1. Advanced fluxomic modeling (FBA and 13C-MFA) (NSF).
2. Syngas fermentation process analysis (NSF).
3. Metabolic engineering for chemical synthesis (DOE and NSF STTR).
4. Data mining and machine learning for rational strain design (NSF).
Industrial collaborations and services.
(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/ )
Synthetic Biologists employ 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, the Tang Lab is interested in these "hypothesis-free" projects for characterizing novel microbial metabolisms.
The metabolism of a host has been optimized through many years of natural evolution. Loading of Synthetic Biology parts may drain cell limited resources and disrupt biomass production and essential 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, many design-build-analysis-learn cycles are needed for balancing these cost/benefit tradeoffs. To overcome this problem, flux analysis (a central component of ME) and genome scale models (GSMs) are essential to measure and allocate cellular energy and carbon resources appropriately. In addition, data driven models (such as machine learning) may replace human intuition to defining and implementing synthetic biology designs and reduce the uncertainty of GSM results. It explores the gap between what is possible and what is deliverable.
I have always found that plans are useless, but planning is indispensable --- Eisenhower