Biography
Xuhang received his B.S. from the School of Life Sciences at Fudan University in 2017, followed by a Ph.D. in Systems, Computational, and Quantitative Biology from UMass Chan Medical School in 2025. He joined the School of Life Sciences and the Center for Life Sciences at Peking University as an Assistant Professor in January 2026, where he established the Laboratory of Systems Metabolism. His research focuses on understanding and manipulating metabolism at the systems level, with the ultimate goal of reprogramming life from its chemistry.
Education
2017 - 2025，PhD，Systems, Computational, and Quantitative Biology，UMass Chan Medical School
2013 - 2017，BS，Biological Sciences，Fudan University
Professional Experience
2026 - present，Assistant Professor，School of Life Sciences, Peking University
2025，Postdoc，UMass Chan Medical School, Department of Systems Biology
Honors and Awards
Sydney Brenner Thesis Award, 2026
UMass Chan Chancellor’s Award, 2025
Editorial Activities
2025 - present，Youth Editorial Board，Phenomics
We seek to understand and manipulate metabolism at the systems level - broadly across the metabolic network and quantitatively considering how reactions work together - with the ultimate goal of reprogramming life from its chemistry. In parallel, we also aim to translate metabolic systems biology into systems-level diagnosis and treatment of Inborn Errors of Metabolism. To achieve these goals, our research integrates systems biology, high-throughput genomics, metabolic biology, mathematical modeling, and AI, with two complementary model systems: C. elegans and human cultured cells.
Our current interests include:
1. Understanding the wiring and rewiring of metabolism at the systems level. By integrating high-throughput genomics with metabolic modeling, we transform the metabolic problem into a genomic problem that can be addressed systematically and at high throughput. This allows us to dissect the operation and regulation of metabolism at the systems level.
2. Developing methods for manipulating metabolism at the systems level and in vivo. Building on the principles learned from studying metabolism, we aim to artificially manipulate, i.e., rewire and even wire, metabolism, and ultimately to reprogram in vivo metabolism at the network scale. To this end, we are integrating artificial intelligence approaches, e.g., protein design, to develop new strategies for metabolic manipulation in living systems.
3. Systems-level solutions for Inborn Errors of Metabolism. We are also interested in translating metabolic systems biology into clinical applications. Our current efforts focus on developing systems-level diagnostic and therapeutic solutions for Inborn Errors of Metabolism (IEM).
Please visit our lab website for more information about our science and culture.
Zhang H*, Li X*, Song D, Yukselen O, Nanda S, Kucukural A, Li JJ, Garber M, Walhout AJM. Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq. Nature Communications, (2025)
Li, X*, Zhang, H*, Hodder T, Wang W, Myers CL, Yilmaz LS, Walhout AJM. Systems-level design principles of metabolic rewiring in an animal. Nature (2025).
Zhang H*, Li X*, Tseyang LT, Giese GE, Wang H, Yao B, Zhang J, Neve RL, Shank EA, Spinelli JB, Yilmaz LS, Walhout AJM. A systems-level, semi-quantitative landscape of metabolic flux in C. elegans. Nature. (2025).
Li X, Walhout AJM, Yilmaz LS. Enhanced flux potential analysis links changes in enzyme expression to metabolic flux. Molecular Systems Biology (2025).
Li X, Yilmaz LS, Walhout AJM. Compartmentalization of metabolism between cell types in multicellular organisms: a computational perspective.Curr Opin Syst Biol.(2022) (review article)
Yilmaz LS*, Li X*, Nanda S, Fox B, Schroeder F, Walhout AJ. Modeling tissue-relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels. Molecular Systems Biology (2020) (cover story)
We seek to understand and manipulate metabolism at the systems level - broadly across the metabolic network and quantitatively considering how reactions work together. In parallel, we are also interested in translating metabolic systems biology into systems-level diagnosis and treatment of Inborn Errors of Metabolism. To achieve these goals, our research integrates systems biology, high-throughput genomics, metabolic biology, mathematical modeling, and AI. We currently use two complementary model systems - C. elegans and human cultured cells - to address these problems in a high-throughput manner.
