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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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Dynamic simulation of the T-helper regulatory network, in the presence of IL-4. The plot provided by SQUAD displays the behavior of each component of the network according to time. The values range between 0 (inactive) and 1 (active). Time 0 indicates the time of addition of IL-4 to the network in the Th0 state. In addition to the plot display, SQUAD provides a dynamic network display (top panel) in which the nodes are colored in real-time according to the activation level from white (completely inactive) to black (completely active). The three top images are snapshots of the network at different time points. At Time 0 the network is in the Th0 and and a pulse of IL-4 is added to the system. The pulse originates a transitory state of activation (Time 2.6 is shown), which eventually leads the system to steady state representing the Th2 state (Time 6 and onwards). Time is expressed in arbitrary units.
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Figure 2: Dynamic simulation of the T-helper regulatory network, in the presence of IL-4. The plot provided by SQUAD displays the behavior of each component of the network according to time. The values range between 0 (inactive) and 1 (active). Time 0 indicates the time of addition of IL-4 to the network in the Th0 state. In addition to the plot display, SQUAD provides a dynamic network display (top panel) in which the nodes are colored in real-time according to the activation level from white (completely inactive) to black (completely active). The three top images are snapshots of the network at different time points. At Time 0 the network is in the Th0 and and a pulse of IL-4 is added to the system. The pulse originates a transitory state of activation (Time 2.6 is shown), which eventually leads the system to steady state representing the Th2 state (Time 6 and onwards). Time is expressed in arbitrary units.

Mentions: SQUAD provides a number of graphical utilities to perform simulations. In the steady state selector panel it is possible to choose the starting state from the list of stable steady states automatically found by the system. The selected state can then be further modified to simulate alternative initial states, allowing for the inclusion of perturbations. The results of the simulations are shown by a plot of the activity of each node against time (Figure 2). Importantly, since the equations used for the dynamic simulation are not fitted with experimentally determined kinetic values, the time is expressed in arbitrary units.


Dynamic simulation of regulatory networks using SQUAD.

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

Dynamic simulation of the T-helper regulatory network, in the presence of IL-4. The plot provided by SQUAD displays the behavior of each component of the network according to time. The values range between 0 (inactive) and 1 (active). Time 0 indicates the time of addition of IL-4 to the network in the Th0 state. In addition to the plot display, SQUAD provides a dynamic network display (top panel) in which the nodes are colored in real-time according to the activation level from white (completely inactive) to black (completely active). The three top images are snapshots of the network at different time points. At Time 0 the network is in the Th0 and and a pulse of IL-4 is added to the system. The pulse originates a transitory state of activation (Time 2.6 is shown), which eventually leads the system to steady state representing the Th2 state (Time 6 and onwards). Time is expressed in arbitrary units.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Dynamic simulation of the T-helper regulatory network, in the presence of IL-4. The plot provided by SQUAD displays the behavior of each component of the network according to time. The values range between 0 (inactive) and 1 (active). Time 0 indicates the time of addition of IL-4 to the network in the Th0 state. In addition to the plot display, SQUAD provides a dynamic network display (top panel) in which the nodes are colored in real-time according to the activation level from white (completely inactive) to black (completely active). The three top images are snapshots of the network at different time points. At Time 0 the network is in the Th0 and and a pulse of IL-4 is added to the system. The pulse originates a transitory state of activation (Time 2.6 is shown), which eventually leads the system to steady state representing the Th2 state (Time 6 and onwards). Time is expressed in arbitrary units.
Mentions: SQUAD provides a number of graphical utilities to perform simulations. In the steady state selector panel it is possible to choose the starting state from the list of stable steady states automatically found by the system. The selected state can then be further modified to simulate alternative initial states, allowing for the inclusion of perturbations. The results of the simulations are shown by a plot of the activity of each node against time (Figure 2). Importantly, since the equations used for the dynamic simulation are not fitted with experimentally determined kinetic values, the time is expressed in arbitrary units.

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