the spindle assembly checkpoint has been satisfied, there will be some sharp rise and fall of Cln2 and Clb5. Start, Irreversibility, and Commitment to the Cell Cycle ergodic set was found, whose cyclin activity profile is pictured in Robustness Robustness of biological systems is critical to the proper function of processes such as the cell cycle. Within our modeling regime Ergodic Sets for BYCC noise is interpreted as the systems’ sensitivity to the control function, and the robustness of the ergodic sets to random perturbations, respectively. To consider the system’s sensitivity to the control function, we considered the activity functions of the ergodic sets. As noted in the previous section, the activity functions governing most of the key species in the system are constant, and hence independent of CSS during the first three phases of the cell cycle. In particular, Cln2, Clb5, and Clb2, which drive bud formation, DNA replication, and mitosis, MRT-67307 web respectively, have their activities governed by constant functions. This indicates that so long as the checkpoints are appropriately activated, the modeled cell will progress through the cell cycle independently of CSS. Therefore our model is robust to the 
variable behavior of the cell growth. Furthermore, this is also consistent with the findings in that a cell deprived of nitrogen will proceed through one round of division and arrest in G1. We modeled this scenario by removing the cell size signal right after Start has been satisfied. Consistent with, as a control function we chose q1 ~t i for t, we show that it is possible to model cellular phenotype as ergodic sets in the context of probabilistic Boolean control networks. In contrast to previous works utilizing Boolean models, our approach centers around understanding not only the cell cycle as a whole, but also its individual phases. Specifically, we modeled the cell cycle as a sequence of models, each representing an individual phase in the cycle. This approach has significant implications as to how the dynamics of the modeled cell cycle are interpreted and are compared with experimental studies. Specifically, in previous works the yeast cell cycle was modeled as a single system where the phases were represented as transient states leading to a attractor corresponding to the G1 phase, or as consecutive states in a cyclic attractor. Considering each phase as an evolving system of its own enabled us to capture continuous dynamics of key species during each phase and compare them to laboratory studies. Modeling each phase separately and transitioning between models via the activation of checkpoints is also consistent with the biological observations that it is not only kinase activity that causes phase transitions, but the completion of each phases task. Ergodic Sets for BYCC 6 Ergodic Sets for BYCC Similar to we find that each phase of the cell cycle and thus the cell cycle as a whole is robust as measured by basin size, i.e. the existence of a single ergodic set for each phase. Stability of the yeast cell cycle has also been considered in the framework of probabilistic Boolean networks, concluding the cell cycle attractor is robust to internal noise. However, this approach is incompatible with our goal of exploring the relevance of ergodic sets as it renders the entire state space a single ergodic set. Modeling extracellular signals as continuous variables allowed us show the stability of the yeast cell cycle network under different choi