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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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Identification of steady states using SQUAD. Graphical description of the identification of steady states using SQUAD. The model for the T-helper cell network (upper left figure) is loaded into SQUAD (upper center figure) using a CellDesigner sbml file. SQUAD displays the steady states identified in the network (upper right figures) through a scrollable list containing the values of the active nodes for each configuration. In addition the steady states can be visualized directly on the network topology (lower figures). For the T-helper network three steady states exist. Based on the molecular fingerprint, each state can be mapped to an existing biological state: State1 with all the nodes inactive corresponds to the Th0 cells. State2 with active IFNγ corresponds to the Th1 cells. State3 with active IL-4 corresponds to the Th2 state.
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Figure 1: Identification of steady states using SQUAD. Graphical description of the identification of steady states using SQUAD. The model for the T-helper cell network (upper left figure) is loaded into SQUAD (upper center figure) using a CellDesigner sbml file. SQUAD displays the steady states identified in the network (upper right figures) through a scrollable list containing the values of the active nodes for each configuration. In addition the steady states can be visualized directly on the network topology (lower figures). For the T-helper network three steady states exist. Based on the molecular fingerprint, each state can be mapped to an existing biological state: State1 with all the nodes inactive corresponds to the Th0 cells. State2 with active IFNγ corresponds to the Th1 cells. State3 with active IL-4 corresponds to the Th2 state.

Mentions: The first step towards modeling signaling networks is to define the components of the network and their connectivity. We symbolize the components of a network through nodes, represented as variables whose values reflect a state of activity. Nodes do not necessarily represent single molecules, but rather functional entities such as molecular complexes. In the T-helper network for example (Figure 1, upper left) the node describing the interferon-γ receptor (IFNg-R) represents a complex of multiple subunits that when active elicits the activation of the nodes downstream. The connectivity among nodes is expressed in terms of "activations" or "inhibitions". Once the topology of the network is established, it can be loaded into SQUAD to perform analyses and simulations.


Dynamic simulation of regulatory networks using SQUAD.

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

Identification of steady states using SQUAD. Graphical description of the identification of steady states using SQUAD. The model for the T-helper cell network (upper left figure) is loaded into SQUAD (upper center figure) using a CellDesigner sbml file. SQUAD displays the steady states identified in the network (upper right figures) through a scrollable list containing the values of the active nodes for each configuration. In addition the steady states can be visualized directly on the network topology (lower figures). For the T-helper network three steady states exist. Based on the molecular fingerprint, each state can be mapped to an existing biological state: State1 with all the nodes inactive corresponds to the Th0 cells. State2 with active IFNγ corresponds to the Th1 cells. State3 with active IL-4 corresponds to the Th2 state.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Identification of steady states using SQUAD. Graphical description of the identification of steady states using SQUAD. The model for the T-helper cell network (upper left figure) is loaded into SQUAD (upper center figure) using a CellDesigner sbml file. SQUAD displays the steady states identified in the network (upper right figures) through a scrollable list containing the values of the active nodes for each configuration. In addition the steady states can be visualized directly on the network topology (lower figures). For the T-helper network three steady states exist. Based on the molecular fingerprint, each state can be mapped to an existing biological state: State1 with all the nodes inactive corresponds to the Th0 cells. State2 with active IFNγ corresponds to the Th1 cells. State3 with active IL-4 corresponds to the Th2 state.
Mentions: The first step towards modeling signaling networks is to define the components of the network and their connectivity. We symbolize the components of a network through nodes, represented as variables whose values reflect a state of activity. Nodes do not necessarily represent single molecules, but rather functional entities such as molecular complexes. In the T-helper network for example (Figure 1, upper left) the node describing the interferon-γ receptor (IFNg-R) represents a complex of multiple subunits that when active elicits the activation of the nodes downstream. The connectivity among nodes is expressed in terms of "activations" or "inhibitions". Once the topology of the network is established, it can be loaded into SQUAD to perform analyses and simulations.

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