Honors & Awards
GRFP, NSF (2020-present)
Education & Certifications
B.S., Massachusetts Institute of Technology, Chemical-Biological Engineering (2020)
Development of a Quorum-Sensing Based Circuit for Control of Coculture Population Composition in a Naringenin Production System.
ACS synthetic biology
2020; 9 (3): 590-597
As synthetic biology and metabolic engineering tools improve, it is feasible to construct more complex microbial synthesis systems that may be limited by the machinery and resources available in an individual cell. Coculture fermentation is a promising strategy for overcoming these constraints by distributing objectives between subpopulations, but the primary method for controlling the composition of the coculture of production systems has been limited to control of the inoculum composition. We have developed a quorum sensing (QS)-based growth-regulation circuit that provides an additional parameter for regulating the composition of a coculture over the course of the fermentation. Implementation of this tool in a naringenin-producing coculture resulted in a 60% titer increase over a system that was optimized by varying inoculation ratios only. We additionally demonstrated that the growth control circuit can be implemented in combination with a communication module that couples transcription in one subpopulation to the cell-density of the other population for coordination of behavior, resulting in an additional 60% improvement in naringenin titer.
View details for DOI 10.1021/acssynbio.9b00451
View details for PubMedID 32040906
General Method for the Identification of Crystal Faces Using Raman Spectroscopy Combined with Machine Learning and Application to the Epitaxial Growth of Acetaminophen.
Langmuir : the ACS journal of surfaces and colloids
2018; 34 (33): 9836-9846
Crystal morphology is one of the key crystallographic characteristics that governs the macroscopic properties of crystalline materials. The identification of crystal faces, or face indexing, is an important technique that is used to get information regarding a crystal's morphology. However, it is mainly limited to single crystal X-ray diffraction (SCXRD) and it is often not applicable to products of routine crystallizations becasue it requires high quality single crystals in a narrow size range. To overcome the limitations of the SCXRD method, we have developed a robust and convenient Raman face indexing method based on work by Moriyama et al. This method exploits small but detectable differences in Raman spectra of crystal faces caused by different orientations of the crystallographic axis relative to the direction and polarization of the excitation laser beam. The method requires the compilation of a Raman spectral library for each compound and must be built and validated by SCXRD face indexing. Once the spectral library is available for a compound, the identity of unknown crystal faces (from any crystal that is larger than laser beam) can be inferred by collecting and comparing the Raman spectra to spectra within the library. We have optimized this approach further by developing a machine-learning algorithm that identifies crystal faces by performing a statistical comparison of the spectra in the Raman library and the Raman spectra of the unknown crystal faces. Here, we report the development of the Raman face indexing method and apply it to three different epitaxial systems: Acetaminophen (APAP) grown as an overlayer crystal on d-mannitol (MAN), d-galactose (GAL), and xylitol (XYL) substrates. For each of these epitaxial systems, the crystals were grown under various experimental conditions and have a wide range of sizes and quality. Using the Raman face indexing method, we were able to perform high-throughput indexing of a large number of crystals from different crystallization conditions, which could not be achieved using SCXRD or other analytical techniques.
View details for DOI 10.1021/acs.langmuir.8b01791
View details for PubMedID 30053784