Ye Wang

701 W. 168th St.

New York, NY 10032

I am a postdoctoral research scientist in the laboratory of Prof. Chaolin Zhang at Columbia University Irving Medical Center. I am interested in how nature writes, reads, and edits information in DNA and RNA, and how we can learn from these systems to build programmable therapeutic technologies.

My fascination with nucleic acid design started during my PhD training at Tsinghua University, where I worked on promoter de novo design and became increasingly drawn to the logic of RNA design. To me, DNA and RNA sequences are not just strings of bases; they are compact regulatory programs shaped by evolution, carrying instructions that can be decoded, redesigned, and eventually repurposed.

Today, my research combines RNA biology, regulatory genomics, and AI-driven biological engineering through iterative, in-the-loop optimization. I develop models and design strategies to understand how sequence and molecular context shape gene regulation, and to engineer functional DNA/RNA elements from first principles.

My long-term goal is to develop scalable RNA-based therapeutic platforms that can correct disease-causing mutations and enable precision medicine for genetic and neurodegenerative disorders. I am especially inspired by a learn-from-nature philosophy: discovering the elegant regulatory solutions already present in biology, and translating them into engineered systems for future medicine.

news

Aug 19, 2025	Presented DeltaSplice — a reference-informed computational framework that systematically predict splice-site usage in human tissues — as a poster at the Eukaryotic mRNA Processing 2025 meeting at Cold Spring Harbor Laboratory.
Feb 19, 2025	Our paper introducing DeepCROSS — a generative AI framework for the inverse design of cross-species bacterial regulatory elements, was selected as a Featured Article in Nature Communications.

selected publications

Synthetic promoter design in Escherichia coli based on a deep generative network

Ye Wang^*, Haochen Wang^*, Lei Wei, Sichao Li, Lingxue Liu, and Xiaowo Wang^†

Nucleic Acids Research, 48(12):6403–6412, Jan 2020

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Cover Paper & Breakthrough Article, Nucleic Acids Research

My research during PhD training focused on developing AI-based generative algorithms and optimization methods for de novo promoter sequence design. Understanding the complex encoding syntax in the genome and using it to build synthetic circuits will accelerate synthetic biology parts discovery and hopefully bring new drug candidates to the clinic. This approach achieved high success rates in in vivo experimental validations, offering valuable insights into synthetic element design and highlighting the potential of deep learning to explore the vast sequence space of synthetic biology.
Systematic representation and optimization enable the inverse design of cross-species regulatory sequences in bacteria

Pengcheng Zhang^*, Qixiu Du^*, Ye Wang^*, Lei Wei, and Xiaowo Wang^†

Nature Communications, 16(1):1763, Feb 2025

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Featured Article — Editors’ Highlights in Biotechnology and Methods, Nature Communications

Long-tail distribution is a common problem in genetic-elements inverse design and engineering tasks based on generative AI. The combination of massive quantification-screen experiments and exploration procedures guided by generative AI efficiently fills in the sequence-activity landscape and significantly improves the performance of predictive and generative models for inverse design. The strategy of generative AI-guided high-throughput exploration shows potential to overcome data scarcity, a common problem in inverse design and engineering tasks. DeepCROSS offers the potential to go beyond designing bacterial regulatory sequences and be used for mammalian regulatory sequence design, taking a step toward constructing complex circuits using generative AI for the synthetic biology community.
Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences

Chencheng Xu^*, Suying Bao^*, Ye Wang, Wenxing Li, Hao Chen, Yufeng Shen, Tao Jiang^†, and Chaolin Zhang^†

Genome Research, 34(7):1052–1065, Mar 2024

Abs DOI

We introduced a reference-informed computational framework that systematically links sequence context to splice-site usage across human tissues, enabling quantitative prediction of splicing-altering mutations. This platform supports functional interpretation of genetic variants in the context of human disease, providing mechanistic insight into how noncoding and coding variants disrupt RNA processing. By transforming large-scale transcriptomic data into predictive models of splicing regulation, our work facilitates clinically meaningful variant prioritization, improves the interpretation of patient genomic data, and informs the rational design of RNA-targeted therapeutic strategies. This framework bridges fundamental RNA biology with translational medicine, creating a scalable foundation for precision diagnostics and RNA-based therapeutic development.
Deep learning unlocks sequence-divergent synthetic promoters to empower Streptomyces natural product engineering

Qun Zhou^*, Ye Wang^*, Jixin Dong^*, Xiaomin Zhao, Alan J.X. Guo, Xiaowo Wang, and Yunzi Luo

Metabolic Engineering, 96:420–433, Apr 2026

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