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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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Screenshots of the perturbation protocol designer tool. This tool is used to create and edit perturbation protocols. The protocol contains two perturbations. For each perturbation an initial state is described. Each perturbation has one ore more "action" elements, which specify the types of perturbations to be performed. More complex protocols can include other types of actions such as perturbations over a time range or node knockouts. (A) Tree view: contains wizard buttons to add, edit and delete parts of the protocol. (B) Text view: provides a textual representation of the protocol file. Once generated, the protocol file can be saved and re-used.
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Figure 3: Screenshots of the perturbation protocol designer tool. This tool is used to create and edit perturbation protocols. The protocol contains two perturbations. For each perturbation an initial state is described. Each perturbation has one ore more "action" elements, which specify the types of perturbations to be performed. More complex protocols can include other types of actions such as perturbations over a time range or node knockouts. (A) Tree view: contains wizard buttons to add, edit and delete parts of the protocol. (B) Text view: provides a textual representation of the protocol file. Once generated, the protocol file can be saved and re-used.

Mentions: The perturbations to be performed are listed within a protocol file written in a dedicated XML format (see Additional file 4, with extension prt). The file describes a set of initial network states and a set of perturbations. Each perturbation corresponds to a separate experiment containing an initial state and a set of actions specifying the node(s) to perturb, the perturbation value(s) as well as the duration and timing of the perturbations. Different types of actions can be specified. For example the singlepulse action modifies the node at a single time point, while rangepulse maintains the perturbation for a determined time period. The protocol file can be created within SQUAD using a wizard tool, which ensures that the protocol file has a valid format (Figure 3). Using these protocols it is possible to reproduce existing biological experiments computationally, or to test new experimental designs. Furthermore, having a file format to specify dynamic simulations allows for the storing, exchanging and comparisons of protocols.


Dynamic simulation of regulatory networks using SQUAD.

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

Screenshots of the perturbation protocol designer tool. This tool is used to create and edit perturbation protocols. The protocol contains two perturbations. For each perturbation an initial state is described. Each perturbation has one ore more "action" elements, which specify the types of perturbations to be performed. More complex protocols can include other types of actions such as perturbations over a time range or node knockouts. (A) Tree view: contains wizard buttons to add, edit and delete parts of the protocol. (B) Text view: provides a textual representation of the protocol file. Once generated, the protocol file can be saved and re-used.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Screenshots of the perturbation protocol designer tool. This tool is used to create and edit perturbation protocols. The protocol contains two perturbations. For each perturbation an initial state is described. Each perturbation has one ore more "action" elements, which specify the types of perturbations to be performed. More complex protocols can include other types of actions such as perturbations over a time range or node knockouts. (A) Tree view: contains wizard buttons to add, edit and delete parts of the protocol. (B) Text view: provides a textual representation of the protocol file. Once generated, the protocol file can be saved and re-used.
Mentions: The perturbations to be performed are listed within a protocol file written in a dedicated XML format (see Additional file 4, with extension prt). The file describes a set of initial network states and a set of perturbations. Each perturbation corresponds to a separate experiment containing an initial state and a set of actions specifying the node(s) to perturb, the perturbation value(s) as well as the duration and timing of the perturbations. Different types of actions can be specified. For example the singlepulse action modifies the node at a single time point, while rangepulse maintains the perturbation for a determined time period. The protocol file can be created within SQUAD using a wizard tool, which ensures that the protocol file has a valid format (Figure 3). Using these protocols it is possible to reproduce existing biological experiments computationally, or to test new experimental designs. Furthermore, having a file format to specify dynamic simulations allows for the storing, exchanging and comparisons of protocols.

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