Itziar Irurzun-Arana (1), Jose MartÃn Pastor (1), Leire Ruiz-Cerdá (1), Ignacio Gonzalez-Garcia (2), Iñaki F. Trocóniz (1), José David Gómez-Mantilla (1).
(1) Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona 31080, Spain. (2) Pharmacy and Pharmaceutical Technology Department, University of Valencia. Valencia, Spain.
Objectives: To provide an overview of the methodology to perform Boolean modeling in Systems Pharmacology, describing the required tools and steps for its implementation.
Methods: Boolean network models are the simplest discrete dynamic models in which the components of a system are represented by nodes and the interactions among them by edges. This type of networks only assumes two states for each component (ON or OFF). The state of each node is determined by its regulator nodes in the network based on Boolean Functions (BFs), which are a combination of AND, OR and NOT operations [1-3]. We describe a method to write BFs in the R environment in order to study the evolution of the constructed network as a function of time. As there is no explicit notion of time in a logic model, each round a network is updated can be considered a time step. The result of the simulations may differ depending on the updating method chosen for the model, which can be synchronous or asynchronous [1-3]. In addition, several tools were developed for the exploratory and quantitative analysis of the network output to evaluate the level of activation of the nodes in all time steps or to cluster the nodes that lead to similar alterations within the network.
Results: Our approach for Boolean modeling of Systems Pharmacology networks entails the following steps: 1) development of the model structure (nodes, edges, and BFs) based on evidence from literature or experimental data, 2) R programming of the corresponding BFs, establishing the stochastic procedure to update the nodes of the network, 3) development of a parallelized simulation algorithm, and 4) analysis of the system output. Moreover, a system perturbation analysis can be performed in order to see which node knockouts or persistent activations lead to significant changes of the network dynamics [1].
Conclusions: Since Boolean models are parameter free, they serve as a starting point for modeling complex pharmacological systems for which a detailed kinetic characterization is not available [1,3]. Applying the proposed tools, simulations of the dynamics of a biological system can be performed, studying the effect of perturbations. The resulting models can be used to analyze signaling networks associated with diseases in order to predict the pathogenesis mechanisms and design potential therapeutic targets.
References:
[1] Saadatpour, A., Albert, R., & Reluga, T. C. (2013). A Reduction Method for Boolean Network Models Proven to Conserve Attractors. SIAM Journal on Applied Dynamical Systems, 12(4), 1997–2011.
[2] Thakar, J., Pilione, M., Kirimanjeswara, G., Harvill, E. T., & Albert, R. (2007). Modeling systems-level regulation of host immune responses. PLoS Computational Biology, 3(6), e109.
[3] Wynn, M. L., Consul, N., Merajver, S. D., & Schnell, S. (2012). Logic-based models in systems biology: a predictive and parameter-free network analysis method. Integrative Biology, 4(11), 1323.
Reference: PAGE 24 () Abstr 3377 [www.page-meeting.org/?abstract=3377]
Poster: Methodology - New Modelling Approaches