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Xin Chen, Ph.D.



Xin CHEN, Ph.D.
Professor, Associate Director
Institute of Pharmaceutical Biotechnology
Faculty of Medicine, Zhejiang University
866 Yuhangtang Road, Hangzhou, China
Tel/Fax: +86-571-88208595
Email: xinchen a.t.
          xinchen.chen a.t.
Xin Chen, Ph.D., is currently Professor and Associate Director of the Institute of Pharmaceutical Biotechnology at Zhejiang University and Professor (status-only) of the Department of Molecular Genetics at the University of Toronto. He received two undergraduate degrees from Shanghai Jiao Tong University, one in biotechnology and one in computer science, and received his Ph.D. degree from the National University of Singapore, in computational sciences. After graduation, he worked as the director of the high-throughput biology department in Blueprint Initiative, Asia, an organization that curated the Biomolecular Interaction Network Database. In 2005, he joined Zhejiang University and once worked as associate professor, professor and associate chair of the bioinformatics program in the College of Life Sciences. He has published more than 40 research articles in peer-reviewed journals. He received the talented scientist award and the innovative research team award from the Zhejiang Provincial Natural Science Foundation of China, and received the new century excellent talents in university award from the Chinese Ministry of Education.
Research Activities
Dr. Chen’s laboratory currently works in two major areas. The first one is omics-based matching of patient physiology for personalized tumor therapy. Our current model to implement precision medicine is to first discover genetic lesions that characterize different tumor pathophysiology, and then categorize cancer patients by their driver mutations. However, there are too many genetic lesions that may lead to tumor development. If we divide cancer patients by their driver mutations, there will be too many categories with each category consisting of only a small number of patients. If traditional drug discovery and development pipeline were going to follow up such small patient bases, the per-patient R&D cost would be significantly higher than today’s average and quickly make precision medicine unaffordable to the general public. In addition, targeting a genetically precisely defined tumor cell-line will meet the problem of tumor heterogeneity. Untargeted tumor cells could go clone selection and expansion, which may lead to metastasis and recurrence. Many studies have shown that targeted therapies produce excellent immediate responses, however, their benefit on long-term survival is highly limited. A major effort of Dr. Chen’s laboratory is to explore an alternative model to implement precision medicine, which is free from these limitations.  His laboratory aims to create a method that can compare the molecular physiologies of two patients and predict whether they will produce the same response to a particular therapy. This method may help doctors create personalized tumor therapy for newly admitted patients using their experiences in treating old patients.
The other major area of work in Dr. Chen’s laboratory is to provide computational tools for synthetic biology, particularly in management of information on genetic building blocks and computer-assisted design of novel building blocks. Synthetic biology enables fast construction of cell factories for production of pharmacologically active compounds and peptides. It also explores the silent part of a microbial genome for its latent synthetic potential, with an aim to expand our current spectrum of chemical structure diversity and discover novel lead compounds with desired bioactivity and pharmacology. Starting from 2016, Dr. Chen collaborates with several leading research groups of synthetic biology in China. His laboratory provides comprehensive computational support for synthetic biology research, including the curation of specialized databases on genetic building blocks, mining novel building blocks from sequenced microbial genomes, automated discovery and annotation of regulatory elements in microbial genomes, computer-assisted design of synthetic routes, compatibility analysis between cell factories and genetic building blocks, optimization of genetic building blocks and computer-assisted design of directed evolution experiments.
Contributions to Science
Research in Dr. Chen’s laboratory focuses on the development of novel strategies and methods for treatment of complex diseases exploiting the synergy between gene functions and using multiple coordinated interventions. He and colleagues have curated a comprehensive series of drug mechanism databases and developed a computational system that predicts the macromolecular targets that a bioactive small compound may physically interact with in vivo, which permitted computational analysis of drug response mechanisms. Furthermore, he proposed the concept of homogenous functional association network and created the gene set linkage analysis (GSLA) method. GSLA evaluates a homogenous functional association network to determine whether an observed omics change, represented as a set of genes, may collaboratively exert an overall functional impact on certain biological processes or functions, which are also represented as gene sets. In previous work, this approach has been used in cell line transcriptome analysis, in tissue transcriptome analysis and in epigenome data analysis. In all cases, GSLA has demonstrated its unique capability to anticipate the synergistic effects of multiple gene changes on certain cellular functions. Some discoveries made with GSLA may lead to new therapies.
Selected Publications
1. Quantitative evaluation of human bone mesenchymal stem cells rescuing fulminant hepatic failure in pigs. D. Shi, J. Zhang, Q. Zhou, J. Xin, J. Jiang, L. Jiang, T. Wu, L. Jiang, W. Ding, S. Sun, J. Li, N. Zhou, L.Zhang, L. Jin, S. Hao, P. Chen, H. Cao, M. Li, L. Li, X. Chen*, J. Li*, Gut 66, 955-964 (2017).
2.Human interactome resource and gene set linkage analysis for the functional interpretation of biologically meaningful gene sets. X. Zhou, P. Chen, Q. Wei, X. Shen, X. Chen*, Bioinformatics 29, 2024-2031 (2013).
3.The predicted Arabidopsis interactome resource and network topology-based systems biology analyses. M. Lin, X. Zhou, X. Shen, C. Mao, X. Chen*, Plant Cell 23, 911-922 (2011).
4. Computational identification of potential molecular interactions in Arabidopsis. M. Lin, B. Hu, L. Chen, P. Sun, Y. Fan, P. Wu, X. Chen*, Plant physiology 151, 34-46 (2009).
5. GIPS: A software guide to sequencing-based direct gene cloning in forward genetics studies. H. Hu, W. Wang, Z. Zhu, J. Zhu, D. Tan, Z. Zhou, C. Mao*, X. Chen*, Plant physiology 170, 1929–1934 (2016).
6. Optimization of molecular docking scores with support vector rank regression. W. Wang, W. He, X. Zhou, X. Chen*, Proteins 81, 1386-1398 (2013).
7. Can an in silico drug-target search method be used to probe potential mechanisms of medicinal plant ingredients? X. Chen, C.Y. Ung, Y.Z. Chen, Natural Product Reports 20, 432-444 (2003).
8. DCDB: drug combination database. Y. Liu, B. Hu, C. Fu, X. Chen*, Bioinformatics 26, 587-588 (2010).
9. Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation. X. Chen*, H. Zhou, Y. B. Liu, J. F. Wang, H. Li, C. Y. Ung, L. Y. Han, Z. W. Cao, Y. Z. Chen*, British journal of pharmacology 149, 1092-1103 (2006).
10. TTD: Therapeutic Target Database. X. Chen, Z.L. Ji, Y.Z. Chen, Nucleic acids research 30, 412-415 (2002).
Selected Patents
1. A method and implementation for searching precision medicine knowledgebase using individual patient’s multi-omics traits. No. 201710218630.
2. A method and implementation for recommending personalized cancer therapy based on the molecular physiology of a patient characterized by his or her omics traits. No. 201710060889.
3. A method and implementation for gene network-based discovery of pathophysiological mechanisms underlying chronic diseases and associated strategies for risk assessment and preventive intervention. No. 201610442551.
4. Application of cytokine DLL4 in preparation of medicine for treating fulminant hepatic failure. No. 201510582307
5. A method and implementation for probability-based advisory of optimal strategy for identification of phenotype-associated genes using resequencing technology. No. 201510890563.