Accounting for robustness in modeling signal transduction responses

Journal of Receptor and Signal Transduction Research
Mohammad Omid Oftadeh, Sayed-Amir Marashi

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.

References

May 31, 2002·Expert Opinion on Investigational Drugs·Roy S Herbst
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Jul 30, 2009·Molecular Systems Biology·Anna Bauer-MehrenFerran Sanz
Aug 1, 2014·PLoS Computational Biology·Junil KimKwang-Hyun Cho
Feb 6, 2017·Scientific Reports·Zhaleh Hosseini, Sayed-Amir Marashi
Dec 28, 2017·Journal of Molecular Cell Biology·Xing-Ming Zhao, Shan Li
Apr 20, 2018·BMC Systems Biology·Yushan QiuXiaoqing Cheng

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