Structural identification of unate-like genetic network models from time-lapse protein concentration measurements

We consider the problem of learning dynamical models of genetic regulatory networks from time-lapse measurements of gene expression. In our previous work, we described a method for the structural and parametric identification of ODE models that makes use of concurrent measurements of concentrations and synthesis rates of the gene products, and requires the knowledge of the noise statistics. In this paper we assume all these pieces of information are not simultaneously available. In particular we propose extensions of that make the method applicable to protein concentration measurements only. We discuss the performance of the method on experimental data from the network IRMA, a benchmark synthetic network engineered in yeast Saccharomices cerevisiae.