Manuscript Topics

Owing to the fast development of high throughput sequencing technologies, the biological research is being revolutionized in both depth and breadth. To further facilitate the development and applications of the omics technology in biomedicine, mathematical and computational methods are expected to adequately exact information from omics data and to better understand the biological systems or diseases.

Mathematical modeling has a long history in biological research. The traditional mathematical models are mostly built for small systems with low dimensions. Although such models are suitable for mathematical analysis but fall short on predicting complex biological behavior, especially in the absence of experimental data. Nowadays, omics data have been increasingly generated and accumulated, providing a good measurement and reflection of biological systems. How to use such omics data to inform mathematical modeling is a promising direction. The challenge lies in the high-dimensionality of the data. Therefore, novel mathematical models or methods that are driven by high-dimensional omics data are required to be developed. In the meanwhile, statistical inference or machine learning methods are usually needed to be employed to infer the model structure and/or parameters.

More recently, single cell omics technologies have been developing fast. Multiple types of single cell omics data (e.g., single cell RNA-seq data, single cell ATAC-seq, spatial transcriptomics) have attracted more and more attentions and interests of computational biologists. Many questions have been raised with respect to data processing, representation, inference or integration in the era of single cell biology. For example, cell type-specific gene regulatory networks or cell-cell communication networks can be constructed based on such single cell omics data, shed new lights on novel biomarker identification and precision medicine.

Proposal topics:
Omics data-informed mathematical modeling
Omics data-driven statistical inference
Computational biology based on omics data
Omics informatics and database
Single cell RNA-seq data analysis
Spatial transcriptomics
Multi-omics integration
Gene regulatory network inference from omics data
Cell-cell communication inference
Network-based biomarker identification
Systems biology and precision medicine

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