Plectix Blog

A Step Closer to the Cloud

by Vy Hoang

Two months ago, I posted a blog entry about big pharmaceutical companies slowly moving to cloud computing as a way to improve the quality of research and the efficiency of the company. Now, scientists can follow suit. Last week, the National Science Foundation (NSF) and Microsoft Corporation announced their plan to give American scientific researchers free access to Microsoft’s new Windows Azure cloud computing system through NSF grants for three years. With competitors like Google and Amazon, Microsoft is trying to distinguish itself by offering custom applications on the Azure platform to scientists, as well as the use of familiar software like Microsoft Excel.

Their goal is to help scientists deal with the massive amounts of research data. Laboratories and universities across the US traditionally have scientific computing centers with supercomputers for solving scientific problems, but in recent times researchers want computing systems that can store and analyze huge amounts of data—which is why I think cloud computing systems may be the way to go. Not only will the cloud cut down on time and cost, fewer people will be needed in the lab to manage data because the cloud is doing most of the work (managing and analyzing data).

With the agreement between NSF and Microsoft set in stone, scientists are a step closer to the cloud—but the question is will they use it? Although the Azure platform provides researchers with tools for managing and analyzing data in a simple manner, are these tools themselves able to make the complexity of research simpler?

Why Not Use Rules?

By Ty Thomson

 

Although the field of rule-based biological modeling has been around for at least ten years, it has yet to really take off.  The reasons why I think that it has yet to catch on should perhaps be the topic of another blog post.  Today I want to discuss how rule-based modeling compares to more traditional modeling approaches.

 

An often-highlighted aspect of rule-based modelingis that it allows you to build models with more species and reactions than you can build using traditional reaction-based approaches.  Often, researchers will wonder whether all those additional reactions means that they will need even more rate constants – and all you modelers out there know that the dearth of rate constants is a huge challenge to modeling. 

 

Although the researchers are right, there are indeed more reactions – which could mean more rate constants – the point of rule-based modeling is to take similar reactions that we suspect to have the same (or similar enough) rate constants and describe them using a single rule.  So the point of rule-based modeling is not to enable the modeler to build more complex models with additional rate constants, but rather to concisely build models where many reactions have the same rate constant.  This is not as uncommon as one might initially think, as, for example, the binding of a ligand to a receptor is unlikely to depend on the phosphorylation state of the intracellular domain of the receptor.  So the binding of the ligand to the different phospho-forms of the receptor can be described with a single rule instead of many reactions. 

 

Even more importantly, if you have reactions with distinct rate constants that therefore cannot be combined with other reactions into a more general rule, it doesn’t mean that you can’t use rule-based modeling tools!  In this case, each rule essentially represents a single reaction, so there’s no real cost to using a rule-based approach.  But even with only a few reactions that can be represented by a single rule, rule-based modeling can help by keeping your knowledge and assumptions organized (by keeping these reactions grouped into a single rule), and preventing you from making mistakes (by ensuring that these reactions actually have the same rate constant as you intended).

 

So I argue, why not use a rule-based approach?  Rule-based modeling is a strict super-set of reaction-based modeling.  You can’t lose.  I believe that in another ten years, the most widely used modeling tools will enable rule-based models, and rule-based modeling will be common.  Now we just need to develop the right set of tools to make it happen.  I am officially announcing a challenge not only to us here at Plectix BioSystems, but to all modeling tool makers out there.  Let’s make it happen.