Vincent J. VanBuren, PhD, FAHA

Education and Training

• Cedar Crest College, BS, Biology, 1994
• Lehigh University, PhD, Molecular Biology, 2002

Research Interests

• Computational Systems Biology
• The value of computational approaches to biology may be summarized in three categories: (1) interpretation, or the analysis of biological data for the purpose of uncovering hidden relationships using an accepted model, (2) prediction, or using models to formulate computational hypotheses that may be further explored with bench experiments, and (3) model generation, or producing a new model by fitting known constraints on an in silico system in order to generate known results and new hypotheses.
• VanBuren's group has two main interests that span the above approaches: (1) reconstruction of biological networks from data-derived theoretical networks and (2) modeling the emergent behavior of stochastic biological systems. With regard to network reconstruction, the team is currently focusing on the problems associated with comparing theoretical networks, or so-called relevance networks derived from high-throughput screening or other data streams, to networks built using traditional reductionist methods. Their goal is to reconstruct transcriptional regulatory networks involved in murine cardiac development. With regard to modeling, they are interested in modeling the emergent behavior of their reconstructed networks, as well as the emergent behavior of self-assembling cellular structures such as microtubules.
• Several projects feed into these two main interests. To build reliable networks from high-throughput data, the data must be carefully annotated and curated, and for microarray data, it is highly desirable to obtain estimates of absolute transcript abundance. VanBuren's team is developing data-handling standard operating procedures and transcript abundance estimation methods to address these important issues. Additionally, they are constructing a suitable systems biology database for murine cardiac development to aid their efforts in comparing their reconstructed networks to "known" networks. It is expected that their development of a public database for this purpose will be of value to the scientific community at large.