Mathematical Modeling of Cell Crosstalk

A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cellular Biophysics".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 2418

Special Issue Editors


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Guest Editor
Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, USA
Interests: statistical and quantitative genetics in medicine; agriculture and evolution
Special Issues, Collections and Topics in MDPI journals
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
Interests: statistical genetics; omics data analysis; bioinformatics; molecular pathway analysis

Special Issue Information

Dear Colleagues,

Many biological processes underlying phenotypic or disease formation are determined by the mechanisms of how neighbouring and distant cells crosstalk to form cellular networks. In particular, cancer cells communicate bi-directionally with their neighboring cancer cells and with the tumor microenvironment through interconnected feedback loops that generate many emergent outcomes for the tumor. Immune cells participate in many types of physical or biological interactions with other immune and nonimmune lineages, facilitating tissue repair, phagocytosis and cell death. Genomic technologies can dissect individual cell state at high resolution, which has afforded an unprecendented opportunity to revolutionize our understanding of cellular functions. However, to more precisely identify, quantify, and characterize cell-cell interactions and their impacts on fundamental organismal processes, it is sorely needed to integrate sophistaicated mathematical models into these genomic technologies. 

This issues will publish a collection of mathemetical and computational models that can study how cells communicate with each other across an organism’s diverse cell types and tissues and how cell crosstalk mediates the outcome of diseases. Written by a group of experienced researchers who stand at the frontiers of computational cell biology, these models can faciliate our understanding of the complexities of cell signaling, differentiation, and proliferation towards the formation of complex traits and diseases. Coupled with the ever-expanding availability of single-cell analysis data, these models will find their immediate implications for modelling important biological processes, such as tumour-immune dynamics and germ-soma crosstalk, with the ultimate goal of improving human health and reproduction

Prof. Dr. Rongling Wu
Dr. Song Wu
Guest Editors

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Keywords

  • cell-cell interaction
  • single-cell analysis
  • mathematical model
  • omics data
  • gene regulatory networks

Published Papers (1 paper)

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Research

25 pages, 4985 KiB  
Article
A Single-Cell Omics Network Model of Cell Crosstalk during the Formation of Primordial Follicles
by Qian Wang, Ang Dong, Libo Jiang, Christopher Griffin and Rongling Wu
Cells 2022, 11(3), 332; https://doi.org/10.3390/cells11030332 - 20 Jan 2022
Cited by 8 | Viewed by 2050
Abstract
The fate of fetal germ cells (FGCs) in primordial follicles is largely determined by how they interact with the surrounding granulosa cells. However, the molecular mechanisms underlying this interactive process remain poorly understood. Here, we develop a computational model to characterize how individual [...] Read more.
The fate of fetal germ cells (FGCs) in primordial follicles is largely determined by how they interact with the surrounding granulosa cells. However, the molecular mechanisms underlying this interactive process remain poorly understood. Here, we develop a computational model to characterize how individual genes program and rewire cellular crosstalk across FGCs and somas, how gene regulatory networks mediate signaling pathways that functionally link these two cell types, and how different FGCs diversify and evolve through cooperation and competition during embryo development. We analyze single-cell RNA-seq data of human female embryos using the new model, identifying previously uncharacterized mechanisms behind follicle development. The majority of genes (70%) promote FGC–soma synergism, only with a small portion (4%) that incur antagonism; hub genes function reciprocally between the FGC network and soma network; and germ cells tend to cooperate between different stages of development but compete in the same stage within a developmental embryo. Our network model could serve as a powerful tool to unravel the genomic signatures that mediate folliculogenesis from single-cell omics data. Full article
(This article belongs to the Special Issue Mathematical Modeling of Cell Crosstalk)
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