**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