A5081263144- CRISPR and Genetic Engineering
- SARS-CoV-2 detection and testing
- Advanced biosensing and bioanalysis techniques
- RNA and protein synthesis mechanisms
- Biosensors and Analytical Detection
- RNA modifications and cancer
- Advanced Memory and Neural Computing
- Mosquito-borne diseases and control
- RNA Interference and Gene Delivery
- Bacterial Genetics and Biotechnology
- RNA Research and Splicing
- Light effects on plants
- SARS-CoV-2 and COVID-19 Research
- Machine Learning in Bioinformatics
- bioluminescence and chemiluminescence research
- Photosynthetic Processes and Mechanisms
- Malaria Research and Control
- 3D Printing in Biomedical Research
- Photoreceptor and optogenetics research
- Bioinformatics and Genomic Networks
Massachusetts Institute of Technology
2019-2023
Harvard University
2019-2023
University of Wisconsin–Madison
2015
A CRISPR set of materials technology is best known as a gene editing tool. English et al. developed group stimuli-responsive hydrogels to respond the programmable nuclease Cas12a (see Perspective by Han ). The undergo molecular macroscopic changes after Cas12a-dependent cleavage double- or single-stranded DNA integrated into gel. authors show controlled release particles linked imprisoned within DNA, degradation gel with solely forming cross-links, and permeabilization partially cross-links....
An integrated, low-cost, sample-to-answer, CRISPR-based diagnostic detects SARS-CoV-2 and variants from unprocessed saliva.
Significance Detection of submicroscopic malaria in asymptomatic individuals is needed for eradication and remains a diagnostic gap resource-limited settings. Nonfalciparum clinical diagnostics are second gap, as these infections have low parasite density commonly undetected. We describe an integrated, 60-min, ultrasensitive specific CRISPR-based the four major pathogenic Plasmodium species that can fill gaps. Using SHERLOCK (specific high-sensitivity enzymatic reporter unlocking) platform,...
Abstract Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior these synthetic biology components remains a challenge, situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as canonical riboswitch model in biology. To facilitate DNN training, synthesize characterize vivo dataset 91,534...
The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim incorporate ML in their research. Automated (AutoML) algorithms can address challenges that come with applying the life sciences. However, these are rarely used systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.g., nucleotide, amino acid, or glycan sequences) cannot be easily compared other AutoML algorithms....
Genetically encoded fluorescent markers have revolutionized cell and molecular biology due to their biological compatibility, controllable spatiotemporal expression, photostability. To achieve in vivo imaging whole animals, longer excitation wavelength probes are needed the superior ability of near infrared light penetrate tissues unimpeded by absorbance from biomolecules or autofluorescence water. Derived infrared-absorbing bacteriophytochromes, phytofluors engineered fluoresce this region...
The coronavirus disease 2019 (COVID-19) pandemic has brought about the unprecedented expansion of highly sensitive molecular diagnostics as a primary infection control strategy. At same time, many laboratories have shifted focus to severe acute respiratory syndrome 2 (SARS-CoV-2) research and diagnostic development, leading large-scale production SARS-CoV-2 nucleic acids that can interfere with these tests. We identified multiple instances, in independent laboratories, which generated...
Abstract Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior these remains a challenge, situation that could be addressed through enhanced pattern recognition from deep learning. Thus, we investigate Deep Neural Networks (DNN) to predict toehold switch function as canonical riboswitch model in synthetic biology. To facilitate DNN training, synthesized characterized vivo dataset 91,534 switches spanning 23...