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Shape, size, and robustness: feasible regions in the parameter space of biochemical networks.

Dayarian A, Chaves M, Sontag ED, Sengupta AM - PLoS Comput. Biol. (2009)

Bottom Line: One measure of robustness has been associated with the volume of the feasible region, namely, the region in the parameter space in which the system is functional.In particular, we found that, between two alternative ways of activating Wingless, one is more robust than the other.As a general modeling strategy, our approach is an interesting alternative to Boolean representation of biochemical networks.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey, United States of America.

ABSTRACT
The concept of robustness of regulatory networks has received much attention in the last decade. One measure of robustness has been associated with the volume of the feasible region, namely, the region in the parameter space in which the system is functional. In this paper, we show that, in addition to volume, the geometry of this region has important consequences for the robustness and the fragility of a network. We develop an approximation within which we could algebraically specify the feasible region. We analyze the segment polarity gene network to illustrate our approach. The study of random walks in the parameter space and how they exit the feasible region provide us with a rich perspective on the different modes of failure of this network model. In particular, we found that, between two alternative ways of activating Wingless, one is more robust than the other. Our method provides a more complete measure of robustness to parameter variation. As a general modeling strategy, our approach is an interesting alternative to Boolean representation of biochemical networks.

Show MeSH
Segment polarity regulatory network including sloppy-paired protein.In this model, the possibility of Wg autoactivation and en repression by CN is not included.
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pcbi-1000256-g002: Segment polarity regulatory network including sloppy-paired protein.In this model, the possibility of Wg autoactivation and en repression by CN is not included.

Mentions: The regulatory network used by von Dassow et al. [3]. This network is shown in Figure 2B. We will refer to this case as von Dassow et al. model.


Shape, size, and robustness: feasible regions in the parameter space of biochemical networks.

Dayarian A, Chaves M, Sontag ED, Sengupta AM - PLoS Comput. Biol. (2009)

Segment polarity regulatory network including sloppy-paired protein.In this model, the possibility of Wg autoactivation and en repression by CN is not included.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2599888&req=5

pcbi-1000256-g002: Segment polarity regulatory network including sloppy-paired protein.In this model, the possibility of Wg autoactivation and en repression by CN is not included.
Mentions: The regulatory network used by von Dassow et al. [3]. This network is shown in Figure 2B. We will refer to this case as von Dassow et al. model.

Bottom Line: One measure of robustness has been associated with the volume of the feasible region, namely, the region in the parameter space in which the system is functional.In particular, we found that, between two alternative ways of activating Wingless, one is more robust than the other.As a general modeling strategy, our approach is an interesting alternative to Boolean representation of biochemical networks.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey, United States of America.

ABSTRACT
The concept of robustness of regulatory networks has received much attention in the last decade. One measure of robustness has been associated with the volume of the feasible region, namely, the region in the parameter space in which the system is functional. In this paper, we show that, in addition to volume, the geometry of this region has important consequences for the robustness and the fragility of a network. We develop an approximation within which we could algebraically specify the feasible region. We analyze the segment polarity gene network to illustrate our approach. The study of random walks in the parameter space and how they exit the feasible region provide us with a rich perspective on the different modes of failure of this network model. In particular, we found that, between two alternative ways of activating Wingless, one is more robust than the other. Our method provides a more complete measure of robustness to parameter variation. As a general modeling strategy, our approach is an interesting alternative to Boolean representation of biochemical networks.

Show MeSH