jkarr

A variety of reasons really. In no particular order: - Still has good math support, rapid development - Better OOP support - More libraries -- everything from web development to scientific computing to GUIs to databasing - More reliable, less buggy - Better development tools -- code completion, testing, coverage, profiling, etc - Free. Therefore can more easily be run on clusters without licensing issues or using the MATLAB MCR

I don't see any friction, except if one needed to port old code. It seems that a lot of people are moving toward SciPy/NumPy these days.

We used a combination of representing copy numbers of molecular species and representing individual molecules of a few specific types: DNA polymerase, RNA polymerase, ribosome, FtsZ

We used a combination of tracking concentrations and individual molecules. For specific macromolecules like DNA polymerase, RNA polymerase, ribosomes, etc we kept track of the position of each individual molecule. For other things like glucose, water, etc with very high copy number we just kept track of the copy number.

I agree modelling entire organisms will ultimately require large collaborations, potentially through projects like Open Worm.

Agreed. MATLAB its set of downsides. On the plus side development is rapid and since 2008 it has good support for classes. We're starting to move toward python for future work.

I think MATLAB itself isn't so bad, so much as the way its typically used -- poorly commented, organized, and untested. We put a lot of effort into clearly commenting and organizing the code. We used matlab-xunit and hudson to manage testing, which worked quite well. http://www.mathworks.com/matlabcentral/fileexchange/22846-ma..., http://www.mathworks.com/matlabcentral/fileexchange/33971-xm....

All of the source code is available at http://simtk.org/home/wholecell. http://wholecell.stanford.edu provides a searchable interface to our training set database.

All of the supplementary material is available at http://simtk.org/home/wholecell. http://wholecell.stanford.edu provides a search interface to all of the training data.

The model is "M. genitalium" in the sense that it attempts to predict M. genitalium's phenotype from its genotype. In order to build the model we needed to use data from many additional organisms. In that sense, the model represents a hybrid of all of these organisms.

We used MATLAB. We organized the training data into a database (referred to as the "knowledge base") which was implemented in MYSQL with a web-based UI implemented in PHP. The various other scripts in shell and Perl were used to run the model on our Linux cluster.