Title: Cell-cell communication and interaction with scRNA-seq and topology 1. Problem or questions being asked Cell-cell communication is a challenging task since the interactions with the cells are quite complex and are difficult to understand or model with actual experiments or in silico methodologies. Integrating different types of -omics is also a complex matter since different sources of data results in different treatments that could give completely different views of a system[1,2]. These sources need particular care when handled together so a system that automates the process could be the breakthrough in modeling complex systems and how tissue is formed and what are the interactions of its components, and cells. Along the same path of complex systems and modeling, most of the methodologies in the field of cell-cell communication do not consider one of the most important sources of information about how the cells interact with each other, which is topological data. 2. Rationale for your approach Modeling the system in both its local components (gene expression, co-expression in the cell and outside of the cell) and the global structure (cell-cell communication and how the whole system behaves to activity in its components) could be done by thinking about how the components inside the cell will eventually interact with components related to cell-cell communication like ligands-receptor interactions. scRNA-seq could be used as the middleman in the modeling and simulation of physical systems and how they are dysregulated from a reference, for example when treatment is done or when a disease is present in the tissue sampled. Topology is necessary to form a model capable of simulation of a complex system where interactions do not end in their single action but propagate in a network in chain reactions. 3. Details of suggested approach The complex set of interactions within cell and cell-cell can be modeled by summarizing the local interaction in multi-layer graphs where the layers represent the cells' signaling pathways joined together in a meta-pathway while the edges between layers are ligand-receptor interactions between the cells [3]. The approach will use a modified version of the PHENSIM[4,5] methodology that accounts for the multi-layer network built and where the interactions layer-layer simulate the interactions of cell-cell since one layer will be associated with a typed-cluster identified via scRNA-seq analysis and the interactions between layers will be filtered by seeing what genes directly related to ligands and receptors are co-expressed or differentially expressed between different typed-clusters. This approach can also be expanded with the use of subgroups of the same typed cluster to account for cells of the same type/cluster communication. 4. How it will affect the broader field Simulation and modeling for complex systems of interactions between cells in a tissue can be a breakthrough for a lot of applications and can have an impact that could affect the field of personalized medicine to the core. To cancer modeling and treatment, to disease response for both drugs and cell-cell interactions in the altered tissue. Modeling also can be expanded almost seamlessly since other types of -omics like spatial transcriptomics would widen the whole spectrum of how cells interact with each other by also taking into account their place in the tissue and building a multi-dimensional graph capable of simulating a whole tissue without any difficulties if modeled and treated correctly. 5. References 1. Wang, R., Peng, G., Tam, P. P. L. & Jing, N. Integration of computational analysis and spatial transcriptomics in single-cell study. Genomics Proteomics Bioinformatics (2022) doi:10.1016/j.gpb.2022.06.006. 2. Sharma, J., Balakrishnan, L., Kaushik, S. & Kashyap, M. K. Multi-Omics Approaches to Study Signaling Pathways. (Frontiers Media SA, 2020). 3. Kumar, M. P. et al. Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics. Cell Rep. 25, 1458–1468.e4 (2018). 4. Alaimo, S. et al. PHENSIM: Phenotype Simulator. PLoS Comput. Biol. 17, e1009069 (2021). 5. Maria, N. et al. Rapid Identification of Druggable Targets and the Power of the PHENotype SIMulator for Effective Drug Repurposing in COVID-19. (2021) doi:10.21203/rs.3.rs-287183/v1.