Research

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.