C-ME: Collaborative Molecular Modeling Environment

Currently biomedical research leverages a combination of disjointed technologies to record data produced by and associated with that research.  The result is that much of the data which researchers share is stored in stand-alone or disconnected systems.  Over time the context used to discuss key concepts can be lost and in some cases the thoughts of scientists are not captured and retrievable.  The Kuhn-Stevens laboratory at The Scripps Research Institute (TSRI) is looking to create an integrated system which brings together many different types of data and allows researchers to collaborate on this data in such a way that the researchers have easy access to historical data while still allowing them to add new research, external reference data and analysis to their project’s data store.

 

Several distinct challenge areas exist whereby technology could enable a higher level of productivity and collaboration.  For example, the research and analysis that occurs over weeks, months, and sometimes years gives birth to a detailed molecular model.  To the layman, the model looks like a blob of atoms.  However, to the researcher, the model might reveal the secrets to understanding protein-protein interactions critical in such disease as many cancers.  Spinning, twisting, and turning the molecules in a virtual graphical environment reveals the smallest of atomic details that spawn hundreds of discussions, dozens of research papers, and further research.  Groups across the world can view the molecules and share complex thoughts and ideas.  But without the proper tools, collaboration can only occur in pockets, discussions are lost in email threads, and ideas and experiments are re-hashed because they aren’t effectively collected.  Good collaborative tools tuned to the needs of scientists, research technologists, and doctors simply do not exist.

 

In collaboration with Interknowlogy, LLC (Carlsbad, CA) and support from Microsoft, Inc., we have developed and are further improving a software client named C-ME to enable improved collaboration among scientists.

Goals

 Improve Collaboration

• Complex experimental data
• Within TSRI and with outside organizations.

Capture more data electronically

• Images
• Discussions
• Structured Data

To provide project data and decisions in context – e.g. annotations on 2D and 3D objects

Leverage existing productivity applications

• MS servers and Office applications
• Purpose specific viewers (e.g. Pymol)

C-ME Architecture

Smart client for rich visual experience and ease of finding information   XML Web Service interfaces   MOSS 2007 Server for storing documents and metadata

 

Model for collaboration: Data organization

• Hierarchical project organization
• Projects
• Entities
• Annotation (Files, notes, images, URLs, screen captures).
• Projects: Images of project overview
• RTK Therapeutics – structural and thermodynamic characterization of protein/protein interactions
• SARS – multiple crystal structures
• CBRP (Cancer cell detection) – multiple blood samples
• Entities are 2D or 3D: Images or PDB
• Select entire image or structure
• Select portions of image or selection of atoms/residues
• Annotations: add information by attaching information

 

Current Uses of C-ME

 

Generally, C-ME is being used during regular laboratory group meetings to discuss and share current progress and problems in a particular research project.  Rather than having to create new PowerPoint presentations, the presenter can start the C-ME application and browse to the appropriate project to share the current thoughts and results.  The annotations placed there collaboratively by other researchers working on that project can be viewed and edited and new annotations can be added on the spot.

 

Similarly, C-ME is being used to provide a tour of a completed crystal structure using the published research paper as a basis.  Now, the reader can step through the C-ME annotations extracted from the paper and watch as the relevant portions of the structure are highlighted to place data in the context of the structural features.  In addition this guided display feature of C-ME will also be used to collaborate with researchers outside of TSRI.

 

From bench-top perspective, C-ME is being used in both structure-based and cell-based research projects. There are currently two primary project models which represent similar but different challenges for the collaborative environment.  The process model which has been most clearly discussed in the early stages of this project is that used for the analysis of molecular structures.  This process starts in the lab with the collection of data beginning with the process of synthesizing the target molecule.  Data involved in this process include the initial ‘gel’ images that help to determine the size, purity and rough amount of the molecule produced.  In addition, a chromatogram image is generated that further describes the purity and state of the molecule that is subsequently used in the protein crystallization process.  After crystals of the molecule are obtained, the process continues by exposing these crystals to X-rays and collecting the diffraction images which researchers analyze leading to the 3-D model of the molecule.

 

The second project model is that used by the Cancer Bioengineering Research Project (CBRP).  This project is focused on detecting rare circulating tumor cells in blood specimens.  These cells have similarities that allow for their automated detection, and determining that the cells are cancerous requires fluorescent microscopy analysis of the cells.  The focus of the project is detecting and characterizing circulating tumor cells in the blood of cancer patients and information is collected on each distinct specimen.  These results need to be associated with patients and each specimen might result in 200+ different images each of which might contain simple annotation from a pathologist and which need to be categorized. 

 

The research team is currently forced to manage this association in a rather labor intensive fashion as the number of valid images is identified and then the types of valid images are categorized.  Similarly samples need to be associated with a patient and the results for a given patient need to be associated with their current treatment regime etc.  Thus the process and problem domain for such a project requires a slightly different approach to managing project data.  However, the underlying collaborative requirements are very similar to those of the SARS analysis project.  Of note the data in the CBRP project consists of more tabular style data with fields representing key attributes which need to be sorted on and grouped.  The CBRP project has significantly more interest in placing attributes which support sorting onto the files associated with its project notebook and being able to update and retrieve those data elements from a single source.