Capturing drug responses by quantitative promoter activity profiling.

Quantitative analysis of cellular responses to drugs is of major interest in pharmaceutical research. Microarray technologies have been widely used for monitoring genome-wide expression changes. However, this approach has several limitations in terms of coverage of targeted RNAs, sensitivity, and quantitativeness, which are crucial for accurate monitoring of cellular responses. In this article, we report an application of genome-wide and quantitative profiling of cellular responses to drugs. We monitored promoter activities in MCF-7 cells by Cap Analysis of Gene Expression using a single-molecule sequencer. We identified a distinct set of promoters affected even by subtle inhibition of the Ras-ERK and phosphatidylinositol-3-kinase-Akt signal-transduction pathways. Furthermore, we succeeded in explaining the majority of promoter responses to inhibition of the upstream epidermal growth factor receptor kinase quantitatively based on the promoter profiles upon inhibition of the two individual downstream signaling pathways. Our results demonstrate unexplored utility of highly quantitative promoter activity profiling in drug research.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e77; doi:10.1038/psp.2013.53; published online 25 September 2013.

Inference of S-system models of genetic networks by solving one-dimensional function optimization problems.

Voit and Almeida have proposed the decoupling approach as a method for inferring the S-system models of genetic networks. The decoupling approach defines the inference of a genetic network as a problem requiring the solutions of sets of algebraic equations. The computation can be accomplished in a very short time, as the approach estimates S-system parameters without solving any of the differential equations. Yet the defined algebraic equations are non-linear, which sometimes prevents us from finding reasonable S-system parameters. In this study, we propose a new technique to overcome this drawback of the decoupling approach. This technique transforms the problem of solving each set of algebraic equations into a one-dimensional function optimization problem. The computation can still be accomplished in a relatively short time, as the problem is transformed by solving a linear programming problem. We confirm the effectiveness of the proposed approach through numerical experiments.