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Incremental and unifying modelling formalism for biological interaction networks.

Yartseva A, Klaudel H, Devillers R, Képès F - BMC Bioinformatics (2007)

Bottom Line: We also show how to extract from our model a classical ODE description of the dynamics of a system.This approach provides an additional level of description between the biological and mathematical ones.It yields, on the one hand, a knowledge expression in a form which is intuitive for biologists and, on the other hand, its representation in a formal and structured way.

View Article: PubMed Central - HTML - PubMed

Affiliation: IBISC - Université d'Evry Val d'Essonne, Tour Evry 2, 523 place des Terrasses de l'Agora, F-91000 Evry, France. iartseva@gmail.com

ABSTRACT

Background: An appropriate choice of the modeling formalism from the broad range of existing ones may be crucial for efficiently describing and analyzing biological systems.

Results: We propose a new unifying and incremental formalism for the representation and modeling of biological interaction networks. This formalism allows automated translations into other formalisms, thus enabling a thorough study of the dynamic properties of a biological system. As a first illustration, we propose a translation into the R. Thomas' multivalued logical formalism which provides a possible semantics; a methodology for constructing such models is presented on a classical benchmark: the lambda phage genetic switch. We also show how to extract from our model a classical ODE description of the dynamics of a system.

Conclusion: This approach provides an additional level of description between the biological and mathematical ones. It yields, on the one hand, a knowledge expression in a form which is intuitive for biologists and, on the other hand, its representation in a formal and structured way.

Show MeSH
A small interaction network representing the chemical species CI and the (regulatory) site named OR1. Left. The influence ICR links the affinity labeled OR of species CI with the site OR1, and the influence IRC links the site OR1 and the species CI. In the λ switch, the regulatory site OR1 corresponds to the regulatory region in the DNA molecule coding for the protein CI. Thus, CI can influence the regulatory site OR1, and the activity of CI can be regulated through the regulatory site OR1. Right. The corresponding relation  indicating the biologically observed states of the network.
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Figure 3: A small interaction network representing the chemical species CI and the (regulatory) site named OR1. Left. The influence ICR links the affinity labeled OR of species CI with the site OR1, and the influence IRC links the site OR1 and the species CI. In the λ switch, the regulatory site OR1 corresponds to the regulatory region in the DNA molecule coding for the protein CI. Thus, CI can influence the regulatory site OR1, and the activity of CI can be regulated through the regulatory site OR1. Right. The corresponding relation indicating the biologically observed states of the network.

Mentions: An influence has a set of attributes, which should describe, in particular, the relationship between the values of the species and those of the regulatory site, like the parameters of the corresponding chemical reaction: kinetic rate or speed, or stoichiometric coefficients. Several examples of the IRCs and ICRs are shown on the Figure 3, by dashed and plain arcs, respectively.


Incremental and unifying modelling formalism for biological interaction networks.

Yartseva A, Klaudel H, Devillers R, Képès F - BMC Bioinformatics (2007)

A small interaction network representing the chemical species CI and the (regulatory) site named OR1. Left. The influence ICR links the affinity labeled OR of species CI with the site OR1, and the influence IRC links the site OR1 and the species CI. In the λ switch, the regulatory site OR1 corresponds to the regulatory region in the DNA molecule coding for the protein CI. Thus, CI can influence the regulatory site OR1, and the activity of CI can be regulated through the regulatory site OR1. Right. The corresponding relation  indicating the biologically observed states of the network.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2200675&req=5

Figure 3: A small interaction network representing the chemical species CI and the (regulatory) site named OR1. Left. The influence ICR links the affinity labeled OR of species CI with the site OR1, and the influence IRC links the site OR1 and the species CI. In the λ switch, the regulatory site OR1 corresponds to the regulatory region in the DNA molecule coding for the protein CI. Thus, CI can influence the regulatory site OR1, and the activity of CI can be regulated through the regulatory site OR1. Right. The corresponding relation indicating the biologically observed states of the network.
Mentions: An influence has a set of attributes, which should describe, in particular, the relationship between the values of the species and those of the regulatory site, like the parameters of the corresponding chemical reaction: kinetic rate or speed, or stoichiometric coefficients. Several examples of the IRCs and ICRs are shown on the Figure 3, by dashed and plain arcs, respectively.

Bottom Line: We also show how to extract from our model a classical ODE description of the dynamics of a system.This approach provides an additional level of description between the biological and mathematical ones.It yields, on the one hand, a knowledge expression in a form which is intuitive for biologists and, on the other hand, its representation in a formal and structured way.

View Article: PubMed Central - HTML - PubMed

Affiliation: IBISC - Université d'Evry Val d'Essonne, Tour Evry 2, 523 place des Terrasses de l'Agora, F-91000 Evry, France. iartseva@gmail.com

ABSTRACT

Background: An appropriate choice of the modeling formalism from the broad range of existing ones may be crucial for efficiently describing and analyzing biological systems.

Results: We propose a new unifying and incremental formalism for the representation and modeling of biological interaction networks. This formalism allows automated translations into other formalisms, thus enabling a thorough study of the dynamic properties of a biological system. As a first illustration, we propose a translation into the R. Thomas' multivalued logical formalism which provides a possible semantics; a methodology for constructing such models is presented on a classical benchmark: the lambda phage genetic switch. We also show how to extract from our model a classical ODE description of the dynamics of a system.

Conclusion: This approach provides an additional level of description between the biological and mathematical ones. It yields, on the one hand, a knowledge expression in a form which is intuitive for biologists and, on the other hand, its representation in a formal and structured way.

Show MeSH