Dr. Xiaobo Zhou
The University of Texas Health Science Center at Houston
School of Biomedical Informatics
Our lab focuses on bioinformatics, systems biology, and cancer biology, and consists of dry and wet labs. The dry lab focuses on developing advanced computational bioinformatics, biomedical imaging informatics, and systematic modeling tools to answer various biological and medical questions such as biomarker and drug-target discovery, disease and drug resistance mechanisms, and personalized medicine delivery studies to improve basic, clinical, and translation research. Our lab is extensively working on next generation sequencing data analysis tools, and particularly working on functional prediction of lncRNAs. We are also working on 3D in-vivo imaging informatics tool development, and medical image informatics, with primary focus in dental surgical planning and Craniomaxillofacial (CMF) deformation prediction based biomechanical properties and clinical information. We also develop software to help scientists and policy makers understand the transmission and spread of influenza and make effective evaluations for influenza control and prevention strategies. We also apply a novel systems biology approach to mechanistically bridge auto- and anti-cancer immunity in the context of pan-cancer incidence using novel computational strategies and systems modeling and unique animal models that connect biological and population scales. The wet lab focuses on understanding bone regeneration, immune cell-cell interaction system, data generation and validation. We apply multi-scale modeling strategies to simulate and guide bone regeneration and to develop dual delivery nanocarriers as injectable synthetic bone grafts in order to reduce the risk of infection and improve bone regeneration for the treatment of contaminated injuries. We also utilize a unique multipronged systems biology approach, which combines the dynamic data from in vivo studies with a predictive multi-scale, multi-compartment, ordinary differential equations (ODEs) model to predict the optimal intervention to prevent immune inhibitory pathways, thereby paving the way to improved immunotherapy.