Education and Training:

09/2015-08/2019: Independent postdoc associate researcher, Princeton University, USA

09/2009-12/2014: Graduate Program of Biophysics, University of California San Francisco, San Francisco, USA

09/2005-07/2009: School of Physics, Peking University, Beijing, China

Research projects:

1. Quantitative microbial ecology

Microbes influence all elements of human well-being, from land to sea, from industrial production to human microbiomes. Although sequencing techniques have blossomed in the last decade, the field of "quantitative microbial ecology" is still in its infancy, with many problems rmain unanswered: how do microorganisms shape and adapt to their niches? To what eextend does microbial ecology differ from classical ecology? And in the cooperator-cheater games, how is community collaboration maintained? Using data mining and dynamical modeling as tools, our lab focuses on the "microenvironment-microbe" feedback, attempting to construct a comprehensive quantitative picture from evolution to metabolic strategy to community ecology in several model systems.

Microorganisms actively shape their nches with secondary metabolites. on-ribosomal peptide synthetase (NPRS) is a class of modular synthetase that contribute to more than half of the known secondary metabolite gene clusters . Diverse NRPS products have provided inspirations for the development of human medications since penicillin, and its assembly-line structure has also encouraged researchers to reengineer novel NRPSs.

Our group has created comprehensive techniques for parsing and analyzing NRPS sequences using conserved motifs. Using this standardized architecture, we are investigating evolutionary patterns and sequence-function connections in a variety of microbial systems, spanning from sea microbes to human pathogenic bacteria.

2. Collective cell fate decisions and pattern formation

In a multicellular development system, such as embryonic development, organogenesis, and microbial community formation, cells with identical genomes spontaneously establish highly organized patterns.  Since the mid-twentieth century, systems biology has been focusing on pattern creation. Despite the rapid development of multi-omics technology in recent years, there are still many questions to be answered: what might be the "source of information" that offers complexity to developmental systems? What type of "fate encoding" can stably and consistently establish spatial patterns from identical gene-regulatory and cell-signaling networks?

Our lab is attempting to explore the fundamental laws in spontaneous pattern formation through statistical and mechanistic modeling, in the following two areas:

2.1. Mapping relationship between gene regulatory networks and multiple fates of a single cell. Cell destiny is controlled by a network of interacting transcription factors, which can be thought of as nonlinear attractors. Differentiation and development are frequently coupled with dozens or hundreds of distinct cell fates. One of our research groups' interests is in understanding and predicting the choice of cells in more than three potential fates at the transcriptional regulatory network level.

2.2. Investigating pattern formation in the multicellular development system. understanding and design The complexity of multicellular systems typically grow spontaneously throughout embryonic development and organogenesis, with little external input. The research group is investigating how the complexity quantified with "positional information"  can be generated through intercellular communications and intracellular gene regulation networks,  using network analysis and mathematical modeling methods.

3. Exploratory data analysis towards big data in biology

With the rapid development of various biotechnologies, the life sciences are generating a vast amount of data. Gaining insight into the underlying mechanism from the data is a core concern of ours research. From the examples of multiple collaborators, we attempt to explore some common rules in data mining methedologoies.

4. Dynamical models of epidemics and non-pharmaceutical interventions.

Infectious diseases pose a significant danger to public health. Epidemic models based on transmission and evolutionary dynamics can, to a certain extent, offer predictions for appropriate preventative and control measurements. In the early days of the COVID-19 outbreak, we began collaborating with a number of researchers on epidemic models of COVID-19, with a focus on examining the role of non-pharmaceutical interventions such as public mask wearcing.

Selected publications:

• Jeremy Howard * , Austin Huang , Zhiyuan Li , Zeynep Tufekci , Vladimir Zdimal , Helene-Mari van der Westhuizen ORCID logo , Arne von Delft , Amy Price , Lex Fridman , Lei-Han Tang , Viola Tang , Gregory L. Watson , Christina E. Bax , Reshama Shaikh , Frederik Questier , Danny Hernandez , Larry F. Chu , Christina M. Ramirez , Anne W. Rimoin. (2020) Face masks against COVID-19: an evidence review. PNAS, 118 (4) e2014564118;

• Zhiyuan Li, Bo Liu, Sophia Hsin-Jung Li, Christopher G. King,Zemer Gitai, Ned S. Wingreen. (2020) Modeling microbial metabolic trade-offs in a chemostat; PLOS Computational Biology; 16(8), e1008156

• Jindong Zan*, Zhiyuan Li*, Maria Diarey Tianero-McIntosh, Jeanette Davis, Russell T. Hill, and Mohamed S. Donia, “A microbial factory of defense chemicals in a tripartite marine symbiosis.” (2019) Science, 364, pp 1056,DOI: 10.1126/science.aaw6732

• Li, Sophia Hsin-Jung, Zhiyuan Li, Junyoung O. Park, Christopher G. King, Joshua D. Rabinowitz, Ned S. Wingreen, and Zemer Gitai. “Escherichia coli translation strategies differ across carbon, nitrogen and phosphorus limitation conditions.” Nature microbiology 3, no. 8 (2018): 939. DOI:10.1038/s41564-018-0199-2

• Jian Shu*, Chen Wu*, Yetao Wu*, Zhiyuan Li*, Sida Shao, Wenhui Zhao, Xing Tang, Huan Yang, Lijun Shen, Xiaohan Zuo, Weifeng Yang, Yan Shi, Xiaochun Chi, Hongquan Zhang, Ge Gao, Youmin Shu, Kehu Yuan, Weiwu He, Chao Tang, Yang Zhao, Hongkui Deng. Induction of Pluripotency in Mouse Somatic Cells with Lineage Specifiers.(2013) Cell, Volume 153, Issue 5, pp 963–975. DOI: 10.1016/j.cell.2013.05.001