赵诗杰博士
- 基本信息
- 教育经历
- 工作经历
- 研究概述
- 发表文章

赵诗杰 博士Shijie Zhao, Ph.D.Assistant Investigator, NIBS, Beijing
Email: zhaoshijie@nibs.ac.cn
教育经历 Education
2019 美国麻省理工学院,计算与系统生物学博士
Ph.D. in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA2013 北京大学,生命科学学士
B.S. in Biological Sciences, Peking University, Beijing, China
工作经历 Professional Experience
2026- 研究员,北京生命科学研究所
2026- Assistant Investigator, NIBS, Beijing2023-2024 博士后,美国博德研究所
2023-2024 Postdoctoral Research Associate, Broad Institute of MIT and Harvard, Cambridge, MA, USA
2021-2022 博士后,美国哥伦比亚大学
2021-2022 Postdoctoral Research Associate, Columbia University, New York, NY, USA
2019-2021 博士后,美国麻省理工学院
2019-2021 Postdoctoral Research Associate, Massachusetts Institute of Technology, Cambridge, MA, USA
研究概述 Research Description
Microbes carry out much of the chemistry that sustains life. They shape ecosystems, influence human health, and enable modern biotechnology. Advances in DNA sequencing now allow microbial genomes to be read at massive scale. Yet for most microbes, we still cannot reliably predict their functions, explain their behavior in communities, or engineer them. The central challenge is no longer data collection, but converting genomic information into mechanistic and predictive understanding.
Our research
program addresses this challenge by building systematic experimental platforms
to measure microbial behavior and applying quantitative modeling to extract
general biological rules from these data. Because the space of genetic
variation and environmental conditions is far too large to explore
exhaustively, models guide experiment selection and hypothesis generation. The
lab operates in an iterative cycle of experimentation, modeling, and
validation, with the biological goal of understanding how microbial genetic
variation shapes colonization, metabolism, and host interaction.
My past work
established the key tools for this approach, including mapping microbial
evolution within hosts, developing single-cell and spatial genomics to resolve
strain organization in complex communities, and constructing large living
strain collections for controlled perturbation. Together, these foundations
support a research program that moves microbiome science from descriptive
surveys toward causal and predictive biology. We pursue three integrated
research directions:
1. Predictive maps
of microbial behaviors
We will quantify
how tens of thousands of microbial strains grow, interact, and adapt across
diverse environmental and host-relevant conditions. These experiments will
generate quantitative maps linking genetic variation to regulatory control,
metabolic state, and ecological fitness. The outcome will be predictive
frameworks for how strains and communities respond to perturbations such as
dietary change, drug exposure, or host physiological shifts.
2. Discovery of
molecular functions from nature’s dark matter
Microbial genomes
contain vast numbers of genes whose functions cannot be reliably inferred from
sequence alone, leaving much of microbial biochemistry unexplored. Testing this
space with synthetic DNA libraries remains costly and limited in scale. We exploit
natural strain diversity and large living strain collections as a
cost-effective and information-rich alternative. By pairing these resources
with functional screening, we aim to uncover new molecular activities in
small-molecule biosynthesis, plasmid maintenance, phage resistance, and
host-associated functions. These discoveries will define sequence–function
relationships and provide mechanistic entry points for biochemical and genetic
investigation.
3. Engineering
previously intractable microbes
发表文章 Publications
1. Shijie Zhao, Tami Lieberman, M. Poyet, K. Kauffman, S. Gibbons, M. Groussin, Ramnik Xavier, Eric Alm. Adaptive evolution within the gut microbiome of healthy people. Cell Host & Microbe. 2019.
2. Wenshan Zheng, Shijie Zhao, Yehang Yin, Huidan Zhang, …, Peter J. Lu, Eric J. Alm, David A. Weitz. High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome, Science. 2022
3. Shijie Zhao, C. Dai, Z. Lu, E. Evans, Eric Alm. Tracking strains predicts personal microbiomes and reveals adaptive evolution within adult twins. BioRxiv. 2020
4. Miles Richardson, Shijie Zhao, Ravi Sheth, Thomas Moody, …, Harris H. Wang. SAMPL-seq reveals micron-scale spatial hubs in the human gut microbiome. Nature Microbiology, 2025.
5. Yiming Huang, Ravi U. Sheth, Shijie Zhao, …, Harris H. Wang. High-throughput microbial culturomics using automation and machine learning. Nature Biotechnology, 2022.
6. I. Brito, T. Gurry, Shijie Zhao, K. Huang, S. Young, T. Shea, W. Naisilisili, A. Jenkins, S. Jupiter, D. Gevers, Eric Alm. Transmission of human-associated microbiota along family and social networks. Nature Microbiology, 2019.
7. J. Schnitzbauer, Y. Wang, Shijie Zhao, M. Bakalar, T. Nuwal, B. Chen, Bo Huang. Correlation analysis framework for localization-based super-resolution microscopy. PNAS, 2018
8. F. Wu, A. Xiao, …, Shijie Zhao, J. Thompson, Eric J Alm, SARS-CoV-2 titers in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases, Science of The Total Environment, 2022
9. An-Ni Zhang, Jeffry M Gaston, Chengzhen L Dai, Shijie Zhao, …, Eric J Alm, Tong Zhang. An omics-based framework for assessing the health risk of antimicrobial resistance genes. Nature Communications, 2021
10. Xiaoqiong Gu, Jean XY Sim, …, Shijie Zhao, Zhanyi Lee, Janelle R Thompson, Eng Eong Ooi, Jenny G Low, Eric J Alm, Shirin Kalimuddin. Gut Ruminococcaceae Levels at Baseline Correlate with Risk of Antibiotic-associated Diarrhea. iScience, 2021
11. Chenhao Li, Martin Stražar, Ahmed Mohamed, Julian Pacheco, Rebecca Walker, Shijie Zhao, …, Damian R Plichta, Ramnik J Xavier. Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria. Cell, 2024
12. An-Ni Zhang, Jeffry Gaston, Pablo Cárdenas, Shijie Zhao, Xiaoqiong Gu, Eric J Alm. CRISPR spacer acquisition is a rare event in human gut microbiome. Cell Genomics, 2025.
13. M. Poyet, M. Groussin, S. Gibbons, …, Shijie Zhao, et al. A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research. Nature Medicine, 2019
14. Y. Shen, N.C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, Shijie Zhao, H. Larochelle, D. Englund, M. Soljačić. Deep Learning with coherent nanophotonic circuits. Nature Photonics, 2017



