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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
The same MIN model as the one used for genetic regulation modeling, enriched with complementary information allowing the translation into differential equations.
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Figure 13: The same MIN model as the one used for genetic regulation modeling, enriched with complementary information allowing the translation into differential equations.

Mentions: Let us consider the example in Figure 13. The MIN model looks very much like the one in Figure 3, but the IRC and ICR are provided with additional properties such as ki, Kaff and production_rate which reflect the kinetic properties of the corresponding biochemical reactions. If the regulatory site "OR1" in Figure 13 is in the state OR1, it means that neither of the two reactions ("CI RNA synthesis" and "CI protein synthesis") take place in the cell. When the same site is in the state OR1·CI, it means that both "CI RNA synthesis" and "CI protein synthesis" take place. Thus, it is possible to reduce this complexity by demultiplicating the regulatory sites as a first step of the translation of a MIN model in ODEs. The demultiplication of a regulatory site R replaces it by a set of (new) species associated to the states of R and a set of (new) regulatory sites associated to the chemical reactions. In other words, every regulatory state of R will now give a chemical species participating in a defined set of chemical reactions, represented by newly generated regulatory sites. After the demultiplication, each regulatory site represents a single chemical reaction, which means that the species connected to it may potentially be produced or consumed, and may be automatically translated to ODEs. Some optimizations may be performed at this stage, for instance, if one knows if the species are consumed or produced, which may be indicated in the attributes (such as "stoichiometry", "production rate", "degradation rate" or "kinetic rate") of the corresponding influences ICRs and IRCs.


Incremental and unifying modelling formalism for biological interaction networks.

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

The same MIN model as the one used for genetic regulation modeling, enriched with complementary information allowing the translation into differential equations.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 13: The same MIN model as the one used for genetic regulation modeling, enriched with complementary information allowing the translation into differential equations.
Mentions: Let us consider the example in Figure 13. The MIN model looks very much like the one in Figure 3, but the IRC and ICR are provided with additional properties such as ki, Kaff and production_rate which reflect the kinetic properties of the corresponding biochemical reactions. If the regulatory site "OR1" in Figure 13 is in the state OR1, it means that neither of the two reactions ("CI RNA synthesis" and "CI protein synthesis") take place in the cell. When the same site is in the state OR1·CI, it means that both "CI RNA synthesis" and "CI protein synthesis" take place. Thus, it is possible to reduce this complexity by demultiplicating the regulatory sites as a first step of the translation of a MIN model in ODEs. The demultiplication of a regulatory site R replaces it by a set of (new) species associated to the states of R and a set of (new) regulatory sites associated to the chemical reactions. In other words, every regulatory state of R will now give a chemical species participating in a defined set of chemical reactions, represented by newly generated regulatory sites. After the demultiplication, each regulatory site represents a single chemical reaction, which means that the species connected to it may potentially be produced or consumed, and may be automatically translated to ODEs. Some optimizations may be performed at this stage, for instance, if one knows if the species are consumed or produced, which may be indicated in the attributes (such as "stoichiometry", "production rate", "degradation rate" or "kinetic rate") of the corresponding influences ICRs and IRCs.

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