We are enthusiastic to formulate a variety of biomedical questions into mathematical problems by developing general mathematical theories, powerful mathematical models and efficient computational methods. We also work closely with multiple biological laboratories to conduct cutting edge research at the frontiers of the life sciences, in particular regarding skin biology, cancer biology and neuroscience, by combining systems biology and machine learning approaches with the cutting-edge single-cell technologies. Currently, we are interested in but not limited to the following aspects.
We have developed a set of new mathematical models and computational methods for analyzing single-cell genemoic data, such as CellChat for inferring and analyzing cell-cell communication and scAI for integrating parallel single-cell transcriptomics and epigenomics. Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of single-cell multiomics and spatial transcriptomics to investigate cellular heterogeneity and organization. We are enthusiastic to further develop efficient machine learning and deep learning models and algorithms to explore the large-scale data generated by these cutting-edge technologies.
Given that we can generally only measure each cell once through single cell-based sequencing, we need computational models to deduce cellular trajectories leading to different cell fate decisions from static snapshot data. We have developed scEpath, an energy landscape-based approach for measuring developmental states and inferring cellular trajectories from single cell RNA-seq data. However, the combination of the dynamical system theories and mathematical modeling with the single-cell data to investagate cellular and gene dynamics still remains challenging. The recent RNA velocity technique and accumulation of single-cell multiomics data bring new opputunity to modeling cellular state and dynamics in single-cell data.
Single-cell sequencing is the promising step in innovation towards making precision medicine more accurate than ever before. We aim to improve mechanistic molecular understanding, prediction, and treatment of disease such as cancer onset and progression by developing and applying mechanistic modeling, nonlinear dynamics analysis, control theories, artificial intelligence and network biology approaches, and integrating these approaches with single-cell multiomics and imaging data.