Welcome to the Tang Lab!
My lab employs metabolic modeling, systems "omics" analyses, and genetic engineering to investigate and modify cell metabolism under diverse growth conditions. The metabolic modeling/analysis tools can understand cell metabolisms and identify rate-limiting steps. Combining "omics" with metabolic engineering will provide an iterative route to develop microbial cell factories (design-build-analysis-learn loop). On the other hand, discovery and development of new platform strains using nonmodel microbes is a promising direction so that we can take advantage of native pathways in platform strains for biosynthesis. Therefore, my Lab often works on "hypothesis-free" researches with environmental microbiologists for characterizing novel microbial metabolisms. On the other hand, the microbial metabolism has been optimized through many years of natural evolution. Loading of Synthetic Biology parts may drain cell resources and disrupt cellular functions. When the host cannot afford these burdens, undesirable physiological responses will take place - removal of one bottleneck in the parent strain breeds new problems in the daughter strain. Therefore, synthetic biology strains are difficult to achieve the desired performance. To overcome these problems, we use flux analysis and data driven models (such as machine learning) to predict outcomes from synthetic biology designs (What is possible? What is deliverable?).
My lab is supported by two grants: DoE BER Rhodococcus project
(Co-PI, 2017~2020); NSF MCB project on machine learning for microbial cell
factories (PI, 2016~2019). My lab also receives small amount of money from
industries. Currently, we are helping Arch Innotek develop nonmodel yeasts for
aroma production via an NSF IIP phase 1 project (Co-PI, 2017~2018). The
annual research funding for my lab is ~$220k (only half of my department
average), but my students are highly productive with limited resources.
On average, each PhD student publishes at least four first
author papers (plus a few co-author papers) from his/her PhD studies.
My lab is supported by two grants: DoE BER Rhodococcus project (Co-PI, 2017~2020); NSF MCB project on machine learning for microbial cell factories (PI, 2016~2019). My lab also receives small amount of money from industries. Currently, we are helping Arch Innotek develop nonmodel yeasts for aroma production via an NSF IIP phase 1 project (Co-PI, 2017~2018). The annual research funding for my lab is ~$220k (only half of my department average), but my students are highly productive with limited resources. On average, each PhD student publishes at least four first author papers (plus a few co-author papers) from his/her PhD studies.
(2010-2011) "CO2 utilization via algal process." (Consortium for Clean Coal Utilization, Peabody Energy, Arch Coal, Ameren)
(2011-2012) "Integration of anaerobic digestion with free fatty acid production via engineered microbial species" (Gates Foundation)
(2012-2013) "Microcoleus vaginatus cultivation for bio-fertilizers" (Terra Biologics, http://www.agventuresalliance.com/team-view/terra-biologics-llc/)
(2014~2015) "Lignin Degradation using a soil bacterium" (Sandia National Lab)
(2015~2016) "Biofuel production from agricultural wastes and biogas" (Helee, LLC http://www.helee.com/)
(2016-present) "Fermentation optimization of yeast strains to produce natural nutrition" (Arch Innotek, http://www.arch-innotek.com/ )
I teach process control (EECE401) and bioprocess engineering (EECE506). Both courses cover significant amount of computer modeling and data analysis. As an undergraduate director, I enjoy teaching very much. I have open door policy and students can stop by for questions at any time. Moreover, I have mentored over 50 UG/high school students for their summer research in my lab via NSF REU and MAGEEP programs. My lab also supports WashU iGEM teams. Each year, my lab hosts a few MS students for independent studies.
Tang lab PhD students need to learn both lab skills and computer modeling. Moreover, PhD students often do internship to accumulate industrial experience (e.g., Mary for BASF, Gang for Sigma, Whitney for LBL, Lian for Sandia National Lab, Tola for Monsanto, and Jeff for Arch-Innotek). So far, Seven PhD students have graduated from the Tang lab and none of PhD students quits. After graduation, most of students work for industry or academia except Whitney (she currently works as a program manager in US EPA in Washington DC). The first two PhD students from the Tang Lab landed faculty jobs (as tenure track assistant professors): Xueyang at Virginia Tech and Arul at Arizona State University. However, due to current funding situations and stressful environments, students are not encouraged to pursue faculty positions any more. Interestingly, recent graduates all become data scientists in big companies (Gang works for Sigma-Aldrich; both Ni and Tola work for Monsanto).
PI Tang rarely goes to national conferences. The limited travel funds mainly support students so that they can have at least two trips to AICHE or SIMB during their PhD studies. Trained as a chemical engineer, PI Tang's research fields cover synthetic biology, bioprocesses, and environmental engineering. His diverse background prepares him well as a reviewer for papers and proposals. Every year, He enjoys being panelist for NSF or other funding agencies (e.g., EPA and DOE). He also actively review manuscripts (3/month) for various journals, including ACS Synthetic Biology, Bioinformatics, Nature Communications, Nature Biotechnology, Plos Computational Biology, Science, Scientific Report. His other national services are associate editors for three biotechnology journals.