Our lab works on computational proteomics of protein turnover and protein networks. We develop and implement bioinformatics and statistical approaches for biological inferences from large-scale, high-throughput proteomics data and their applications in biomedical problems. We developed algorithms for peak detection and quantification, identification of structures in multivariate data, stochastic time-course modeling to extract dynamical features, construction of protein networks, and error control in the resulting inferences. In collaboration with our experimentalist colleagues, we applied these techniques to study molecular mechanisms in non-alcoholic fatty liver disease, aging, and neurodegenerative diseases.
About Us
Our lab works on computational proteomics of protein turnover and protein networks. We develop and implement bioinformatics and statistical approaches for biological inferences from large-scale, high-throughput proteomics data and their applications in biomedical problems. We developed algorithms for peak detection and quantification, identification of structures in multivariate data, stochastic time-course modeling to extract dynamical features, construction of protein networks, and error control in the resulting inferences. In collaboration with our experimentalist colleagues, we applied these techniques to study molecular mechanisms in non-alcoholic fatty liver disease, aging, and neurodegenerative diseases.
Open positions
We have open positions (postdoctoral researcher) available for highly motivated individuals. The projects are broadly in bioinformatics of mass spectrometry-based proteomics. If interested, please contact the PI using the above email.