haotian guo
PHD Student
Haotian's Bio

PHD Student

Short Bio

Haotian comes from Tianjin Municipality, China, which is next to Beijing and has the largest port in North China. After graduating from Fudan University at Shanghai in 2015, he came to Paris for the further study in AIV Master program at CRI. He did his master internships in Lindner Team in INSERM, Minc group in Institut Jacques Monod and Gore lab in MIT Physics. In 2016, he was enrolled in FdV program at CRI and started his PhD project supervised by Ariel B. Lindner, in Evolutionary and Systems Biology team of the INSERM U1001 (now U1284), and financed by the EU Marie Skłodowska-Curie fellowship via INSPIRE project. Also, Haotian is an iGEM Alumni, who re-founded the Fudan iGEM team in 2013 (gold medal and world championship in overgrad), mentored Paris Bettencourt iGEM team in 2017 (gold medal in overgrad), 2018 (gold medal, 2 nominations, in overgrad), and participated as a judge.


Research Interests

Haotian wants to understand the generic bases of design principles underlying the diverse, complex phenomena in living systems, and how those diversity and complexity emerges. Especially, his research harnesses the power of synthetic biology to reveal the true in the made.

His research mainly focuses on the causal rules among sequence, structure, and function of RNAs, especially riboswitch, RNA structural dynamics, RNA-RNA and RNA-protein interactions. Current projects includes studies on RNA regulation of CRISPR RNA processing, and liquid-liquid phase separation of RNA molecules.

Besides, Haotian was also trained as a biochemist during college, and worked in the field of cytoskeleton and motor proteins, DNA origami, CRISPR, yeast biomechanics, and ecological dynamics of bacterial community before.


Selected Publications

Multi-state theory and riboswitch design principles, Talk in Cold Spring Harbor Asia meeting of Synthetic biology in 2014, https://www.youtube.com/watch?v=t3M1iz5cKOY

Seq2DFunc: 2-dimensional convolutional neural network on graph representation of synthetic sequences from massive-throughput assay, preprint https://www.biorxiv.org/content/10.1101/2019.12.22.886085v1

In vivo real-time RNA probing by AI-aided design of CRISPR RNA switch
RNA-based switch designs for all
Phase separated RNA-based synthetic organelle in E. coli
Build synthetic organelle in E. coli for various applications, and studies on membraneless organelle