Research Overview

Research Approaches

Metabolic analysis, process modeling and engineering: The Tang lab applies process or metabolic modeling, omics analysis, and 13C-Metabolic Flux Analysis (MFA) to decipher microbial metabolism, perform genome-to-phenome mapping, and optimize bioprocesses. The metabolic models integrated with omics data can also reveal complex cellular regulations. Our Lab projects focus on: 1) novel pathways in nonmodel microbial species; 2) flux network for using or co-utilizing different substrates; 3) flux organizations in engineered or evolved strains under stressed conditions, and 4) simulation or prediction of complex cellular processes. Recently, the Tang lab is working on knowledge mining and AI approaches to guide biomanufacturing development. 

Fig. 1. 13C based metabolic analysis pipeline

Research Questions about the Rules of Life 

1) Multi-level and interconnected metabolic regulations: High throughput engineering tools and omics analyses have been well-established. However, it is still difficult to decipher complex cellular performance under various growth conditions. How to integrate metabolic models with data driven approaches to improve our understandings and designs of microbial metabolism for bioproductions?      

2) Host genetic/nongenetic heterogeneities: How to understand metabolic variations in single cells?  How engineered strains are evolved in bioreactor conditions? Where are mutational locations? How to correlate the growth stresses and metabolic burdens to mutational driving force? How to design strains to be tolerant of metabolic burden?  

3) Metabolite channeling:  native strain may coordinate cascade enzymes via metabolite channels or metabolons. Such regulatory strategies reduce intermediate toxicity and increase enzyme efficiency, which lead to robust cellular functions under stressed conditions. It is still unclear how cells control degree of channeling in its central pathways and how channeling governs flux network.

Fig 2: Cell metabolic regulations (Channeling is often overlooked.)

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On-going research

Project 1. Elucidate and mitigate deleterious mutations during recombinant production scale up: Microbial metabolisms are complex systems. Unlike controllable modular systems (such as 'hardware'), re-programming cellular processes or creating novel synthetic hosts are highly constrained by multi-level metabolic regulations and random evolutionary mutations. To understand such challenges and their impacts on bio-manufacturing, metabolomics, 13C-MFA, genome resequencing and RNA-Seq may reveal mutational hot spots and to design effective mitigation methods. See our recent paper (Current Opinion in Biotechnology, 2020, 66:227-235).

Fig. 3: Scale up of engineered hosts (over generations, non-producers will become dominant.)

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Project 2. Study on proximity channeling:  Metabolic models seldom consider intracellular diffusion limitations. Intriguingly, cascade enzymes of long-step pathways might self-assembled to form proximity channeling via weak interactions. However, proximity channeling of central pathways in living cells is difficult to verify and its roles on metabolic regulations are unclear. We are interested in deciphering proximity channeling and interactions between cascade enzymes in central pathways via combined 13C-metabolomics, flux modeling, metabolomics, in vitro enzyme assay, and targeted genetic engineering. See our recent papers (AICHE Journal 2019; Metabolic Engineering 2019; Biotech Advance 2018).

Project 3. Pathway discovery and metabolic analysis of nonmodel species or genetic engineered workhorses: We have characterized over 20 microbial species, including cyanobacteria, Shewanella, Mycobacteria, Geobacter, Geobacillus, E.coli, Yarrow, Sphingobium, Dehalococcoides, Thermoanaerobacter, Heliobacteria, Roseobacter, Chlorobaculum, Chloroflexus, Chlorella, Clostridium, Pseudomonas, Colwellia, Rhodococcus, Yarrowia ... We are studying diverse feedstock for microbial utilization or co-utilization, including CO₂, oils, anaerobic digestion wastes, plastics, lignin byproduct, and formate. We have collaborated with many other groups for understanding metabolic pathways and regulations.

Project 4. Machine learning for biomanufacturing:  We work on knowledge mining and machine learning of metabolic engineering and bioprocess data. We aim to extract new insights from large datasets using natrual language processing (GPT-4). We are developing machine learning methods to improve model predictions and to optimize bioprocess controls. Please see papers on PlosOne (vol 14, e0210558), Biotech Advance (vol 36, 1308-1315), and PlosComputational Biology (vol 12, e1004838, machine learning software mFlux).

Project 5. Environmental Bioremediation, Plastic/Lignin Waste Upcycling and CO2 capture: The Tang Lab works on N and P waste remediations, regolith utilizations, plastic degradation by microbial community, conversion of bio-wastes to biopolymer or natural products, algal process modeling and optimizations, and harmful algal bloom controls.