The Optimization & Computational Systems Biology Lab is in the Institute of Applied Mathematics at Academy of Mathematics and Systems Science (AMSS) at Chinese Academy of Sciences (CAS). We also belong to the National Center for Mathematics and Interdisciplinary Sciences (NCMIS) at Chinese Academy of Sciences. Our research focuses on optimization, computational biology, and systems biology. We aim to construct networks for complex biomolecular systems such as gene regulatory networks via optimization and statistics models. By further integrating multiple data sources into network models, we aim to elucidate the relationship between sequence variant, regulatory element, regulator, gene expression, and evolution of biomolecular systems, to probe design principles of biological regulations and networks, and to investigate systems biology mechanisms of complex traits. To achieve these aims, we develop diverse computational methods ranging from theory, model, and algorithm.

• Gene Regulatory Network Modeling. : modeling and analysis of gene regulatory network. Ongoing projects in the lab include: interactions among chromatin regulators, sequence specific transcription factors, and cis-regulatory sequence elements; context specific regulatory network reconstruction.

• Computational Systems Biology. : bridging the phenotype and genotype by network modeling. Ongoing projects in the lab include: reconstruction of network models and mechanism underlying complex traits such as development, differentiation, reprogramming, and evolutionary adaption by genomic data integration; revealing regulatory elements, chromatin regulators, transcriptional factors, and genes and their function and dynamics in biological processes.

• Data Integration and Modeling. : data integration and representation modeling. Ongoing projects in the lab include: heterogeneous and multi-layer data integration methodology; matched genomic data integration; complex network data exploration; data dimensional reduction models to reveal key molecules.

• Optimization and Statistics Models. : developing novel optimization and statistical methods to analyze biological sequence variant, regulatory element, regulator, gene expression, evolution, function, phenotype data of interest. We are also interested in the nonlinear combinatorial optimization and connection between deterministic optimization and statistical models.