长聘教轨副教授
以酵母为模式生物，系统整合多组学数据，同时结合机器学习算法，开发新一代多尺度细胞代谢模型，全面提升模型预测性能。
基于代谢模型理性预测，结合定量蛋白质组学、代谢流组学和代谢物组学，寻找工业菌株高产的代谢瓶颈与调控机制，在系统层次上重塑细胞内资源分配和代谢流调节，达到工业菌株的系统改造和优化设计。
采用系统生物学的手段，通过比较基因组学、细胞代谢模型预测和生理学实验，揭示不同酵母菌株抗逆机制和代谢多样性的进化起源。
Yuan L, Lu H* (co-corresponding author), Li F, Nielsen J and Kerkhoven* EJ. HGTphyloDetect: facilitating the identification and phylogenetic analysis of horizontal gene transfer. Briefings in Bioinformatics, 2023: 1–7.
Lu H, Kerkhoven EJ, Nielsen J. Multiscale models quantifying yeast physiology: towards a whole-cell model. Trends in Biotechnology, 2022, 40(3):291-305.
Lu H#, Li F#, Yuan L#, Domenzain I, Yu R, Wang H, Li G, Chen Y, Ji B, Kerkhoven EJ, Nielsen J*. Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection. Molecular Systems Biology, 2021(17):e10427.
Lu H#, Li F#, Sánchez BJ, Zhu Z, Li G, Domenzain I, Marcišauskas S, Anton PM, Lappa D, Lieven C, Beber ME, Sonnenschein N, Kerkhoven EJ, Nielsen J*. A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nature Communications, 2019, 10 (1):1-13.
Lu H, Liu X, Huang M*, Xia J, Chu J*, Zhuang Y, Zhang S, Noorman H. Integrated isotope-assisted metabolomics and (13)C metabolic flux analysis reveals metabolic flux redistribution for high glucoamylase production by Aspergillus niger. Microb Cell Fact, 2015(14):147.
Lu H, Cao W, Ouyang L, Xia J, Huang M* , Chu J*, Zhuang Y, Zhang S, Noorman H. Comprehensive reconstruction and in silico analysis of Aspergillus niger genome-scale metabolic network model that accounts for 1210 ORFs. Biotechnology and Bioengineering, 2017, 114(3):685-695.
Lu H#, Cao W#, Liu X, Sui Y, Ouyang L*, Xia J, Huang M, Zhuang Y, Zhang S, Noorman H, Chu J*. Multi-omics integrative analysis with genome-scale metabolic model simulation reveals global cellular adaptation of Aspergillus niger under industrial enzyme production condition. Sci Rep, 2018, 8(1):14404.
国家自然科学基金面上项目，基于深度学习策略的酶约束代谢模型构建和分析方法研究，主持，2024.01.01 - 2027.12.31
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