Wang Lab
Digital Intelligence for Tissue Regeneration, ZOC and GIBH
Research Interests
Evolutionary selection has shaped striking diversity in regenerative capacity across species and tissues. The Wang Lab focuses on elucidating the principles underlying these differences in regenerative capacity, particularly in the retina and liver. While zebrafish and other non-mammalian species can regenerate the retina, this capacity is largely absent in mammals. In contrast, mammals have retained a remarkable ability for liver regeneration, yet chronic injury can impair this regenerative program and lead to fibrosis. How have these differences in the regenerative capacity evolved? Can regenerative programs from regenerative species be harnessed to restore retinal regeneration in humans? Can we enhance regeneration in the liver to prevent or reverse fibrosis?
To address these questions, we integrate high-throughput sequencing and organoid technologies with computational and statistical approaches, complemented by cellular and animal experiments. Through interdisciplinary approaches, the Wang Lab is dedicated to uncovering the fundamental principles of tissue regeneration and developing regenerative strategies for diseases such as tissue fibrosis and degeneration.
Biological Interests
Tissue Regeneration
Tissue regeneration holds great potential for interfering with tissue fibrosis, degenerative diseases, etc. In zebrafish, Müller glial cells play essential roles in retinal regeneration. Although mammalian retinas contain Müller glia, mammals can not automatically regenerate retinas after injury. It is still unclear which regulatory mechanisms in Müller glia leads to cross-species differences on retinal regeneration.
Tissue Fibrosis
Tissue fibrosis is from an excessive accumulation of extracellular matrix components and can affect any organ, including the liver.
Computational Interests
Omics and Regulatory Networks
Regulatory networks are used to uncover gene regulatory relationships in diverse biological processes, e.g. tissue regeneration.
Omics and Artificial Intelligence
Artificial intelligence especially deep learning is powerful to predict biomedical outcomes through learning high-throughput sequencing data and large-scale images.