12月6日 学术报告（三个）

发布时间:2019-12-02

学术报告1

报告题目：Evolutionary dynamics of cancer: from epigenetic regulation to cell population dynamics

主讲人：雷锦誌，清华大学周培源应用数学研究中心副教授 

报告时间：2019年12月6日15:00 - 15:40

报告地点：南校区新数学楼416室

主持：张家军副教授

摘要：Cancer is a group of diseases involving abnormal cell growth with the potential to invade or spread to other parts of the body. Cancer development is a long-term process which remains mostly unknown; predictive modeling of the evolutionary dynamics of cancer is one of the major challenges in computational cancer biology. In this talk, I introduce a general mathematical framework for understanding the behavior of heterogeneous stem cell regeneration, and the application of the model framework to study the evolutionary dynamics of cancer. The proposed model framework generalizes the classical G0 cell cycle model, incorporates the epigenetic states of stem cells that are represented by a continuous multidimensional variable, and the kinetic rates of cell behaviors, including proliferation, differentiation, and apoptosis, which are dependent on their epigenetic states. The random transition of epigenetic states is represented by an inheritance probability that can be described as a conditional beta distribution. Moreover, the model framework can be extended to investigate gene mutation-induced tumor development. The model equation further suggests a numerical scheme of multi-scale modeling for tissue growth where a multiple cell system is represented by a collection of epigenetic states in each cell. We applied the numerical scheme to model the two processes of inflammation-induced tumorigenesis and tumor relapse after CD19 chimeric antigen receptor(CAR) T cell therapy of acute B lymphoblastic leukemia (B-ALL). Model simulations reveal the multiple pathways of inflammation-induced tumorigenesis, and the a mechanism of tumor relapse due to leukemic cell plasticity induced by CAR-T therapy stress.

学术报告2

报告题目：Computational methods to elucidate chromatin topological structures using 3D genomic maps

主讲人：张世华，中国科学院数学与系统科学研究院教授  

报告时间：2019年12月6日15:50 - 16:30

报告地点：南校区新数学楼416室

主持：张家军副教授

摘要：The chromosome conformation capture (3C) technique and its variants have been employed to reveal the existence of a hierarchy of structures in three-dimensional (3D) chromosomal architecture, including compartments, topologically associating domains (TADs), sub-TADs and chromatin loops. In this talk, I am going to introduce three methods on deciphering 3D genomic maps: (1) a mixed-scale dense convolutional neural network model (HiCMSD) to enhance low-resolution Hi-C interaction map for deciphering accurate multi-scale topological structures; (2) a generic and efficient method to identify multi-scale topological domains (MSTD), including cis- and trans-interacting regions, from a variety of 3D genomic datasets; (3) a powerful and robust circular trajectory reconstruction tool CIRCLET without specifying a starting cell for resolving cell cycle phases of single cells by considering multi-scale features of chromosomal architectures.

学术报告3

报告题目：肿瘤细胞逆向分化机制的数学模型及其推断问题

主讲人：周达，厦门大学数学科学学院副教授  

报告时间：2019年12月6日16:40 - 17:20

报告地点：南校区新数学楼416室

主持：张家军副教授

摘要：Many fast renewing tissues are characterized by a hierarchical cellular architecture, with tissue specific stem cells at the root of the cellular hierarchy, differentiating into a whole range of specialized cells. There is increasing evidence that tumors are structured in a very similar way, mirroring the hierarchical structure of the host tissue. In some tissues, differentiated cells can also revert to the stem cell phenotype, which increases the risk that mutant cells lead to long lasting clones in the tissue. In this talk, I will review some recent development of cancer cell de-differentiation. We are particularly interested in how to detect de-differentiation based on experimental data, and why de-differentiation could be selected as an adaptive mechanism in the context of cellular hierarchies.