• TITLE: Application of Robust Model Validation Using SOSTOOLS to the Study of G-Protein Signaling in Yeast
• AUTHORS: Tau-Mu Yi, Maryam Fazel, Xin Liu, Tosin Otitoju, Antonis Papachristodoulou, Stephen Prajna, and John Doyle.
• ABSTACT: Two major methodological challenges in modeling biological systems are model (in)validation and parameter estimation. The traditional approach is to fit the model parameters to data. If the fit is good enough, the model is considered validated, and a single consistent vector of parameter values is identified. An alternative approach pioneered by Packard, Frenklach, Seiler and colleagues (Frenklach et al., 2002) can be used to define the range of parameter values that is consistent with the data. In addition, parametric and data uncertainty are explicitly described. If an invalidation certificate is found, the feasible parameter space is proved empty; otherwise, attempts to describe the feasible parameter space are carried out. We refer to this methodology as Robust Model Validation (RMV). Here we perform RMV using sum of squares (SOS) programs implemented by the MATLAB toolbox SOSTOOLS (Prajna et al., 2002) to generate semidefinite programs (SDPs) to either invalidate the model or determine parameter bounds. The principal advantage of SOS over conventional SDP techniques such as the S-procedure is the possibility of using higher-order multipliers to obtain tighter parameter bounds. We applied SOSTOOLS to a simple model of the yeast heterotrimeric G-protein cycle and experimental input-output data. We were able to invalidate the model based on this real data. Furthermore, using synthetic data that did not invalidate the model, we explored different techniques for representing the feasible parameter space.
• STATUS: In Proceedings of FOSBE (Foundations of Systems Biology and Engineering), Aug 2005.
• FOSBE paper: pdf file