Abstract
BackgroundIn 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.ResultsHere we introduce our new freely downloadable software, the CRA Toolbox. The CRA Toolbox is an Object-Oriented MATLAB package which implements the features of CRA for ODE models. It offers the users the ability to import a mathematical model in Systems Biology Markup Language (SBML), to perturb the model parameter space and to choose the reference node for the robustness analysis. The CRA Toolbox allows the users to visualize and save all the generated results through a user-friendly Graphical User Interface (GUI). The CRA Toolbox has a modular and flexible architecture since it is designed according to some engineering design patterns. This tool has been successfully applied in three nonlinear ODE models: the Prostate-specific Pten−/− mouse model, the Pulse Generator Network and the EGFR-IGF1R pathway.ConclusionsThe CRA Toolbox for MATLAB is an open-source tool implementing the CRA to perform conditional robustness analysis. With its unique set of functions, the CRA Toolbox is a remarkable software for the topological study of biological networks. The source and example code and the corresponding documentation are freely available at the web site: http://gitlab.ict4life.com/SysBiOThe/CRA-Matlab.
Highlights
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
Results we show how to use the Conditional Robustness Algorithm (CRA) Toolbox and we report the results of the application of CRA to three different Ordinary Differential Equation (ODE) models: the Prostate-specific Pten−/− mouse model, the Pulse Generator Network and the EGFR-IGF1R pathway
Prostate-specific Pten−/− mouse model we use the ODE model proposed in [13] to illustrate the functionalities of the CRA Toolbox
Summary
Robustness of a complex biochemical network is one of the most relevant properties to investigate for the development of novel targeted therapies. In Systems Biology, mathematical modeling and computational software are important tools to unravel the complexity of biological systems and predict their behavior under different perturbations [1]. Since cell growth is driven by protein interaction networks, the proliferation activity can be quantified by looking at the activation of a protein involved in the proliferation process [5] In mathematical modeling, this can be done by perturbing model parameters and analyze how the concentration of the protein of interest changes over time. An algorithm developed for this purpose is the Conditional Robustness Algorithm (CRA) proposed in [5] This algorithm, through computational perturbations and simulations, identifies a small number of nodes in the cancer network which influences most the activity of the proliferation indicator. By conditioning these nodes with specific drugs, it may be possible to reduce the tumor robustness
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