2010/01/31 by Viviane Galvao, Viviane Galvão, José García Vivas Miranda +7 · 3 citations
Biochemistry, Genetics and Molecular Biology · #Artificial intelligence #Biology #Cell #Cell Image Analysis Techniques #Complex network #Computer science #Context (archaeology) #Evolutionary biology #Gene Regulatory Network Analysis #Genetics #Modular design #Modularity (biology) #Multicellular organism #Single-cell and spatial transcriptomics #q-bio.CB #q-bio.MN
paper · pdf · doi:10.1073/pnas.0914748107
published as Proc. Nat. Acad. Sci. 107, 5750 (2010) · 32 pages, 7 figures
openalex publication_date 2010/03/10 · arxiv created 2016/03/28 · arxiv updated 2016/03/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Cell differentiation in multicellular organisms is a complex process whose mechanism can be understood by a reductionist approach, in which the individual processes that control the generation of different cell types are identified. Alternatively, a large-scale approach in search of different organizational features of the growth stages promises to reveal its modular global structure with the goal of discovering previously unknown relations between cell types. Here, we sort and analyze a large set of scattered data to construct the network of human cell differentiation (NHCD) based on cell types (nodes) and differentiation steps (links) from the fertilized egg to a developed human. We discover a dynamical law of critical branching that reveals a self-similar regularity in the modular organization of the network, and allows us to observe the network at different scales. The emerging picture clearly identifies clusters of cell types following a hierarchical organization, ranging from sub-modules to super-modules of specialized tissues and organs on varying scales. This discovery will allow one to treat the development of a particular cell function in the context of the complex network of human development as a whole. Our results point to an integrated large-scale view of the network of cell types systematically revealing ties between previously unrelated domains in organ functions.