报告题目:
Mining biological function by using single cell and spatial transcriptomics data
报告摘要:
Biological functional activities include intracellular functions such as transcriptional regulation, metabolism, and signaling transduction, and intercellular activities such as cell-cell interactions. My long-term career goal is to develop cutting edge mathematical formulations and methods to identify and quantify meaningful biological functions in different systems from multi-omics data. We have recently developed a computational method, namely scFEA, to estimate cell-wise metabolic fluxome by using single-cell RNA-seq data. To the best of our knowledge, this is the first and only method can estimate sample-wise flux distribution of a metabolic network in single cell resolution. scFEA utilizes a factor graph base representation of metabolic network and a novel graph neural network model for flux estimation, by assuming metabolic flux can be modeled as a neural network of the genes involved in neighboring reactions, and minimization of the flux imbalance of intermediate metabolites. We further developed downstream functions to compute (i) accumulation or depletion of metabolites, (ii) the impact of each gene on metabolic fluxome, and (iii) the subset of cells having distinct variation of certain metabolic modules. I will also introduce a few other computational capabilities developed by my lab to characterize metabolic shifts, transcriptional regulation, cell-cell interaction in disease tissue microenvironment by using single cell or spatial transcriptomics data.
报告人简介：
张驰，美国印第安纳大学医学院医学与分子遗传系副教授，本科毕业于北京大学数学系（06级），博士毕业于佐治亚大学生物信息专业， 于2016-2022年在印第安纳大学医学院任助理教授，现晋升为副教授。张驰实验室的研究主要专注于开发全新的数学模型与人工智能算法，挖掘生物及医疗大数据，完成对生物或临床问题中的复杂机理的建模，对新药物靶点及抗药机制的预测，以及提出全新生物医疗问题的研究可能。曾获NSF Career Award，美国癌症科研协会青年科学家奖，共同主持或参与多项NIH,NSF,与DOD基金，也是NIH资助的儿童肿瘤卓越研究专业项目DHART SPORE中生物统计及生物信息方面的负责人。张驰实验室近年的必赢766net手机版发表在Nucleic Acids Research, Genome Research, Journal of Clinical Research, NeurIPS, AAAI，Biometrics等杂志或会议，参与的合作研究发表在New England Journal of Medicine，Nature Medicine等杂志。
会议地址：腾讯会议：368-538-325
会议时间：2023年3月28日8:00-10:00
