2013 Research
Components of the research project include learning about computational methods to study large molecular biology datasets such as DNA microarray and proteomic data, analysis of such data generated from engineered tissues, literature analysis of results and predictions, and experimental follow-up of predictions. Students will learn how to work together in teams. They will gain an understanding and appreciation of how computer science, tissue engineering, and molecular biology can be fruitfully combined to study cellular processes.
2012 Undergrad Research Symposium
2012 VBI Undergraduate Research Symposium
One graduate student or post-doctoral researcher in the groups of the investigators will frequently meet with the participants in the institute and share their experiences in research, life as a graduate student, and subsequent career plans.

2012 Research Project
Computationally-Driven Experimental Biology in Engineered Tissues

Nina Blanson1, Biomedical Engineering; Demarcus Briers2, Biology; Kali Manning3, Biomedical Engineering; Dr. Richard Helm4, Dr. T.M. Murali5, Dr. Padma Rajagopalan6

1Yale University, New Haven, CT, 2Prairie View A&M University, Prairie View, TX, 3Worcester Polytechnic Institute, Worcester, MA, 4Department of Biochemistry Virginia Tech, Blacksburg, VA, 5Department of Computer Science Virginia Tech, Blacksburg, VA, 6Department of Chemical Engineering Virginia Tech, Blacksburg, VA

The overall goal of our interdepartmental collaboration was to build systems biology models of bioengineered liver tissues. The 3D liver mimic developed by the Rajagopalan lab consists of a collagen base, hepatocytes (the primary cell type in the liver), a polyelectrolyte multilayer, and at least one other type of liver cell. Ongoing work includes transcriptomic, proteomic, and computational analysis to determine how intercellular signaling maintains hepatic phenotype in the liver models.

To create a gold standard of hepatic proteins, we determined the most abundant proteins in Rattus norvegicus liver cells, and analyzed these identified proteins in the context of a rat protein interaction network. We used liquid chromatography and mass spectrometry protocols on freshly isolated liver samples resulting in the confident identification of 253 proteins. Liver specific enzymes such as Carbamoyl-phosphate synthase, the rate limiting step of the urea cycle, appeared most often on the list of identified proteins reinforcing the accuracy and relevance of the protein set.

Next, we used the STRING network of rat protein interactions and the 253 recovered proteins to create a reduced, liver-specific network. We employed two algorithms, Speed and Performance In Clustering (SPICi), and Molecular Complex Detection (MCODE), to compute protein clusters within this network. Finally, we used Generic GO Term Finder to determine the biological functions enriched in the three largest clusters determined by SPICi and MCODE. All clusters were enriched in metabolic processes that are well known to be performed by the liver. Our results further reinforced the protein identification steps.

These data provide a foundation for comparing 3D liver mimics with in vivo liver cells. In achieving these objectives, we learnt the fundamental elements of interdisciplinary academic collaborations.

2013 Program Dates: May 28 - August 3
Application deadline is February 4, 2013

Computationally-Driven Experimental Biology in Engineered Tissues is funded by NSF Award No. DBI-1062380