000114498 001__ 114498
000114498 005__ 20190324125635.0
000114498 0247_ $$2doi$$a10.1186/1471-2105-8-462
000114498 02470 $$2DAR$$a12092
000114498 02470 $$2ISI$$a000252972700001
000114498 037__ $$aARTICLE
000114498 245__ $$aDynamic simulation of regulatory networks using SQUAD
000114498 269__ $$a2007
000114498 260__ $$c2007
000114498 336__ $$aJournal Articles
000114498 520__ $$aBackground The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. Results We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by eliciting receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. Conclusions The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.
000114498 700__ $$aDi Cara, Alessandro
000114498 700__ $$aGarg, Abhishek
000114498 700__ $$0240269$$g167918$$aDe Micheli, Giovanni
000114498 700__ $$aXenarios, Ioannis
000114498 700__ $$aMendoza, Luis
000114498 773__ $$tBMC Bioinformatics$$j8$$q462
000114498 8564_ $$uhttps://infoscience.epfl.ch/record/114498/files/1471-2105-8-462.pdf$$zn/a$$s1274097
000114498 909C0 $$xU11140$$0252283$$pLSI1
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000114498 937__ $$aEPFL-ARTICLE-114498
000114498 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000114498 980__ $$aARTICLE