Robustness of biochemical networks

12918 2015 216 Fig1 HTML

Abstract

Background

The study of cancer therapy is a key issue in the field of oncology research and the development of target therapies is one of the main problems currently under investigation. This is particularly relevant in different types of tumor where traditional chemotherapy approaches often fail, such as lung cancer.

Results

We started from the general definition of robustness introduced by Kitano and applied it to the analysis of dynamical biochemical networks, proposing a new algorithm based on moment independent analysis of input/output uncertainty. The framework utilizes novel computational methods which enable evaluating the model fragility with respect to quantitative performance measures and parameters such as reaction rate constants and initial conditions. The algorithm generates a small subset of parameters that can be used to act on complex networks and to obtain the desired behaviors. We have applied the proposed framework to the EGFR-IGF1R signal transduction network, a crucial pathway in lung cancer, as an example of Cancer Systems Biology application in drug discovery. Furthermore, we have tested our framework on a pulse generator network as an example of Synthetic Biology application, thus proving the suitability of our methodology to the characterization of the input/output synthetic circuits.

Conclusions

The achieved results are of immediate practical application in computational biology, and while we demonstrate their use in two specific examples, they can in fact be used to study a wider class of biological systems.

Keywords

Robustness analysis Cancer robustness Target therapies Lung cancer Drug discovery Cancer systems biology EGFR-IGF1R networks


Additional Information

For additional information and extended list of pubblications, please visit also Fortunato Bianconi's home page.


Related Publications and Citations

Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology
Open Access

@Article{Bianconi2015,
author={Bianconi, Fortunato and Baldelli, Elisa and Luovini, Vienna and Petricoin, Emanuel F. and Crin{\`o}, Lucio and Valigi, Paolo},
title={Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology},
journal="BMC Systems Biology},
year=2015,
volume=9,
number=1,
pages=70,
issn={1752-0509},
doi={10.1186/s12918-015-0216-5},
url={http://dx.doi.org/10.1186/s12918-015-0216-5}
}

 

An approach for optimally extending mathematical models of signaling networks using omics data

@inproceedings{bianconi2015approach,
title={An approach for optimally extending mathematical models of signaling networks using omics data},
author={Bianconi, Fortunato and Patiti, Federico and Baldelli, Elisa and Crin{\`o}, Lucio and Valigi, Paolo},
booktitle={Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE},
pages={6501--6504},
year={2015},
organization={IEEE}
}