title: Energy landscape reveals that the budding yeast cell cycle is a robust and adaptive multi-stage process.
abstract: Quantitatively understanding the robustness, adaptivity and efficiency of cell cycle dynamics under the influence of noise is a fundamental but difficult question to answer for most eukaryotic organisms. Using a simplified budding yeast cell cycle model perturbed by intrinsic noise, we systematically explore these issues from an energy landscape point of view by constructing an energy landscape for the considered system based on large deviation theory. Analysis shows that the cell cycle trajectory is sharply confined by the ambient energy barrier, and the landscape along this trajectory exhibits a generally flat shape. We explain the evolution of the system on this flat path by incorporating its non-gradient nature. Furthermore, we illustrate how this global landscape changes in response to external signals, observing a nice transformation of the landscapes as the excitable system approaches a limit cycle system when nutrients are sufficient, as well as the formation of additional energy wells when the DNA replication checkpoint is activated. By taking into account the finite volume effect, we find additional pits along the flat cycle path in the landscape associated with the checkpoint mechanism of the cell cycle. The difference between the landscapes induced by intrinsic and extrinsic noise is also discussed. In our opinion, this meticulous structure of the energy landscape for our simplified model is of general interest to other cell cycle dynamics, and the proposed methods can be applied to study similar biological systems.
authors: School of Physics, Peking University, Beijing, China.
authors: Cheng
authors: C
authors: Lv
authors: LMAM and School of Mathematical Sciences, Peking University, Beijing, China.
authors: Xiaoguang
authors: X
authors: Li
authors: School of Physics, Peking University, Beijing, China; Center of Quantitative Biology, Peking University, Beijing, China.
authors: Fangting
authors: F
authors: Li
authors: LMAM and School of Mathematical Sciences, Peking University, Beijing, China.
authors: Tiejun
authors: T
authors: Li
mesh_terms: D000465:Algorithms; D002453:Cell Cycle / Q000502:physiology*; D019295:Computational Biology; D004734:Energy Metabolism; D008954:Models, Biological; D004718:Saccharomycetales / Q000502:physiology*