Empirical multiscale networks of cellular regulation.
Grouping genes by similarity of expression across multiple cellular conditions enables the identification of cellular modules. The known functions of genes enable the characterization of the aggregate biological functions of these modules. In this paper, we use a high-throughput approach to identify the effective mutual regulatory interactions between modules composed of mouse genes from the Alliance for Cell Signaling (AfCS) murine B-lymphocyte database which tracks the response of approximately 15,000 genes following chemokine perturbation. This analysis reveals principles of cellular organization that we discuss along four conceptual axes. (1) Regulatory implications: the derived collection of influences between any two modules quantifies intuitive as well as unexpected regulatory interactions. (2) Behavior across scales: trends across global networks of varying resolution (composed of various numbers of modules) reveal principles of assembly of high-level behaviors from smaller components. (3) Temporal behavior: tracking the mutual module influences over different time intervals provides features of regulation dynamics such as duration, persistence, and periodicity. (4) Gene Ontology correspondence: the association of modules to known biological roles of individual genes describes the organization of functions within coexpressed modules of various sizes. We present key specific results in each of these four areas, as well as derive general principles of cellular organization. At the coarsest scale, the entire transcriptional network contains five divisions: two divisions devoted to ATP production/biosynthesis and DNA replication that activate all other divisions, an "extracellular interaction" division that represses all other divisions, and two divisions (proliferation/differentiation and membrane infrastructure) that activate and repress other divisions in specific ways consistent with cell cycle control.
de Bivort
Benjamin
B
Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA. bivort@fas.harvard.edu
Huang
Sui
S
Bar-Yam
Yaneer
Y
D000818:Animals; D001402:B-Lymphocytes / Q000166:cytology / Q000378:metabolism*; D002453:Cell Cycle; D048708:Cell Growth Processes; D019295:Computational Biology / Q000379:methods*; D005786:Gene Expression Regulation; D051379:Mice; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D015398:Signal Transduction; D049490:Systems Biology