← Back to Event List

Graduate Students Seminar

Online on Blackboard Collaborate



Date & Time

April 20, 2022, 11:00 am12:00 pm


Session Chair:Bo Liu
Discussant:Dr. Malinovsky

Speaker 1: Luan (Chip) Nguyen
Stochastic Modeling of Glucosome Formation
Biomolecular condensates are important cellular compartments that involve diverse biological processes. In glucose metabolism, human liver-type phosphofructokinase 1 (PFKL) has been shown to organize different enzymes into a multienzyme metabolic condensate - the glucosome, which presumably regulates the direction of glucose flux in living human cells. However, it is still challenging to identify the factors that control PFKL phase separation and glucosome formation. In this work, a stochastic model is developed by the principle of Langevin dynamics to study how properties of PFKL as a scaffolder contribute to the condensate formation. Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) is used and the molecular dynamics simulation shows various features that can be essential to explain the PFKL role in the initiatory step of glucosome condensate formation.
Speaker 2: Sidd Roy
Dynamic Risk Prediction for Cervical Precancer Screening with Continuous and Binary Longitudinal Biomarkers
We are interested in developing dynamic risk models using flexible joint models that use multiple longitudinal biomarkers to improve cervical precancer risk estimation for HPV positive women. In this project, we develop a joint model to link a novel continuous methylation biomarker and a binary cytology biomarker to the time to precancer outcome using shared random effects. The model uses a discretization of the time scale to allow for closed-form likelihood expressions, thereby avoiding potential high dimensional integration of the random effects. The method handles an interval censored time-to-event outcome due to intermittent clinical visits, incorporates sampling weights to deal with stratified sampling data, and can provide immediate and 5-year risk estimates that may inform clinical decision making.