Computational Biology and Research Informatics
Path BioResource was created by the Department of Pathology and Laboratory Medicine to provide administrative support to the departmentally-based shared resource laboratories.
Through partnering with the School of Medicine administration, as well as Centers and Institutes within the University community, Path BioResource seeks to provide all investigators access to high quality,
cost-effective advanced technology services as well as the scientific expertise to use these technologies effectively in their research efforts.
Research informatics refers to the development and operation of computer-based systems that facilitate the operational management of Core and Center facilities.
Over the past six years Path BioResource has been developing and refining a web-based system for Service Center management.
This system provides tools for monthly invoicing, usage tracking, and financial reporting. The Path BioResource database utilizes University of
Pennsylvania systems such as the PennERA to validate and monitor accounts, the ISC feeder to automate accounting processes, and Compliance Guidelines as we strive to maintain best business practices.
In the last two years, in addition to supporting facilities within the Department of Pathology and Laboratory Medicine, Path BioResource has begun partnering with other departments, generally at the
other department's instigation. This has arisen because Path BioResource has automated accounting practices essential for service centers to function, and it enables business administrators and grants
managers to closely monitor federal funding expenditures.
Our computational biology activities are focused around the theme of pattern discovery in high dimensional biology.
Resident knowledge includes advanced methods of computational modeling, machine learning, and high dimensional data analysis algorithms.
A particular area of concentration is the analysis of multiparameter flow cytometry (FCM) data, and in particular the application of tools we have developed
for "fingerprinting" FCM data as well as using clustering and empirical modeling methods to extract information from flow cytometry experiments.
We are users as well as developers of the R Statistical Computing Environment. Our package FlowFP for cytometric fingerprinting
of flow cytometry data is a part of the Bioconductor
collection of R packages and is integrated with over a dozen other packages specifically dedicated to flow cytometry, creating a comprehensive toolkit for advanced analysis of FCM data.
We have expertise in the use of all of these tools to create comprehensive analysis solutions.