UROP Proceedings 2021-22

School of Engineering Department of Chemical and Biological Engineering 75 The Construction of Dual-functional Synthetic RNA Control Unit Supervisor: KUANG Becki Yi / CBE Student: WONG Hiu Ching / CBGBM Course: UROP1100, Fall Techniques of polymerase chain reaction (PCR), gel electrophoresis, urea-PAGE, in vitro transcription (IVT), cell passage and transfection are explored and investigated in this UROP project. By performing a series of experiments, it is shown that by using these techniques, any DNA fragments containing desired genes can be amplified, fused and transcribed before being introduced into cells for further expression and growth given the correct conditions and procedure. Through this UROP project, these wet-lab skills have been acquired and practiced, and can be further applied to future researches in fields such as genetic function and cell type characterization, as well as protein synthesis and drug discovery. The Construction of Dual-functional Synthetic RNA Control Unit Supervisor: KUANG Becki Yi / CBE Student: YAM Tsz Yui / BIEN Course: UROP1000, Summer Over the past few decades, deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) has gone from an enigmatic molecule with presumed structure and functions within the nucleus to one of the hallmarks of modern biology. There are now many studies regarding either DNA or RNA nowadays, for instance, DNA insertion and RNA modification. The aim of this project is to master the skill to expand plasmids in bacterial cells. Besides, the second aim is to master the skill to synthesize DNA templates and obtain corresponding message RNAs (mRNAs) from in vitro transcription (IVT) with high purity. Improving Data Analysis Methods for Shotgun Proteomics Supervisor: LAM Henry Hei Ning / CBE Student: CHAN Tsz Fung / BIEN Course: UROP1000, Summer Spectral library search is one of the effective and efficient approaches for peptide identification. With the aid of the developed machine learning models, massive tandem mass spectra can be predicted and construct a spectral library for matching with the sample spectrum to identify the peptide sequence by their similarities. The purpose of this study is to validate the spectra predicting models and analyze the implication of machine learning on spectral library search for improving the effectiveness of peptide identification. This study will apply various spectra-predicting machine learning models in building spectra libraries and evaluate the performance of the spectral library built with the predicted spectra with the real experimental data.