Biography

Dr. Shou-Wen Wang received his bachelor’s degree in Engineering Physics from Tsinghua University in 2013. He went on to obtain a PhD in Physics from Tsinghua University in 2018. There, he investigated a range of biological systems from the perspective of non-equilibrium statistical physics, which leads to new insights regarding the relationshipbetween energy cost and biological function. During 2018-2022, Dr. Wang worked as a postdoctoral researcher in the Department of Systems Biology at Harvard Medical School, where he developed methods for analyzing single-cell lineage tracing and multi-omic data to better understand the fundamental principles of cell differentiation and embryonic development. He was awarded the Quantitative Biology Award from the Damon Runyon Cancer Research Foundation during his postdoc. In 2023, he joined Westlake University as an assistant professor in the School of Life Sciences, with a joint appointment in the physics department at the School of Science.

Research

The rapid progress of single-cell genomics over the past decade has established an important technology foundation for systematically understanding diverse biological processes such as development and tissue homeostasis. These technologies include high-throughput single-cell RNA sequencing, cellular lineage tracing with DNA barcoding, and single-cell multi-omics. As these technologies are being integrated to provide richer and more detailed information at the single-cell level, it remains a challenge to effectively extract quantitative biological insights from such large datasets.

Dr. Shou-Wen Wang has developed a range of computational methods, including 1) combining transcriptomic and lineage information at the single-cell level to effectively predict the early fate bias within a heterogeneous stem cell population; 2) developing methods for analyzing single-cell multi-omics data, integrating them with lineage information to reveal the molecular basis of cell fate choice and cell identities.

The laboratory is dedicated to developing quantitative analysis and modeling methods for single-cell lineagetracing data, and integrating transcriptomic and epigenomic measurements to further reveal the molecular mechanisms underlying biological phenomena at different scales, such as cell differentiation and tissue homeostasis. The laboratory will closely collaborate with other groups, integrate cutting-edge experimental designs and data collection for lineage tracing, and draw inspiration from fields such as statistical physics, computer science, and applied mathematics for data analysis. We will pursue a research path that crosses academic disciplines and combines experimental and computational approaches.

Representative Publications

* These are corresponding authors.

1, L. Li, S. Bowling, Q. Yu, Karel Alcedo,Wei-Chien Yuan, Mark Ferreira, A. M. Klein, S.-W. Wang*, F. D. Camargo*, DARLIN mouse enables efficient joint profiling of lineage barcodes, transcriptome, and epigenome in single cells (2022, in preparation).

2, S.-W. Wang*, M. J. Herriges, K. Hurley, D. N. Kotton, A. M. Klein*, CoSpar identifies early cell fate biases from single cell transcriptomic and lineage information,Nat. Biotechnol. (2022).

3, A. E. Rodriguez-Fraticelli, C. S. Weinreb,S.-W. Wang, R. P. Migueles, M. Jankovic, M. Usart, A. M. Klein, S. Lowell, F. D. Camargo*, Combined single cell lineage and transcriptome sequencing reveals Tcf15 as a master regulator of hematopoietic stem cell fate,Nature 583, 585 (2020).

4, S.-W. Wang*, and L.-H. Tang*, Emergence of collective oscillations in adaptive cells,Nat. Commun. 10, 5613 (2019).

5, S.-W. Wang, A.-F. Bitbol*, N. S. Wingreen*, Revealing evolutionary constraints on proteins through sequence analysis,PLOS Comput. Biol.15, e1007010 (2019)

6, S.-W. Wang, K. Kawaguchi, S.-i. Sasa and L.-H. Tang, Entropy production of nanosystems with time scale separation,Phys. Rev. Lett. 117, 070601 (2016).

Contact 

Email:wangshouwen@westlake.edu.cn

Website:https://www.shouwenwang-lab.com/

We are a multi-disciplinary, computational biology lab. We collaborateclosely with other groups to use state-of-the-art single-cell omics technologies to studymulti-scale questions in cell differentiation and development. We welcome individuals with backgrounds in computational biology, experimental biology, physics, computer science and other disciplines to join us. Successful candidates will have the opportunity to tackle fundamental biological questions in cell differentiation and development, receive cross-disciplinary training, and experience the joy of creating at the cutting edge. We are always looking for passionate, talented and dedicated new team members! The lab currently has several positions available for postdocs, PhD students, research assistants, etc. Please see our lab website for more details.