(693d) Robustness Analysis and Perturbation Assay Revealing Novel Cell Cycle Regulators

Short Abstract ? Multi-scale robustness analysis can exploit the predictive power of computational biological models. We present an integrative systems-level approach for seeking novel regulators that contribute to the robustness of a gene regulatory network. Based on a hybrid model of the Caulobacter cell division cycle, we applied various analytical tools to the in silico model in order to identify vulnerable operating points. Replicating these conditions in vivo through perturbation assays led to the experimental discovery of novel regulatory functions, which were capable of rescuing cell cycle in various growth conditions.

Keywords ? robustness, cell cycle, Caulobacter, asymmetric cell division I. BACKGROUND C ell-division cycle is a fundamental process, which controls many aspects of development. We study Caulobacter crescentus, a popular model for understanding bacterial asymmetric division, which shares many common features with that of eukaryotic stem cells. In addition to ODE based models published by other groups, we have shown that a hybrid in silico model was able to simulate the entire cell cycle by accommodating the complexity and incompleteness of our understanding. A great number of discoveries have recently been made on the spatial mechanisms that are critical to the asymmetric cell division, making it more challenging to understand the integrated time-space feedback controls formed in the cell cycle regulatory network. We are also starting to discover subtle, non-essential regulatory motifs that do not simply turn on or off a cell function, but make cell cycle robustness under various. II. MATERIALS AND METHODS We conducted a multi-scale robustness analysis on an updated Caulobacter cell cycle model to look for novel regulatory functions. We first performed a parameter sensitivity analysis to identify the critical parameters to which the in silico cell cycle model was sensitive. A following Monte Carlo simulation gave us a measure of robustness in terms of how often the cell cycle would fail when parameters deviate from their nominal values. Next, we performed a more rigorous analysis using symbolic modeling checking (SMV), which is a formal verification tool developed in engineering for checking signal timing. SMV essentially varies the relative timing of all the cell cycle events and searches the entire state space in order to captures ?logic errors?, or wrong discrete decisions made by the cell cycle control circuits. A more detailed stochastic analysis using the SSA solver was then performed on selected pathways to yield more insights. The multi-scale robustness analysis provided a list of specific clues for finding regulators that were missing in the in silico model. III. RESULTS The robustness analysis generated a list of conditions under which the in silico cell cycle model would fail. We used perturbation assays to create these conditions in vivo, which showed that Caulobacter cells could still divide under these predicted conditions. The discrepancies between in silico model and in vivo experiments led to the discovery of several novel mechanisms the Caulobacter cells employ to achieve robust control over its varying environment. As a case study, we discovered a new regulator, BmrA, which forms multiple nested feedbacks with the master cell cycle regulator CtrA, DnaA, and CcrM. The regulatory role of BmrA was not detectable under normal cell division rates. However, under both fast and slow growth conditions, BmrA dramatically reduces the failure rate of the cell cycle by balancing the relative levels between the master regulators. We also found extra methylation mechanisms and promoters that work synergistically to improve the robustness of the Caulobacter cell cycle. IV. CONCLUSION Multi-scale robustness analysis and target perturbation assays allow computational models to predict new regulatory functions that are not obvious from simulations. It also provides testable hypotheses for experimentalists to follow through on.