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The role of noise and positive feedback in the onset of autosomal dominant diseases.

Bosl WJ, Li R - BMC Syst Biol (2010)

Bottom Line: Autosomal dominant (AD) diseases result when a single mutant or non-functioning gene is present on an autosomal chromosome.These diseases often do not emerge at birth.Model pathways for two autosomal dominant diseases, AD polycystic kidney disease and mature onset diabetes of youth (MODY) were simulated and the results are compared to known disease characteristics.

View Article: PubMed Central - HTML - PubMed

Affiliation: Harvard Medical School, Boston, MA 02115, USA. william.bosl@childrens.harvard.edu

ABSTRACT

Background: Autosomal dominant (AD) diseases result when a single mutant or non-functioning gene is present on an autosomal chromosome. These diseases often do not emerge at birth. There are presently two prevailing theories explaining the expression of AD diseases. One explanation originates from the Knudson two-hit theory of hereditary cancers, where loss of heterozygosity or occurrence of somatic mutations impairs the function of the wild-type copy. While these somatic second hits may be sufficient for stable disease states, it is often difficult to determine if their occurrence necessarily marks the initiation of disease progression. A more direct consequence of a heterozygous genetic background is haploinsufficiency, referring to a lack of sufficient gene function due to reduced wild-type gene copy number; however, haploinsufficiency can involve a variety of additional mechanisms, such as noise in gene expression or protein levels, injury and second hit mutations in other genes. In this study, we explore the possible contribution to the onset of autosomal dominant diseases from intrinsic factors, such as those determined by the structure of the molecular networks governing normal cellular physiology.

Results: First, simple models of single gene insufficiency using the positive feedback loops that may be derived from a three-component network were studied by computer simulation using Bionet software. The network structure is shown to affect the dynamics considerably; some networks are relatively stable even when large stochastic variations in are present, while others exhibit switch-like dynamics. In the latter cases, once the network switches over to the disease state it remains in that state permanently. Model pathways for two autosomal dominant diseases, AD polycystic kidney disease and mature onset diabetes of youth (MODY) were simulated and the results are compared to known disease characteristics.

Conclusions: By identifying the intrinsic mechanisms involved in the onset of AD diseases, it may be possible to better assess risk factors as well as lead to potential new drug targets. To illustrate the applicability of this study of pathway dynamics, we simulated the primary pathways involved in two autosomal dominant diseases, Polycystic Kidney Disease (PKD) and mature onset diabetes of youth (MODY). Simulations demonstrate that some of the primary disease characteristics are consistent with the positive feedback-stochastic variation theory presented here. This has implications for new drug targets to control these diseases by blocking the positive feedback loop in the relevant pathways.

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Effect of Gaussian noise added to production rate on product concentration. Gaussian noise added to the production rate causes changes in the concentration of the protein. The standard deviation of the noise, σ, and the size of the time step over which the rate is changing, Δ, both affect the size of the concentration variations as shown here.
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Figure 4: Effect of Gaussian noise added to production rate on product concentration. Gaussian noise added to the production rate causes changes in the concentration of the protein. The standard deviation of the noise, σ, and the size of the time step over which the rate is changing, Δ, both affect the size of the concentration variations as shown here.

Mentions: The variation in protein concentration due to adding a Gaussian noise term to the production rate is illustrated in Figure 4. The curves are offset for visual illustration. Each curve represents a protein concentration that is maintained by a constant decay rate and a production rate that consists of a constant plus a Gaussian random noise term. The noise in each of these cases has a standard deviation of either 1.0 or 0.2. Since the concentration is affected by how long the production rate term deviates from its average value, the time step over which the random variation is changing also affects the concentration variability. It is clear in Figure 4 that a longer time for production rate deviation results in larger variability in product concentration.


The role of noise and positive feedback in the onset of autosomal dominant diseases.

Bosl WJ, Li R - BMC Syst Biol (2010)

Effect of Gaussian noise added to production rate on product concentration. Gaussian noise added to the production rate causes changes in the concentration of the protein. The standard deviation of the noise, σ, and the size of the time step over which the rate is changing, Δ, both affect the size of the concentration variations as shown here.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2902440&req=5

Figure 4: Effect of Gaussian noise added to production rate on product concentration. Gaussian noise added to the production rate causes changes in the concentration of the protein. The standard deviation of the noise, σ, and the size of the time step over which the rate is changing, Δ, both affect the size of the concentration variations as shown here.
Mentions: The variation in protein concentration due to adding a Gaussian noise term to the production rate is illustrated in Figure 4. The curves are offset for visual illustration. Each curve represents a protein concentration that is maintained by a constant decay rate and a production rate that consists of a constant plus a Gaussian random noise term. The noise in each of these cases has a standard deviation of either 1.0 or 0.2. Since the concentration is affected by how long the production rate term deviates from its average value, the time step over which the random variation is changing also affects the concentration variability. It is clear in Figure 4 that a longer time for production rate deviation results in larger variability in product concentration.

Bottom Line: Autosomal dominant (AD) diseases result when a single mutant or non-functioning gene is present on an autosomal chromosome.These diseases often do not emerge at birth.Model pathways for two autosomal dominant diseases, AD polycystic kidney disease and mature onset diabetes of youth (MODY) were simulated and the results are compared to known disease characteristics.

View Article: PubMed Central - HTML - PubMed

Affiliation: Harvard Medical School, Boston, MA 02115, USA. william.bosl@childrens.harvard.edu

ABSTRACT

Background: Autosomal dominant (AD) diseases result when a single mutant or non-functioning gene is present on an autosomal chromosome. These diseases often do not emerge at birth. There are presently two prevailing theories explaining the expression of AD diseases. One explanation originates from the Knudson two-hit theory of hereditary cancers, where loss of heterozygosity or occurrence of somatic mutations impairs the function of the wild-type copy. While these somatic second hits may be sufficient for stable disease states, it is often difficult to determine if their occurrence necessarily marks the initiation of disease progression. A more direct consequence of a heterozygous genetic background is haploinsufficiency, referring to a lack of sufficient gene function due to reduced wild-type gene copy number; however, haploinsufficiency can involve a variety of additional mechanisms, such as noise in gene expression or protein levels, injury and second hit mutations in other genes. In this study, we explore the possible contribution to the onset of autosomal dominant diseases from intrinsic factors, such as those determined by the structure of the molecular networks governing normal cellular physiology.

Results: First, simple models of single gene insufficiency using the positive feedback loops that may be derived from a three-component network were studied by computer simulation using Bionet software. The network structure is shown to affect the dynamics considerably; some networks are relatively stable even when large stochastic variations in are present, while others exhibit switch-like dynamics. In the latter cases, once the network switches over to the disease state it remains in that state permanently. Model pathways for two autosomal dominant diseases, AD polycystic kidney disease and mature onset diabetes of youth (MODY) were simulated and the results are compared to known disease characteristics.

Conclusions: By identifying the intrinsic mechanisms involved in the onset of AD diseases, it may be possible to better assess risk factors as well as lead to potential new drug targets. To illustrate the applicability of this study of pathway dynamics, we simulated the primary pathways involved in two autosomal dominant diseases, Polycystic Kidney Disease (PKD) and mature onset diabetes of youth (MODY). Simulations demonstrate that some of the primary disease characteristics are consistent with the positive feedback-stochastic variation theory presented here. This has implications for new drug targets to control these diseases by blocking the positive feedback loop in the relevant pathways.

Show MeSH
Related in: MedlinePlus