Mathematics Seminar - 03/07/24

Abstract:

Cell cycle operates through a complex interplay of reactions that orchestrate the sequence of cell cycle events. Given the intricate nature of the regulatory network governing the cell cycle—characterized by numerous feedback and feed-forward loops and crosstalk—understanding its dynamic behavior requires more than intuitive reasoning alone. Therefore, the regulatory network is often converted into a set of ordinary differential equations which allows one to simulate network dynamics and better understand how it determines cell cycle progression. However, constructing a model of cell cycle regulation is inherently challenging, involving around 100 variables and equations, along with over 100 unknown parameters. The model is usually calibrated by comparing the computed behavior of the model with the actual behavior of cells. Yet, determining precise parameter values from available experimental data through a systematic search of the multidimensional parameter space is impossible. Moreover, the cell cycle regulatory network itself is uncertain. To overcome these challenges, various approaches are employed to model cell cycle regulation. I will present a "standard component" modeling approach that combines advantageous features of Boolean networks and differential equations. This framework allows us to generate quantitative predictions while reducing the number of adjustable parameters, thereby easing the burden of estimation from data. I will also present our perturbation approach, which is used to evaluate cell cycle regulatory networks and predict networks that better capture cell cycle dynamics. Finally, I will briefly touch upon other strategies that can help us make progress in building comprehensive data-centric models of protein regulatory networks.

For more information, please contact:  Dr. Vu Thai Luan, luan@math.msstate.edu, (662)-325-7162