Genome-wide system analysis reveals stable yet flexible network dynamics in yeast.
Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in Saccharomyces cerevisae (budding yeast). We recover static properties like hubs (genes having several out-going connections), network motifs and modules, which have previously been derived from multiple data sources such as whole-genome expression measurements, literature mining, protein-protein and transcription factor binding data. Further, our analysis uncovers some novel dynamical design principles; hubs are both repressed and repressors, and the intra-modular dynamics are either strongly activating or repressing whereas inter-modular couplings are weak. Finally, taking advantage of the inferred strength and direction of all interactions, we perform a global dynamical systems analysis of the network. Our inferred dynamics of hubs, motifs and modules produce a more stable network than what is expected given randomised versions. The main contribution of the repressed hubs is to increase system stability, while higher order dynamic effects (e.g. module dynamics) mainly increase system flexibility. Altogether, the presence of hubs, motifs and modules induce few flexible modes, to which the network is extra sensitive to an external signal. We believe that our approach, and the inferred biological mode of strong flexibility and stability, will also apply to other cellular networks and adaptive systems.
Gustafsson
M
M
Department of Science and Technology, Linköping University, Norrkoping, Sweden. micho@itn.liu.se
Hörnquist
M
M
Björkegren
J
J
Tegnér
J
J
D000222:Adaptation, Physiological / Q000502:physiology; D002453:Cell Cycle / Q000502:physiology; D018797:Cell Cycle Proteins / Q000378:metabolism*; D003198:Computer Simulation; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D012441:Saccharomyces cerevisiae / Q000166:cytology* / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
D018797:Cell Cycle Proteins; D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins