Feng Feng, Ph.D.

FengResearch Assistant Professor of Microbiology
72 East Concord Street
Office: L519; 617-638-4123
Lab: L519
ffeng@bu.edu

B.M. Peking University Health Science Center
M.E. Cornell University
Ph.D. Cornell University

My research focuses on the innate immunity and dendrite cell immunology, as well as the development of computational and mathematical models and software tools for understanding/profiling immune responses. I am particularly interested in understanding the Toll-like receptor (TLR) induced transcriptional factor pathways.

TLR and innate immunity. The innate immune system, comprising the cells and mechanisms that protect the host from infection in a generic and non-specific manner, functions not only as the first line of defense but also as a trigger of the following specific adaptive immune responses. Among the potent activators of innate immune responses are Toll-like receptors, activation of which unwinds a tightly controlled transcriptional program that involves approximately 100 transcription factors and results in changes in more than 1000 genes. In order to fully understand the positive and negative control of the complex transcriptional network involved in this process, high-throughput systems biology approaches are necessary. Our research on dendritic cells through large scale gene-expression array assays combining quantitative analyses has identified a group of important transcriptional factors regulating the TLR-stimulated innate immunity. The transcription factors include Mxd1, Hoxa1, Jdp2, Pml, Nr4a3, Hivep3, Maff, Axud1, Foxp4, AI481105, Ehf and Aff3.

Computational immunology and immune modeling. Computational immunology is a field that applies high-throughput genomic, bioinformatics and mathematic approaches to immunology. Our research aim is to convert immunological data into computational problems, solve these problems using mathematical and computational approaches and then convert these results into immunologically meaningful interpretations. We also develop computational tools for monitoring and profiling the immune responses. Some areas of current interest are gene expression, deep sequencing, cell division, single cell PCR and image analysis.

Selected Publication:

  1. Lan Zhu, Feng Feng and Carlos Bustamante. A regression-based approach for estimating recombination rate from population genomic data. BIOCOMP’11 Proceedings. 2011 (BIC3553).
  2. Shan Jiang, Chaoran Li, Virginie Olive, Erik Lykken, Feng Feng, Jose Sevilla, Lin He and Qi-Jing Li. Molecular dissection of the miR-17-92 cluster’s ciritical dual roles in promoting Th1 responses and preventing inducible Treg differentiation. Blood. Oct, 2011. (doi:10.1182/blood-2011-05-355644).
  3. Feng Feng, J Gavalchin, et al. 17β-Estradiol (E-2) administration to male (NZB x SWR)F1 mice results in the onset of glomerulonephritis with an associated increase in IdLNF1-reactive memory T-lymphocytes. Lupus. 2011 (doi: 10.1177/0961203311425519).
  4. Feng Feng, Ana Paula Sales, et al. A Bayesian Approach for Estimating Calibration Curves and Unknown Concentrations of Immunoassays. Bioinformatics. (2011) 27 (5): 707-712 (doi:10.1093/bioinformatics/btq686 2010. 12.10)
  5. Feng Feng, Jennifer Nyland, Michelle Banyai, Arthur Tatum, Allen E. Silverstone and Jerrie Gavalchin. The induction of the lupus phenotype by estrogen is via estrogen receptor-α dependent pathway. Clinical Immunology. 2010; 134(2);226-36 (doi:10.1016/j.clim. 2009.10.004).
  6. Mitha F, Lucas TA, Feng Feng, et al. The Multiscale systems immunology project: software for cell-based immunological simulation. Source Code Biol Med. 2008 Apr 28;3:6.
  7. Zhu L, Zhang Z, Feng Feng, et al. Single nucleotide polymorphisms refine QTL intervals for hip joint laxity in dogs. Anim Genet. 2008 Apr; 39(2); 141-6.
  8. Chan C, Feng Feng, Ottinger J, Foster D, West M, Kepler TB. Statistical mixture modeling for cell subtype identification in flow cytometry. Cytometry A. 2008 May 21.
Primary teaching affiliate
of BU School of Medicine