Cell Collective: an interactive modeling resource for the scientific community
Advisor Information
Tomas Helikar
Location
Dr. C.C. and Mabel L. Criss Library
Presentation Type
Poster
Start Date
6-3-2015 11:00 AM
End Date
6-3-2015 12:30 PM
Abstract
The advantages of computational modeling in biomedical research include the ability to analyze and simulate complex biological systems, acting as a predictive agent to generate new hypotheses capable of being tested in the laboratory. Until recently, computational modeling has been limited to those with a background in computational methods. In addition, models are published in a format not easily manipulated or replicated by the majority of the scientific community. In order to expand the usability of computational modeling to a wider audience, researchers need 1)an easily accessible modeling environment and 2) an environment enabling them to create, modify, and edit previously built models. Beginning to address these gaps was the goal of my FUSE project, using software called the Cell Collective. Cell Collective is a computational modeling platform allowing researchers to collaboratively build, analyze, and simulate computational network-models. Through its intuitive user interface, the platform makes computational modeling accessible to laboratory researchers without prior training in mathematics or computer science. Using this platform, I built, annotated, and validated 17 models available to the research community. The models were built with Cell Collective’s Bio-Logic Builder, capable of manipulating a network based on biochemical context instead of requiring direct input of complex mathematical functions (which are compiled in background of the software). The models were fully annotated using Cell Collective’s Knowledge-Base, a repository containing the citations and evidence supporting every interaction within a model available for review and further expansion by other researchers. Ultimately, the models built for my project provide a sizeable and accessible environment for researchers to expand upon and modify based on their own scientific interests.
Cell Collective: an interactive modeling resource for the scientific community
Dr. C.C. and Mabel L. Criss Library
The advantages of computational modeling in biomedical research include the ability to analyze and simulate complex biological systems, acting as a predictive agent to generate new hypotheses capable of being tested in the laboratory. Until recently, computational modeling has been limited to those with a background in computational methods. In addition, models are published in a format not easily manipulated or replicated by the majority of the scientific community. In order to expand the usability of computational modeling to a wider audience, researchers need 1)an easily accessible modeling environment and 2) an environment enabling them to create, modify, and edit previously built models. Beginning to address these gaps was the goal of my FUSE project, using software called the Cell Collective. Cell Collective is a computational modeling platform allowing researchers to collaboratively build, analyze, and simulate computational network-models. Through its intuitive user interface, the platform makes computational modeling accessible to laboratory researchers without prior training in mathematics or computer science. Using this platform, I built, annotated, and validated 17 models available to the research community. The models were built with Cell Collective’s Bio-Logic Builder, capable of manipulating a network based on biochemical context instead of requiring direct input of complex mathematical functions (which are compiled in background of the software). The models were fully annotated using Cell Collective’s Knowledge-Base, a repository containing the citations and evidence supporting every interaction within a model available for review and further expansion by other researchers. Ultimately, the models built for my project provide a sizeable and accessible environment for researchers to expand upon and modify based on their own scientific interests.