Accounting for robustness in modeling signal transduction responses. (2nd November 2018)

• Record Type: Journal Article
• Authors: Oftadeh, Mohammad Omid; Marashi, Sayed-Amir
• Is Part Of: Journal of receptor and signal transduction research. Volume 38:Number 5/6(2018)
• Page Start: 442
• Page End: 447
• Publication Date: 2018-11-02
• Subjects: Boolean model -- signaling network -- objective function -- MILP; Cell receptors -- Periodicals; Cellular signal transduction -- Periodicals; 571.6
• DOI: 10.1080/10799893.2019.1572762
• Languages: English
• ISSNs: 1079-9893

Abstract

Abstract: A classical question in systems biology is to find a Boolean model which is able to predict the observed responses of a signaling network. It has been previously shown that such models can be tailored based on experimental data. While fitting a minimum-size network to the experimentally observed data is a natural assumption, it can potentially result in a network which is not so robust against the noises in the training dataset. Indeed, it is widely accepted now that biological systems are generally evolved to be very robust. Therefore, in the present work, we extended the classical formulation of Boolean network construction in order to put weight on the robustness of the created network. We show that our method results generally in more relevant networks. Consequently, considering robustness as a design principle of biological networks can result in more realistic models.