Abstract
The task of the DREAM4 (Dialogue for Reverse Engineering Assessments and Methods) “Predictive signaling network modeling” challenge was to develop a method that, from single-stimulus/inhibitor data, reconstructs a cause-effect network to be used to predict the protein activity level in multi-stimulus/inhibitor experimental conditions. The method presented in this paper, one of the best performing in this challenge, consists of 3 steps: 1. Boolean tables are inferred from single-stimulus/inhibitor data to classify whether a particular combination of stimulus and inhibitor is affecting the protein. 2. A cause-effect network is reconstructed starting from these tables. 3. Training data are linearly combined according to rules inferred from the reconstructed network. This method, although simple, permits one to achieve a good performance providing reasonable predictions based on a reconstructed network compatible with knowledge from the literature. It can be potentially used to predict how signaling pathways are affected by different ligands and how this response is altered by diseases.
Highlights
There is an increasing agreement of the scientific community in attributing complex disease such as cancer, diabetes, heart disease and autoimmunity to defects in signaling trasduction pathways
We present a simple method able to reconstruct, from single-stimulus/inhibitor protein data, cause-effect networks representing signaling pathways and to predict protein levels during multi-stimulus/inhibitor perturbations
This method, developed and applied to the Predictive Signaling Network Modeling challenge of DREAM4 competition, can be used to discover how signaling pathways are altered by diseases and to predict the effect of multiple agents/drugs
Summary
There is an increasing agreement of the scientific community in attributing complex disease such as cancer, diabetes, heart disease and autoimmunity to defects in signaling trasduction pathways. In the case of cancer, it is generally acknowledged that genetic mutations are involved in the onset of the disease, but its manifestation is at the pathway functional signaling level [1,2]. An important step towards a dynamic understanding of the functions and behaviors relevant to a particular system is modeling protein interactions, by integrating available knowledge on signaling pathways with novel highthroughput protein expression data. Some pioneering efforts were accomplished by Li et al [3] who developed a computational framework for a functional input-output description of the Tolllike receptor signaling and the identification of potential targets for its modulation, and by Mitsos et al [4] who proposed a computational approach based on the experimental protocol introduced in [5] and a methodology to create cell-specific Boolean models as presented in [6], to evaluate drug actions on signaling pathways
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call