It emerges as an exciting new field both in quantitative biology and biological physics on studying how eukaryotic cells make cell fate decisions and convert between different cell types by integrating big data analyses and mechanistic studies1. My lab has developed tools and theoretical framework from chemical physics, single cell imaging, computational image analysis, and high throughput data analyses. We combine single cell high throughput (e.g., sequencing and imaging) data analyses and mechanistic modeling2-6. Example problems include how cell cycle couples to various differentiation processes, and associated gene regulations. I especially look for applicants with background in dynamics inference from data, single cell genomics data analyses, and/or mechanistic studies of cellular processes.
More information about the Xing lab,
www.csb.pitt.edu/Faculty/xing/
github.com/xing-lab-pitt
www.addgene.org/Jianhua_Xing/
1. Xing, J. Reconstructing data-driven governing equations for cell phenotypic transitions: integration of data science and systems biology. Physical Biology 19, 061001 (2022).
2. Qiu, X. et al. Mapping Transcriptomic Vector Fields of Single Cells. Cell 185, 690-711 (2022).
3. Wang, W., Ni, K., Poe, D. & Xing, J. Transiently Increased Coordination in Gene Regulation During Cell Phenotypic Transitions. PRX Life 2, 043009 (2024).
4. Wang, W. et al. Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data. Science Advances 6, eaba9319 (2020).
5. Wang, W., Poe, D., Yang, Y., Hyatt, T. & Xing, J. Epithelial-to-mesenchymal transition proceeds through directional destabilization of multidimensional attractor. eLife 11, e74866 (2022).
6. Chen, Y. et al. GraphVelo allows inference of multi-modal single cell velocities and molecular mechanisms. bioRxiv, 2024.12.03.626638 (2024).
I look for applicants with the following general qualifications:
1) Will receive or just received PhD;
2) Strongly motivated, and eager to learn new things;
3) Have demonstrated high productivity and independence;
4) Collaborate well with others and show strong communication skills;
Applicants with strong computer programming and single cell data analysis (including machine learning) background will receive favorable consideration.
For application, please send c.v. and a brief research plan indicating research interest on integrating quantitative biological physics and big data analyses to Dr Jianhua Xing at xing1@pitt.edu. Three reference letters will be requested later.