Computational Modeling of Cell and Tissue Level Metabolic Characterization of the Human Metabolic Network by Using scRNA-seq Data

Abstract

The heterogeneity of metabolic pathways is a hallmark of many common disease types. Nowadays, there are several sources of knowledge on the core components of metabolic networks and sub-networks we have obtained, however, there are still limitations in our knowledge of the integrated behavior and metabolic reprogramming of cells microenvironment. Basically, the metabolic changes can be characterized by different factors, and the changes are different from one cell to another cell because of their high plasticity. The large amount of single-cell and tissue data gained from disease tissue has the potential to provide information on a cell functioning state and its underlying phenotypic changes. Hence, advanced systems biology models and computational tools are in pressing need to empower reliable characterization of metabolic variations in disease by using scRNA-seq data. Our preliminary data include (1) a new computational method to estimate cell-wise metabolic flux and states from single-cell and tissue transcriptomics data, and (2) matched scRNA-seq data and metabolomics experiment on cells under perturbed biochemical conditions and knock-down of metabolic genes, both of which form the computational and experimental foundations of this project. In this dissertation, we proposed to develop a suite of novel computational methods, systems biology models, and quantitative metrics to bring the following unmet capabilities: (1) reconstruction of context-specific and subcellular-resolution metabolic network for different disease types, (2) estimation of cell-/sample-wise metabolic flux by considering metabolic imbalance, metabolic exchange between cells in the disease microenvironment, (3) a systematic evaluation of the functional impact of variations in gene expression, metabolite availability and network structure on the context-specific metabolic network and flux. By implementing these methods using scRNA-seq data, we addressed the following outstanding biological questions: (i) identification of genes, metabolites, and network topology with high impact on metabolic variations, (ii) estimation of metabolic flux, and (iv) assessment of metabolic changes over metabolic network. Successful execution of the proposed research provides a suite of computational capabilities to analyze metabolic variations that could be broadly utilized by the biomedical research community.