Plectix Blog

New Cellucidate Features

By Traci Freed

In recent months we have received valuable feedback from our users and wanted to share information on some exciting, new features that have been introduced to make Cellucidate not only easier to use, but a more efficient platform for doing work.  If you are not a current Cellucidate user, register for an account at www.cellucidate.com.

Save Multiple Simulations
The redesigned model page lets you run a simulation multiple times using different parameters each time and then save these simulations to easily compare the results of each change of parameters.  Manage your simulations to keep those that are important in your research and delete the ones you do not need.

Copy cBooks Faster
The copy feature has been updated to significantly improve the speed at which cBooks are copied.  Remember when you were unable to copy those large, complex cBooks?  Well, now you can copy them quickly no matter the size.  If you've also wanted to share a cBook with a colleague to copy but haven't been able to because it takes too long, now is the time to share it.

Improved Search
Cellucidate users have been asking for a better search function and based on that feedback we've improved search so you can better find the cBooks you are looking for.  Most users want to find cBooks about a specific pathway and with the old search it was difficult to find the particular content you needed.  We've updated this feature so that when searching you will only be given the results for cBooks and users so you can better find exactly what you are looking for.  Why not check it out for yourself?  We've got some great content related to the EGFR pathway and you can see the new search in action.

New Desktop Application
We are excited to announce a new feature many of you have asked for - a desktop application for building Cellucidate models.  We are almost done building this functionality and the new application will provide an environment for writing Kappa text files directly with the added benefits of syntax highlighting, error checking, rule visualization, simulations and static analysis tools such as the contact map.  The new application also integrates Cellucidate to facilitate easy upload of models to Cellucidate.  Those of you who have used our old desktop application, Kappa Factory, will love this new one!  If you would like to be notified when an advance beta copy is available, email us at support@cellucidate.com.

Cellucidate Help Resources
We recently introduced comprehensive help and support resources to assist you as you learn to navigate Cellucidate, use its many features and incorporate it into your daily workflow. The detailed help content includes topics such as "Getting Around Cellucidate" and "Building a Model".  Get stuck while using Cellucidate?  Feel free to ask for help with your issues and questions, request new features you'd like to see and interact in discussions with other Cellucidate users.

As we continue to evolve the Cellucidate experience, we welcome your feedback.  Be sure to email your feedback to support@cellucidate.com.

Universal Analysis of Complex Networks

By Joshua Havumaki

We live in an age of complexity. Large data sets and intricate networks are ubiquitous and underlie many daily events from flight patterns to the spread of H1N1. Biological networks (like those explored by proteomics) are effectively mapped using statistical and other computational approaches.  Although these networks can be represented relatively easily, the technology used to generate the data is startlingly inaccurate. For example, yeast two-hybrid, a commonly used method for discovering protein-protein interactions, is considered to have "medium" accuracy - on the order of 50% false positives.

A recent article in PNAS titled, "Missing and Spurious Interactions and the Reconstruction of Complex Networks" by Guimera et al. outlines a method to accurately analyze different types of complex networks. It strongly relies on the fact that the nodes in networks can be organized into groups. Group membership governs which nodes can interact. 

The algorithm predicts missing or incorrect interactions and reconstructs networks from noisy data. It was successfully applied to five different types of networks ranging from connections within a neural network to interactions within a pod of dolphins, and in all cases it effectively separated signal from noise. This was confirmed by introducing errors into the network that were culled by the algorithm.

Although it has only been tested on relatively small networks, the researchers suggest applying this method to larger protein-protein interaction networks. If successful, this could begin to remove the plethora of false positives created by our somewhat crude technologies.

Uncertainty in Modeling

By Ty Thomson

Earlier this week I was talking with a co-worker’s husband, and he was asking about exactly what we do here at Plectix.  I was trying to explain the idea of biological modeling to him, and I realized that I wasn’t entirely sure if this type of modeling differs from any other.

Like all modeling, you start off with a bunch of knowledge and assumptions.  The model is just an expression of the knowledge and assumptions in a formal (generally mathematical) way that allows you to use computational power to reason about the system (often directly though simulation).

From my understanding, the mathematical descriptions of chemical kinetics are actually fairly accurate, so assuming that the same principles apply to protein-mediated reactions, it would seem that the overwhelming source of inaccuracy in models of protein networks is whether the reactions themselves are right.  Thus, for modeling in systems biology, lack of knowledge is the problem, not representation of the knowledge in a formal/mathematical form.  Is this the case with other forms of modeling?  Or, for example in financial modeling, do we understand the system being modeled but have poor or inaccurate mathematical descriptions of the pieces of the system?

The inaccuracies in biological models appear to me to be firmly rooted in the overwhelming uncertainty that we have in our understanding of what’s actually happening in living systems.  It’s actually pretty valuable that our mathematical descriptions are reasonably good, so that changes in our hypotheses or “knowledge” can be evaluated directly via models without worry that we’re just evaluating our mathematical descriptions of events.  After all, we are much more interested in improving our knowledge about the system itself than in understanding the mathematics required to describe it.  So while biological modeling is pretty lucky in this respect, is this also the case for modeling in other fields?  I’d be interested to find out.

Moving to the Cloud

By Vy Hoang

With the recent interest in cloud computing, it isn’t surprising that big pharma companies, such as Pfizer, Eli Lilly and Johnson & Johnson, are also experimenting with the cloud. Cloud computing offers many advantages for a low price. These companies can easily predict the time spent and the cost of a job on the cloud if they know what job is pushed into the cloud. Thus, the pharmas can manage their money and time in a more cost-efficient way. In addition, cloud computing is fast because of the access to a vast number of parallel CPUs. Pfizer reported that running their Rosetta macromolecular modeling took only 3.5 hours using 500 of Amazon’s Elastic Compute Cloud (EC2) instances instead of 48 hours on their internal computers. The New Jersey based Johnson & Johnson is hosting analytical software such as NONMEM (non-linear mixed effects modeling) in the cloud. Taking advantage of the on-demand cloud infrastructure, Johnson & Johnson is able to scale up or down to run large simulation jobs at will. Eli Lilly is running both non-research and discovery applications in the cloud, a few of which could not run internally. Therefore, the cloud can provide more efficient, scalable servers to run jobs that couldn’t be run in house.

Although the results look promising, cloud computing poses some challenges for pharma companies. The main issues are security and accessibility. Some people view the cloud as an unsecure space and mistakenly feel that cloud resources are just extra servers that Amazon is not using. However, as they learn more about how the model works with its on-disk encryption, in-memory encryption and wire encryption, the people are beginning to come around. Yet, the complexity of cloud computing is an additional factor that is making cloud computing difficult to use. Without the help of an IT team, the pharmas would have considerably difficulty in understanding how to use the cloud. Pfizer stated that it was hard to get metrics out of the cloud such as what jobs are running or how long they are taking without any IT help. Whatever’s going on in the cluster is not as simple as you think. The pharmas simply want the cloud to be easier to use with less technical complexity so the users can do what they need to do. Even with these challenges, pharma companies are not deterred because the vendors are giving in to their demands, making it more manageable to use the cloud.

These early cloud adopters believe that cloud computing is the next step in improving the quality of research. Researchers will be able to solve problems in the cloud that were considered impossible before. As long as you know what you’re getting into, cloud computing can be the right way to go.

Biological Logic

By Traci Freed

Cellucidate was recently in a Nature feature about intuitive approaches to computer modeling for biologists.  The article gives an interesting discussion of "executable biology", a new approach to biological modeling that makes simulations of cells easier for scientists to build, understand and study.  To read the full article, visit http://www.cellucidate.com/26nov09.html.