Layne T. Watson


Parameter estimation in systems biology is typically done by enforcing experimental observations through an objective function as the parameter space of a model is explored by numerical simulations. Past studies have shown that one usually finds a set of “feasible” parameter vectors that fit the available experimental data equally well, and that these alternative vectors can make different predictions under novel experimental conditions. In this study, we characterize the feasible region of a complex model of the budding yeast cell cycle under a large set of discrete experimental constraints in order to test whether the statistical features of relative protein abundance predictions are influenced by the topology of the cell cycle regulatory network.


Layne T. Watson

Publication Details

Date of publication:
February 28, 2017
BMC Systems Biology
Page number(s):
Issue Number:
Article No.30
Publication note:

Cihan Oguz, Layne T. Watson, William T. Baumann, John J. Tyson: Predicting network modules of cell cycle regulators using relative protein abundance statistics. BMC Syst. Biol. 11(1): 30:1-30:24 (2017)