Research Interests:

The Zou lab is mainly interested in the evolution modes of molecular sequences and the corresponding methodology. We study interesting questions on the frontiers of molecular evolution, primarily using computational approaches such as comparative analysis, statistical test, model simulation, and machine learning. 1. Evolution patterns and modes of molecular sequences. Molecular evolution models describe the evolution process of DNA or protein sequences are among the core tools in bioinformatic analyses. In recent years, accumulation of sequence data allows us to recognize more complex patterns and modes in sequence evolution. For example, prevalent heterogeneity of features such as evolutionary rate among species or between genomic loci, and the non-independence between biochemically interacting loci in the sequence, i.e. epistasis or coevolution. Based on available comparative genomics data, we are interested in finding the biological mechanisms and driving factors of such patterns, and how to combine these factors into molecular evolution analyses, so that we can better model the patterns and processes of sequence evolution, i.e. phylogeny and adaptation, etc. 2. Application of deep learning in evolution analyses (AI for Evolution). Deep learning is a fast-advancing field of computational tools, suitable for complex pattern extraction and prediction from big data. Regarding the living system, the genotype-phenotype mapping from molecular sequences to organismal morphology and function is complex and of high orders, thus difficult to capture. We hope to harness deep learning techniques to extract complex high-order features of genomes, proteins, or phenotypes, and explore the application of such features in identifying or predicting evolution patterns such as phylogeny and adaptation. Meanwhile, we want to map phenotypes and functions back to molecular mechanisms, using interpretable AI approaches. 3. Other directions. We are broadly interested in the topics of molecular or morphological phenotype evolution, case study of genome adaptation in particular taxonomic groups, etc.

Awards and Honors:

Professional Activities:

Research Grants:

Selected Publications:

(#co-first author, *corresponding author): 17.Masubuchi T, Chen L, Marcel N, Wen GA, Caron C, Zhang J, Zhao Y, Morris GP, Chen X, Hedrick SM, Lu LF, Wu C, Zou Z*, Bui JD*, Hui E*. Functional differences between rodent and human PD-1 linked to evolutionary divergence. Sci. Immunol., 2025, 10:eads6295. doi:10.1126/sciimmunol.ads6295. 16.Lu Y, Ballerio A, Wang S, Zou Z, Gorb SN, Wang T, Li L, Ji S, Zhao Z, Li S, Tong Y, Chen Y, Zhuo D, Luo C, Zhang W, Liu N, Gu Q, Bai M. The evolution of conglobation in Ceratocanthinae. Commun. Biol., 2022, 5:1–14. doi:10.1038/s42003-022-03685-2. 15. Si S, Xu X*, Zhuang Y, Gao X, Zhang H, Zou Z*, and Luo SJ*. The genetics and evolution of eye color in domestic pigeons (Columba livia). PLOS Genet., 2021, 17:e1009770. doi: 10.1371/journal.pgen.1009770. 14. Zou Z, and Zhang J. Are nonsynonymous transversions generally more deleterious than nonsynonymous transitions? Mol. Biol. Evol., 2021, 38:181-191. doi: 10.1093/molbev/msaa200. 13.Lyons DM*, Zou Z*, Xu H, Zhang J. Idiosyncratic epistasis creates universals in mutational effects and evolutionary trajectories. Nat. Ecol. Evol., 2020, 4:1685-1693. doi: 10.1038/s41559-020-01286-y. 12. Zou Z, and Zhang J. The nature and phylogenomic impact of sequence convergence. Phylogenetics in the Genomic Era (C. Scornavacca, et al., eds). No commercial publisher | Authors open access book, pp.4.6:1-4.6:17, 2020. ffhal-02536347 11. Zou Z#, Zhang H#, Guan Y, Zhang J. Deep residual neural networks resolve quartet molecular phylogenies. Mol. Biol. Evol., 2020, 37:1495-1507. doi: 10.1093/molbev/msz307. 10.Zhang  W, Xu X, Yue B, Hou R, Xie J, Zou ZT, Han Y, Shen F, Zhang L, Xie Z, Yuan Y, Yin Y, Fu W, Chen D, Huang W, Liu Z, Tang Y, Zhao B, Zhang Q, Chen W, Zhang R, Chen J, Luo SJ, Zhang Z. Sorting out the genetic background of the last surviving South China tigers. J. Hered., 2019, 110: 641–650.doi: 10.1093/jhered/esz034. 9. Zou Z, and Zhang J. Amino acid exchangeabilities vary across the tree of life. Sci. Adv., 2019, 5: eaax3124. doi: 10.1126/sciadv.aax3124. 8. Ding X, Zou Z, Brooks III CL. 2019. Deciphering protein evolution and fitness landscapes with latent space models. Nat. Commun., 10: 5644. doi: 10.1038/s41467-019-13633-0. 7. Zou Z, and Zhang J. Gene tree discordance does not explain away the temporal decline of convergence in mammalian protein sequence evolution. Mol. Biol. Evol., 2017, 34: 1682-1688. doi: 10.1093/molbev/msx109. 6. Zou Z, and Zhang J. Morphological and molecular convergences in mammalian phylogenetics. Nat. Commun., 2016, 7: 12758. doi: 10.1038/ncomms12758. 5. Oetjens MT, Shen F, Emery SB, Zou Z and Kidd JM. 2016. Y-Chromosome structural diversity in the bonobo and chimpanzee lineages. Genome Biol. Evol., 8: 2231-2240. doi: 10.1093/gbe/evw150. 4. Zou Z, and Zhang J. Are convergent and parallel amino acid substitutions in protein evolution more prevalent than neutral expectations? Mol. Biol. Evol., 2015, 32: 2085-2096.doi: 10.1093/molbev/msv091. 3. Zou Z, and Zhang J. No genome-wide convergence for echolocation. Mol. Biol. Evol., 2015, 32: 1237-1241. doi: 10.1093/molbev/msv014. 2. Zou Z-T, Uphyrkina O, Fomenko P, Luo S-J. The development and application of a multiplex short tandem repeat (STR) system for identifying subspecies, individuals and sex in tigers. Integr. Zool., 2015, 10: 376-388. doi:10.1111/1749-4877.12136. 1.Xu X, Dong GX, Hu XS, Miao L, Zhang XL, Zhang DL, Yang HD, Zhang TY, Zou ZT, Zhang TT, Zhuang Y, Bhak J, Cho YS, Dai WT, Jiang TJ, Xie C, Li R, Luo SJ. The genetic basis of white tigers. Curr. Biol., 2013, 23:1031–1035. doi:10.1016/j.cub.2013.04.054.