Mohammad Taheri-Ledari - Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
Amirali Zandieh - Department of Biophysics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
Kaveh Kavousi - Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
Sayed-Amir Marashi - Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran

Personalized molecular networks help in elucidating drug resistance mechanisms, finding new therapeutictargets, and predicting the effectiveness of combination therapy. In this study a method for simultaneouslyinferring gene regulatory network and mode-of-action of drugs from gene expression measurements of drugperturbedcancer cell-lines is proposed. An inferred model is a dynamic state-space representation whosevariables are genes and inputs are drug dosages. Such a model includes gene-gene and drug-gene (mode-ofaction)relationships. To implement the proposed method, drug-induced expression levels of nearly ۱۰۰۰landmark genes for a set of cell-lines in the LINCS database were employed. Since no time-series geneexpression data are available the cell-lines are assumed to be in steady-state. The central challenge of thepresent study is under-determinedness for which a set of solutions are examined. Finally, the performance ofthe proposed method is evaluated by performing a cross-validation on the set of drugs for a specific cell-line.

Dynamical system, State-space model, Network inference, Gene regulatory network, Drug mode-of-action, Under-determinedness, Cancer cell-line