Zhiping Weng graduated from the University of Science and Technology of China in 1992 with B.S. in Electrical Engineering. In 1993, she entered the graduate program in Biomedical Engineering at Boston University, and received her Ph.D. in 1997. The focus of her thesis research was in computational biology, specifically on calculating binding free energies of protein-protein interactions. In January 1997 Dr. Weng was appointed Instructor of Biomedical Engineering at Boston University. In that capacity she taught and conducted research, and had primary responsibility for the development of the Bioinformatics program and the core curriculum in Bioinformatics.  In January 1999 the Biomedical Engineering Department at Boston University decided to grow in the area of Bioinformatics.  After a national search, the department appointed Dr. Weng a tenure-track assistant professor. In September 2003, Dr. Weng was promoted to Associate Professor with tenure. Until December 2007, Dr. Weng’s research had been focused on developing computational methods to obtain a predictive understanding of transcriptional regulation and protein-protein interaction. She had published 90 articles, including 75 peer-reviewed journal articles.

On 1 January 2008, Dr. Weng moved to University of Massachusetts Medical School to build and direct a new Program in Bioinformatics and Integrative Biology. She is a full professor, with tenure in Department of Biochemistry and Molecular Pharmacology. She continues research on computational analysis of transcriptional regulation. She has started to study epigenomics and nucleosome positioning, which play important roles in transcriptional regulation. In addition, she is investigating the function and regulation of small RNAs in metazoan. For more information, please visit Dr. Weng's lab Website (http://zlab.umassmed.edu/ ).

Bioinformatics and Computational Genomics

focus our research on regulatory molecules and their interactions, such as regulatory proteins and their DNA/RNA target sites, small silencing RNAs and their RNA targets, and protein-protein interaction. Our lab has three main projects:

• Gene Regulation

We aim to develop computational methods for understanding the molecular mechanism of gene regulation. We develop novel ways to discover transcription factor binding sites in genomic DNA. Because the sequences of these sites are of low information content, we pursue multiple approaches, including better characterizing transcriptional start sites and alternative proximal promoters, detecting clusters of transcription factor binding sites using probabilistic models, and identifying genes that are co-regulated and taking advantage of the enrichment of the sequence motifs in their promoters. We take an integrative approach using extensive high-throughput genomic and epigenomic data, such as chromatin-immunoprecipitation of transcription factors, nucleosome positioning, histone modifications, DNA methylation, and DNA replication.

• Protein Docking

We develop methods to compute binding affinities between protein molecules. Combining this ability with a fast Fourier transform-based search algorithm, we develop computational methods for predicting protein-complex structures. We take a multiple-stage approach, i.e., we develop an initial stage algorithm ZDOCK to perform an exhaustive search in the translational and rotational space, and subsequent refinement algorithms such as ZRANK for structure refinement and reranking. We participate in the community-wide blind test of protein docking algorithms CAPRI.

• Small Silencing RNAs

We develop computational methods to understand the biogenesis and regulatory mechanisms of small silencing RNAs (microRNAs or miRNAs, small silencing RNAs or siRNAs, and PIWI-interacting RNAs or piRNAs). We build computational pipelines to analyze high-throughput sequencing data of small silencing RNAs. We map tens of millions of sequence reads to the genome, quantify their length and nucleotide properties, genomic localization, relative abundance in different cell types and/or genotypes, evolutionary conservation, and discover any other features that can uncover the biogenesis and target recognition of the small silencing RNAs.