@article{
	11391_1456359,
	author = {Bianconi, F. and Antonini, C. and Tomassoni, L. and Valigi, P.},
	title = {CRA toolbox: Software package for conditional robustness analysis of cancer systems biology models in MATLAB},
	year = {2019},
	journal = {BMC BIOINFORMATICS},
	volume = {20},
	abstract = {In cancer research, robustness of a complex biochemical network is one of the most relevant properties to investigate for the development of novel targeted therapies. In cancer systems biology, biological networks are typically modeled through Ordinary Differential Equation (ODE) models. Hence, robustness analysis consists in quantifying how much the temporal behavior of a specific node is influenced by the perturbation of model parameters. The Conditional Robustness Algorithm (CRA) is a valuable methodology to perform robustness analysis on a selected output variable, representative of the proliferation activity of cancer disease.},
	keywords = {Conditional robustness analysis; MATLAB package; Ordinary differential equation models; Signaling networks; Animals; Computer Simulation; Disease Models, Animal; ErbB Receptors; Humans; Kinetics; Male; Mice; Neoplasms; Organ Specificity; PTEN Phosphohydrolase; Prostate; Receptor, IGF Type 1; Signal Transduction; Systems Biology; Algorithms; Models, Biological; Software},
	url = {http://www.biomedcentral.com/bmcbioinformatics/},
	doi = {10.1186/s12859-019-2933-z},
	number = {1}
}

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In cancer systems biology, biological networks are typically modeled through Ordinary Differential Equation (ODE) models. Hence, robustness analysis consists in quantifying how much the temporal behavior of a specific node is influenced by the perturbation of model parameters. The Conditional Robustness Algorithm (CRA) is a valuable methodology to perform robustness analysis on a selected output variable, representative of the proliferation activity of cancer disease.","keywords":"Conditional robustness analysis; MATLAB package; Ordinary differential equation models; Signaling networks; Animals; Computer Simulation; Disease Models, Animal; ErbB Receptors; Humans; Kinetics; Male; Mice; Neoplasms; Organ Specificity; PTEN Phosphohydrolase; Prostate; Receptor, IGF Type 1; Signal Transduction; Systems Biology; Algorithms; Models, Biological; Software","url":"http://www.biomedcentral.com/bmcbioinformatics/","doi":"10.1186/s12859-019-2933-z","number":"1","bibtex":"@article{\n\t11391_1456359,\n\tauthor = {Bianconi, F. and Antonini, C. and Tomassoni, L. and Valigi, P.},\n\ttitle = {CRA toolbox: Software package for conditional robustness analysis of cancer systems biology models in MATLAB},\n\tyear = {2019},\n\tjournal = {BMC BIOINFORMATICS},\n\tvolume = {20},\n\tabstract = {In cancer research, robustness of a complex biochemical network is one of the most relevant properties to investigate for the development of novel targeted therapies. 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