AUTHOR=Lecca Paola , Lombardi Giulia , Latorre Roberta Valeria , Sorio Claudio 

TITLE=How the latent geometry of a biological network provides information on its dynamics: the case of the gene network of chronic myeloid leukaemia

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2023.1235116

DOI=10.3389/fcell.2023.1235116

ISSN=2296-634X

ABSTRACT=The concept of the latent geometry of a network that can be represented as a graph has emerged from the classrooms of mathematicians and theoretical physicists to become an indispensable tool for determining the structural and dynamic properties of the network in many application areas, including contact networks, social networks, and especially biological networks. And it is precisely latent geometry that we discuss in this article to show how the geometry of the metric space of the graph representing the network can influence its dynamics. The identification of the optimal latent geometry becomes important not only because it determines the topological properties of the graph, a well-established result, but also because in it it is possible to link the concept of dissimilarity between nodes to the concept of distance and thus of the transmission speed of the interaction between nodes, i.e. the propensity and efficiency of their interaction. To give an example of the usefulness of studying the variety, size and curvature of the metric space of a biological network, here we examine the gene network reconstructed from genes involved in the onset and development of chronic myeloid leukaemia (CML). The correct identification of the latent geometry of the network leads to experimentally confirmed results, and, because of this, it is a trustable mean to unveil important information on the dynamics of the network. In the case study under consideration, we show how from the identification of a hyperbolic latent geometry, it is possible to determine a list of candidate gene drivers of the network dynamics.