried to come up with a compromise proposal that would combine the best elements of the two current "generic annotation" proposals that are under discussion.

When we presented this at COMBINE 2013 it was well received by the members of the community that attended the FBC session. For background on this discussion please see this document.

We call this proposal AdditionalProperties and an initial specification can be found here.

COMBINE 2013 presentations

It was really fantastic to leave a great meeting like COMBINE in Paris and fly straight to South Africa for a vacation. I'll post some vacation pictures elsewhere but for now here are links to my presentation and the slides I made while chairing the FBC session.

A short presentation: Modelling at genome scale: introducing the SBML3 FBC package

And some technical stuff on a new proposal for generic annotation and a formal UML description of the gene association extension discussed during HARMONY 2013.

COMBINE 2013: standards ride again!

Just finished having a very productive meeting at COMBINE 2013, nice to see how standards are developing, especially, that rhere are a number of things that are finally stable enough to implement. Heading up the list: SBML Groups and COMBINE Archive.

Feeling all geeked out so perhaps this is a great time for a vacation ;-)

ICSB 2013 SBML3FBC and PySCeS CBMPy

The real advantage of a "human readable" model definition format

A colleague of mine wanted to submit a model along with a paper that they had published and had somehow converted from their native format into SBML, unfortunately this had not been entirely successful and I was asked to diagnose the problem after COPASI reported a serious error in the file.

Using PySCeS I converted the file to the PySCeS model format where some translation errors were immediately apparent, could be compared to the original equation and fixed. Reloading the file, testing the steady-state solution and re-exporting it to SBML was a piece of cake.

Not bad going for a Friday afternoon ;-)

Funky Amsterdam promotional video

This is a new advertisement for Amsterdam as a cool tourist destination ... and why its nice to live there ;-)

Finally registered on GitHub

After using Subversion since it replaced CVS I've decided to get going with Git (username: bgoli) and have committed the PySCeS 0.9.0 release to GitHub to play around with.

Open source and free-to-use

I've just returned from a trip to Latvia and after discussions there was reminded of the value of open source, freely available/usable software. Of course while working in South Africa I was acutely aware of this but after living in the Netherlands for a while one gets spoilt (and lazy). What do I mean? Unless your definition of Science is such that it is restricted to the "wealthier western world" e.g.

• Windows is not free


• MATLAB (and especially it's toolboxes) are not free


• academic access to commercial software is not free


• publishing in open access journals is not free

Even though it is more difficult, takes more time and could even be career counter productive, I believe that development in Life Science software (a foundation/basic activity) should be as unrestricted (widely available) as possible.

The question of course is how does one try to convey this to funders, reviewers etc - who are embedded in a more affluent environment - that this is a valid consideration.

A more general question is whether it is worth the effort of developing a freely available tool when one can quickly whip up the code in something like MATLAB (or any other software based on a commercial platform)? In the end this starts to look like more like an ethical question e.g. "Science and Society"  than a practical one. of course whether practicing research Scientists should be concerning themselves with this sort of dilemma is up to the individual.

PySCeS 0.9.0 has been released

Finally, after far too long, I'm happy to release a new version of PySCeS. This release has many bug fixes and provides a diverse range of new features, including:

• parallel parameter scanning


• generalized supply demand analysis


• exporting simulations as SED-ML (including COMBINE archives)


• support for scanning moiety totals


• compatbility support for the latest versions of scipy/numpy


• enhanced plotting support e.g. users may now choose the Matplotlib backend

This hopefully is the start of a whole new development/maintenance cycle, more news on the PySCeS website (http://pysces.sf.net) and thanks to Che for helping to debug an irritating Windows issue.

 

And now for a real keyboard ...

My old keyboard died and its (long pondered over) replacement arrived today a "Zowie Celeritas" that makes use of mechanical Cherry MX Brown switches. So far so good, it has an awesome feel for typing - once you realize you don't have to push the key all the way down :-)

Other than a small compatibility issue with VirtualBox's USB ghosting I'm sold on mechanical switches. The only problem is that that at work I have a (comparatively) spongy, sticky rubber dome keyboard that is on its way to be replaced ... :-)

Update: a while ago I couldn't take the generic work keyboard anymore and bought a Das Professional model S (silent) also using Cherry MX Browns. This is another amazing keyboard, no more finger strain and I can now create typo's and other gibberish at high speed. I would say the Das is probably my preferred keyboard for typing with the Zowie good for more general use (e.g. Starcraft)

Writing out arrays and linked lists with pysces.write.*

PySCeS array export facilities have been expanded with a new set of array export functions. This includes exporting arrays and linked lists (lists of lists) to various flavours of text file.

These methods are either fully customizable and generic or specifically tailored for writing CSV (comma) and Text (<tab> delimited) files that include the array data and optionally row/col labels. They are all available in the ''pysces.write.*'' module (see documentation/docstrings for details).

In [3]: pysces.write.
pysces.write.exportArray2CSV
pysces.write.exportArray2TXT
pysces.write.exportLabelledArray
pysces.write.exportLabelledArray2CSV
pysces.write.exportLabelledArray2TXT
pysces.write.exportLabelledArrayWithHeader
pysces.write.exportLabelledArrayWithHeader2CSV
pysces.write.exportLabelledArrayWithHeader2TXT
pysces.write.exportLabelledLinkedList

 

SED-ML gets more interesting when you start using it: PySCeS

Last year I started getting more active in the "Simulation Experiment Description Markup Language" (SED-ML) community. At its simplest SED-ML is a way of describing time-simulation plots in a standard way, of course it is way more than this, especially after the workshop hosted by Dagmar in Rostock last November year.

Just recently, using Frank Bergmann's.SED-ML tools for the conversion I've added SED-ML support into PySCeS. Its pretty simple to use as well, anything generated with mod.doSimPlot() can be exported.

import pysces
mod = pysces.model('chain.psc','d:\\projects\\modeldir')
mod.doSimPlot(50,100)
mod.exportSimAsSedML(output='files,archive,combine')

mod.exportSimAsSedML() does all the work and its arguments specify that output should be produced as raw SBML/SED-ML files, a SEDML archive (the same files zipped together) and a COMBINE archive (*.omex) a recently proposed archive format that includes the same files with all sorts of metadata.

While this is the simplest use-case, to do this I've wrapped most of the SED-ML 1.1 spec into a Python class that can be used to generate arbitrary SED-ML descriptions. This actually generates Frank's SED-ML script which then either uses libSEDML, installed locally, or the web services provided  here to generate the SED-ML.

sed = pysces.SED.SED(sed_id, sed_output_path)

In [11]: sed.
sed.addTaskPlot           sed.addModel
sed.addDataGenerator      sed.addTaskDataGenerators 
sed.addModelAlt           sed.writeCOMBINEArchive
sed.addPlot               sed.writeSedScript
sed.addSimulation         sed.writeSedXArchive
sed.addTask               sed.writeSedXML

In [11]: sed.

This will all be available in the PySCeS 0.9 release (soon :-))

Recon 2 translated to SBML Level 3 with FBC

Yesterday I translated the newly released Human metabolic map Recon 2 from its existing COBRA format into SBML 3 with FBC. The model is now available from cbmpy.sourceforge.net

It turned out to be trivial, here's what I did:

1. Downloaded the model from biomodels


2. Loaded it into CBMPy, this translates the model into SBML3+FBC using libSBML 5.8.0
   
   

   cmod = cbm.CBRead.readCOBRASBML('MODEL1109130000.cobra.xml', os.getcwd())

   
   


3. At this point there is no objective function defined so I created one (R_biomass_reaction)
   

   cmod.createObjectiveFunction('R_biomass_reaction')

   
   


4. All that's left is to solve it:
   

   cbm.CBSolver.analyzeModel(cmod)
   
   INFO: Model is optimal: 1
   Solution status =  1 : optimal
   Solution method =  2 : dual
   Objective value =  100.0
   Status: Model is optimal

   
   


5. As this is a rather large model I decided to try my multicore FVA and see what happens (I decided to use two processing threads on  a quad core CPU). This took about and hour in the end which is slow, but this is a good excuse to look at some of the newer optimized FVA algorithms out there.
   

   fvadat = cbm.CBMultiCore.runMultiCoreFVA(cmod, procs=2)

   
   


6. Finally I want to write the whole model, the FBA solution and FVA results to file. Excel 97 is actually quite a good format for this, next step is a COMBINE archive.
   

   cbm.CBWrite.writeModelToExcel97(cmod, 'results.xls')