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Dynamic simulation of regulatory networks using SQUAD.

Di Cara A, Garg A, De Micheli G, Xenarios I, Mendoza L - BMC Bioinformatics (2007)

Bottom Line: Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Swiss Institute of Bioinformatics, Vital-IT Group, Quartier Sorge - Batiment Genopode, CH-1015 Lausanne, Switzerland. alessandro.dicara@merckserono.net

ABSTRACT

Background: 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 activating 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.

Conclusion: 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.

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Perturbations of the Th0 state with IL-4 and IFN-γ. Each curve represents the activity of a node in function of time. (A) Effect of IL-4 (red upward triangle) on the Th0 state (B) effect of IL-4 (red upward triangle) on the Th0 state and effect of IFNγ (yellow upward triangle) on the IL-4 induced Th2 state.
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Figure 4: Perturbations of the Th0 state with IL-4 and IFN-γ. Each curve represents the activity of a node in function of time. (A) Effect of IL-4 (red upward triangle) on the Th0 state (B) effect of IL-4 (red upward triangle) on the Th0 state and effect of IFNγ (yellow upward triangle) on the IL-4 induced Th2 state.

Mentions: We have used the perturbation framework on the T-helper cell network to assess the effects of IL-4 and IFN-γ (Figure 4 and Additional file 4). As shown in the previous section, the addition of IL-4 to the Th0 state moves the network towards the Th2 steady state (Figure 4A). Similarly, using the perturbation protocols we tested the effect of adding an IFN-γ pulse on the Th2 steady state (Figure 4B). Under these circumstances the Th2 state is temporarily perturbed, but returns to the Th2 state, consistent with experimental data showing the stability of Th2 cells [19].


Dynamic simulation of regulatory networks using SQUAD.

Di Cara A, Garg A, De Micheli G, Xenarios I, Mendoza L - BMC Bioinformatics (2007)

Perturbations of the Th0 state with IL-4 and IFN-γ. Each curve represents the activity of a node in function of time. (A) Effect of IL-4 (red upward triangle) on the Th0 state (B) effect of IL-4 (red upward triangle) on the Th0 state and effect of IFNγ (yellow upward triangle) on the IL-4 induced Th2 state.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2238325&req=5

Figure 4: Perturbations of the Th0 state with IL-4 and IFN-γ. Each curve represents the activity of a node in function of time. (A) Effect of IL-4 (red upward triangle) on the Th0 state (B) effect of IL-4 (red upward triangle) on the Th0 state and effect of IFNγ (yellow upward triangle) on the IL-4 induced Th2 state.
Mentions: We have used the perturbation framework on the T-helper cell network to assess the effects of IL-4 and IFN-γ (Figure 4 and Additional file 4). As shown in the previous section, the addition of IL-4 to the Th0 state moves the network towards the Th2 steady state (Figure 4A). Similarly, using the perturbation protocols we tested the effect of adding an IFN-γ pulse on the Th2 steady state (Figure 4B). Under these circumstances the Th2 state is temporarily perturbed, but returns to the Th2 state, consistent with experimental data showing the stability of Th2 cells [19].

Bottom Line: Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Swiss Institute of Bioinformatics, Vital-IT Group, Quartier Sorge - Batiment Genopode, CH-1015 Lausanne, Switzerland. alessandro.dicara@merckserono.net

ABSTRACT

Background: 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 activating 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.

Conclusion: 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.

Show MeSH
Related in: MedlinePlus