Limits...
Threshold-dominated regulation hides genetic variation in gene expression networks.

Gjuvsland AB, Plahte E, Omholt SW - BMC Syst Biol (2007)

Bottom Line: If the parameter perturbation shifts the equilibrium value too far away from threshold, the gene product is no longer an effective regulator and robustness is lost.In the present study all feedback loops are negative, and our results suggest that threshold robustness is maintained by negative feedback which necessarily exists in the homeostatic state.Our results suggest that threshold regulation is a generic phenomenon in feedback-regulated networks with sigmoidal response functions, at least when there is no positive feedback.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Animal Science and Aquaculture, Norwegian University of Life Sciences, 1432 As, Norway. arne.gjuvsland@cigene.no

ABSTRACT

Background: In dynamical models with feedback and sigmoidal response functions, some or all variables have thresholds around which they regulate themselves or other variables. A mathematical analysis has shown that when the dose-response functions approach binary or on/off responses, any variable with an equilibrium value close to one of its thresholds is very robust to parameter perturbations of a homeostatic state. We denote this threshold robustness. To check the empirical relevance of this phenomenon with response function steepnesses ranging from a near on/off response down to Michaelis-Menten conditions, we have performed a simulation study to investigate the degree of threshold robustness in models for a three-gene system with one downstream gene, using several logical input gates, but excluding models with positive feedback to avoid multistationarity. Varying parameter values representing functional genetic variation, we have analysed the coefficient of variation (CV) of the gene product concentrations in the stable state for the regulating genes in absolute terms and compared to the CV for the unregulating downstream gene. The sigmoidal or binary dose-response functions in these models can be considered as phenomenological models of the aggregated effects on protein or mRNA expression rates of all cellular reactions involved in gene expression.

Results: For all the models, threshold robustness increases with increasing response steepness. The CVs of the regulating genes are significantly smaller than for the unregulating gene, in particular for steep responses. The effect becomes less prominent as steepnesses approach Michaelis-Menten conditions. If the parameter perturbation shifts the equilibrium value too far away from threshold, the gene product is no longer an effective regulator and robustness is lost. Threshold robustness arises when a variable is an active regulator around its threshold, and this function is maintained by the feedback loop that the regulator necessarily takes part in and also is regulated by. In the present study all feedback loops are negative, and our results suggest that threshold robustness is maintained by negative feedback which necessarily exists in the homeostatic state.

Conclusion: Threshold robustness of a variable can be seen as its ability to maintain an active regulation around its threshold in a homeostatic state despite external perturbations. The feedback loop that the system necessarily possesses in this state, ensures that the robust variable is itself regulated and kept close to its threshold. Our results suggest that threshold regulation is a generic phenomenon in feedback-regulated networks with sigmoidal response functions, at least when there is no positive feedback. Threshold robustness in gene regulatory networks illustrates that hidden genetic variation can be explained by systemic properties of the genotype-phenotype map.

Show MeSH

Related in: MedlinePlus

Variation in steady state values for Models 1, 9, 12, and 14. Boxplots show the distributions of the 81 coefficients of variance for all three genes using seven different Hill coefficients ranging from p = 1 to p = 100. For each Hill coefficients the three plots show from left to right the coefficient of variation for gene 1 (red), 2 (green), and 3 (blue), respectively. The boxes show the quartiles and the median. The black vertical lines extend to the largest observation and the smallest. The long black horisontal line shows the coefficient of variation 0.288 of the perturbed production rates αj.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2238762&req=5

Figure 4: Variation in steady state values for Models 1, 9, 12, and 14. Boxplots show the distributions of the 81 coefficients of variance for all three genes using seven different Hill coefficients ranging from p = 1 to p = 100. For each Hill coefficients the three plots show from left to right the coefficient of variation for gene 1 (red), 2 (green), and 3 (blue), respectively. The boxes show the quartiles and the median. The black vertical lines extend to the largest observation and the smallest. The long black horisontal line shows the coefficient of variation 0.288 of the perturbed production rates αj.

Mentions: Models 1, 9, 12, and 14 represent four different classes among the 14 models: Model 1 represents models with a negative feedback loop between the two genes plus autoregulation, Model 9 has just a pure negative feedback loop and no autoregulation, Model 12 has interaction but no feedback loop between genes 1 and 2, and Model 14 has no interaction at all between genes 1 and 2 (Fig. 3). The comparison (Fig. 4) of the distributions of CV1k and CV2k over the 81 parameter sets to the distribution of CV3k shows that there is a marked difference between the CVs of the singular variables x1 and x2 and the downstream, regular variable x3. This difference is most marked for high Hill coefficient, but is present even under Michaelis-Menten conditions. While in almost all cases the variation in is larger than CVuni, the variations in and are considerably smaller for most parameter sets, in Model 14 for all parameter sets. The result for Model 14 is not surprising, as it is well known that negative autoregulation leads to a high degree of robustness [30,33,34].


Threshold-dominated regulation hides genetic variation in gene expression networks.

Gjuvsland AB, Plahte E, Omholt SW - BMC Syst Biol (2007)

Variation in steady state values for Models 1, 9, 12, and 14. Boxplots show the distributions of the 81 coefficients of variance for all three genes using seven different Hill coefficients ranging from p = 1 to p = 100. For each Hill coefficients the three plots show from left to right the coefficient of variation for gene 1 (red), 2 (green), and 3 (blue), respectively. The boxes show the quartiles and the median. The black vertical lines extend to the largest observation and the smallest. The long black horisontal line shows the coefficient of variation 0.288 of the perturbed production rates αj.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2238762&req=5

Figure 4: Variation in steady state values for Models 1, 9, 12, and 14. Boxplots show the distributions of the 81 coefficients of variance for all three genes using seven different Hill coefficients ranging from p = 1 to p = 100. For each Hill coefficients the three plots show from left to right the coefficient of variation for gene 1 (red), 2 (green), and 3 (blue), respectively. The boxes show the quartiles and the median. The black vertical lines extend to the largest observation and the smallest. The long black horisontal line shows the coefficient of variation 0.288 of the perturbed production rates αj.
Mentions: Models 1, 9, 12, and 14 represent four different classes among the 14 models: Model 1 represents models with a negative feedback loop between the two genes plus autoregulation, Model 9 has just a pure negative feedback loop and no autoregulation, Model 12 has interaction but no feedback loop between genes 1 and 2, and Model 14 has no interaction at all between genes 1 and 2 (Fig. 3). The comparison (Fig. 4) of the distributions of CV1k and CV2k over the 81 parameter sets to the distribution of CV3k shows that there is a marked difference between the CVs of the singular variables x1 and x2 and the downstream, regular variable x3. This difference is most marked for high Hill coefficient, but is present even under Michaelis-Menten conditions. While in almost all cases the variation in is larger than CVuni, the variations in and are considerably smaller for most parameter sets, in Model 14 for all parameter sets. The result for Model 14 is not surprising, as it is well known that negative autoregulation leads to a high degree of robustness [30,33,34].

Bottom Line: If the parameter perturbation shifts the equilibrium value too far away from threshold, the gene product is no longer an effective regulator and robustness is lost.In the present study all feedback loops are negative, and our results suggest that threshold robustness is maintained by negative feedback which necessarily exists in the homeostatic state.Our results suggest that threshold regulation is a generic phenomenon in feedback-regulated networks with sigmoidal response functions, at least when there is no positive feedback.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Animal Science and Aquaculture, Norwegian University of Life Sciences, 1432 As, Norway. arne.gjuvsland@cigene.no

ABSTRACT

Background: In dynamical models with feedback and sigmoidal response functions, some or all variables have thresholds around which they regulate themselves or other variables. A mathematical analysis has shown that when the dose-response functions approach binary or on/off responses, any variable with an equilibrium value close to one of its thresholds is very robust to parameter perturbations of a homeostatic state. We denote this threshold robustness. To check the empirical relevance of this phenomenon with response function steepnesses ranging from a near on/off response down to Michaelis-Menten conditions, we have performed a simulation study to investigate the degree of threshold robustness in models for a three-gene system with one downstream gene, using several logical input gates, but excluding models with positive feedback to avoid multistationarity. Varying parameter values representing functional genetic variation, we have analysed the coefficient of variation (CV) of the gene product concentrations in the stable state for the regulating genes in absolute terms and compared to the CV for the unregulating downstream gene. The sigmoidal or binary dose-response functions in these models can be considered as phenomenological models of the aggregated effects on protein or mRNA expression rates of all cellular reactions involved in gene expression.

Results: For all the models, threshold robustness increases with increasing response steepness. The CVs of the regulating genes are significantly smaller than for the unregulating gene, in particular for steep responses. The effect becomes less prominent as steepnesses approach Michaelis-Menten conditions. If the parameter perturbation shifts the equilibrium value too far away from threshold, the gene product is no longer an effective regulator and robustness is lost. Threshold robustness arises when a variable is an active regulator around its threshold, and this function is maintained by the feedback loop that the regulator necessarily takes part in and also is regulated by. In the present study all feedback loops are negative, and our results suggest that threshold robustness is maintained by negative feedback which necessarily exists in the homeostatic state.

Conclusion: Threshold robustness of a variable can be seen as its ability to maintain an active regulation around its threshold in a homeostatic state despite external perturbations. The feedback loop that the system necessarily possesses in this state, ensures that the robust variable is itself regulated and kept close to its threshold. Our results suggest that threshold regulation is a generic phenomenon in feedback-regulated networks with sigmoidal response functions, at least when there is no positive feedback. Threshold robustness in gene regulatory networks illustrates that hidden genetic variation can be explained by systemic properties of the genotype-phenotype map.

Show MeSH
Related in: MedlinePlus