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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
Translation of dynamic parameters from  to MLM. Left For the small network, represented on the Figure 3, the interspecies regulation relation ΨCI,CI is constructed. Right The obtained translated regulatory graph and its labels (θ, ε) with corresponding threshold pairs (shown in bold for positive pairs and in italic for negative ones in bottom tables). Bottom Ordering the CI values as absent ≺CI low ≺CI high enables to produce several fully ordered subset of ΨCI,CI.
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Figure 9: Translation of dynamic parameters from to MLM. Left For the small network, represented on the Figure 3, the interspecies regulation relation ΨCI,CI is constructed. Right The obtained translated regulatory graph and its labels (θ, ε) with corresponding threshold pairs (shown in bold for positive pairs and in italic for negative ones in bottom tables). Bottom Ordering the CI values as absent ≺CI low ≺CI high enables to produce several fully ordered subset of ΨCI,CI.

Mentions: The Figure 9 illustrates the dynamic parameters translation from MIN model which is presented in Figure 3.


Incremental and unifying modelling formalism for biological interaction networks.

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

Translation of dynamic parameters from  to MLM. Left For the small network, represented on the Figure 3, the interspecies regulation relation ΨCI,CI is constructed. Right The obtained translated regulatory graph and its labels (θ, ε) with corresponding threshold pairs (shown in bold for positive pairs and in italic for negative ones in bottom tables). Bottom Ordering the CI values as absent ≺CI low ≺CI high enables to produce several fully ordered subset of ΨCI,CI.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: Translation of dynamic parameters from to MLM. Left For the small network, represented on the Figure 3, the interspecies regulation relation ΨCI,CI is constructed. Right The obtained translated regulatory graph and its labels (θ, ε) with corresponding threshold pairs (shown in bold for positive pairs and in italic for negative ones in bottom tables). Bottom Ordering the CI values as absent ≺CI low ≺CI high enables to produce several fully ordered subset of ΨCI,CI.
Mentions: The Figure 9 illustrates the dynamic parameters translation from MIN model which is presented in Figure 3.

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