Itziar Irurzun-Arana (1), José David Gómez-Mantilla (1), Iñaki F. Trocóniz (1).
(1) Pharmacometrics & Systems Pharmacology, Department of Pharmacy and Pharmaceutical Technology, School of Pharmacy, University of Navarra, Pamplona 31080, Spain.
Objectives: To provide an easy-to-use and efficient methodology to perform attractor analysis on Boolean models of Systems Pharmacology networks, guiding through the required tools and steps, and showing key outcomes and their representation and impact interpretation.
Methods: Boolean network models are the simplest discrete dynamic models in which the components of a system are represented by nodes that assume two possible states, ON or OFF (1). The state of each node is determined by its regulator nodes in the network based on transition rules known as Boolean functions. For any initial condition, Boolean models eventually evolve into a limited set of stable states known as attractors (2,3). Attractors in moderate size Boolean models are often linked to cellular steady states, cell cycles or to phenotypes. However, large-scale or highly interconnected networks as the ones used in Systems Pharmacology converge into a special type of attractor known as complex attractor. Complex attractors consist on set of states in which the system irregularly oscillates (2,3), making its interpretation difficult due to the high number of stable states involved in them. An approach to overcome this problem is to generate the probability that a given node is ON inside the complex attractor.
Results: In order to minimize the effort to implement Boolean models, run simulations and analyze the results, we developed an R framework called SPIDDOR (Systems Pharmacology for effIcient Drug Development On R). One of the new features of the SPIDDOR framework is that it identifies the different steady-states of Boolean networks, known as attractors, in order to describe the long-time behavior of a system. Once an attractor is found, a dynamic perturbation analysis can be performed in order to identify which node knockouts or persistent activations lead to considerable changes in the attractors of the system, and consequently, in the activation probability of the nodes that represent the attractor.
Conclusions: With the tools explained in this methodology, the dynamics of a biological/pharmacological system can be simulated to identify its attractors and therefore understand how perturbations may alter its behavior. 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] Wynn ML, Consul N, Merajver SD, Schnell S. Logic-based models in systems biology: a predictive and parameter-free network analysis method. Integr Biol. 2012;4(11):1323.
[2] M. Hopfensitz, C. Müssel, M. Maucher HAK. Attractors in Boolean Networks – A tutorial. Comput Stat. 2013;28(1):19–36.
[3] Saadatpour A, Albert I, Albert R. Attractor analysis of asynchronous Boolean models of signal transduction networks. J Theor Biol. 2010 Oct 21;266(4):641–56.
Reference: PAGE 25 (2016) Abstr 5936 [www.page-meeting.org/?abstract=5936]
Poster: Methodology - New Modelling Approaches