SLAI Seminar 28th Session will be discussing the topic on "Chromatin Folding: Physical Modeling and AI Prediction",  from 10am to 11:30am, March 10th (Tuesday) at Room B401, online participation is welcome (Tencent Meeting ID: 734-891-927)

报告主题：染色质折叠的物理建模与AI预测

时间：2026年3月10日（周二）上午10点 - 11点30

地点: 深圳河套学院B401教室

线上参与：腾讯会议号734-891-927

主讲嘉宾：黄恺研究员

主持人：姬艳丽教授

讲者简介 About the Speaker:

黄恺2009年本科毕业于清华大学工程物理系，2015年获威斯康星大学麦迪逊分校材料学博士学位，之后在美国西北大学生物医学工程系从事博士后研究。于2020年加入深圳湾实验室任特聘研究员，致力于四维基因组、生物相分离、AI4Biology等交叉领域的研究及相关理论计算方法开发。曾获美国生物物理年度博士后奖，广东省珠江人才计划青年拔尖人才。研究成果以第一及通讯作者（含共同）发表在Nature, Cell Research, Science Advances, Nature Commutations, JACS等知名学术期刊上。在染色质高级结构方面的工作人类基因组计划前负责人Francis Collins评价为“人类三维基因组的新图像”。相关研究得到国自然创新研究群体（B类）及面上等项目支持。

Dr. Huang Kai graduated with a bachelor's degree from the Department of Engineering Physics at Tsinghua University in 2009 and obtained his Ph.D. in Materials Science from the University of Wisconsin-Madison in 2015. He subsequently conducted postdoctoral research in the Department of Biomedical Engineering at Northwestern University. In 2020, he joined Shenzhen Bay Laboratory as a junior principal investigator, dedicating his work to interdisciplinary fields such as four-dimensional genomics, biological phase separation, and AI4Biology, as well as the development of related theoretical and computational methods. Dr. Huang has received the Annual Postdoctoral Award for Biophysics from the Biophysical Society in the United States and Pearl River Talents Program of Guangdong Province Young Top Talent Project. His research, as first and corresponding author (including co-authorship), has been published in renowned academic journals such as Nature, Cell Research, Science Advances, Nature Communications, and JACS. His work on chromatin higher-order structures was described by Francis Collins, former director of the Human Genome Project, as "a new image of the human three-dimensional genome." His research has also received support from projects such as the National Natural Science Foundation of China's Innovative Research Group (Category B) and general programs.

报告摘要：

染色质作为遗传信息的物理载体，其动态三维结构调控着基因表达、DNA复制等关键生命过程。为统一解释染色质的长程互作、密度不均与自相似等高级结构特征，我们提出了“染色质三维森林模型”，预测染色质在单细胞水平广泛存在多位点高阶相互作用。近期Pore-C实验验证了染色质多体互作的普遍性，并揭示部分功能性调控元件具有显著的互作协同性。为识别参与多体互作的关键基因位点，我们整合Hi-C和表观遗传数据，构建并训练了图神经网络模型，实现了染色质多体互作的AI预测。进一步分析表明，远程顺式元件通过多体互作参与基因表达调控。我们采用CRISPRi技术对AI预测的增强子协同作用进行了实验证实，支持模型的生物学有效性。本研究展示了人工智能在基因组结构与功能预测中的应用潜力，并为四维基因组学研究提供了有力的理论框架与计算工具。

Abstract: Chromatin, as the physical carrier of genetic information, its dynamic three-dimensional structure regulates key life processes such as gene expression and DNA replication. To provide a unified explanation for the long-range interactions, density heterogeneity, and self-similarity observed in chromatin higher-order structures, we proposed the "3D Forest Model of Chromatin," predicting the widespread existence of multi-site higher-order interactions at the single-cell level. Recent Pore-C experiments have validated the universality of chromatin multivalent interactions and revealed that certain functional regulatory elements exhibit significant interaction synergy. To identify key genomic loci involved in multivalent interactions, we integrated Hi-C and epigenetic data to construct and train a graph neural network model, enabling the AI-based prediction of chromatin multivalent interactions. Further analysis indicated that distal cis-regulatory elements participate in gene expression regulation through multivalent interactions. We experimentally validated the AI-predicted synergistic effects of enhancers using CRISPRi technology, supporting the biological validity of the model. This study demonstrates the potential of artificial intelligence in predicting genome structure and function, while providing a robust theoretical framework and computational tool for research in four-dimensional genomics.