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Dynamical pathway analysis.

Xiong H, Choe Y - BMC Syst Biol (2008)

Bottom Line: However, to date, no such method exists for elucidating and quantifying the differential dynamical behaviour of genetic regulatory networks, which can have greater impact on phenotypes than individual genes.We developed mathematical methods to study the stability, the controllability, and the steady-state behaviour, as well as the transient responses of biological networks under different environmental perturbations.Therefore differential dynamical properties can be a valuable tool in biomedical research.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Texas A&M University, College Station, TX 77843, USA. hxiong@cs.tamu.edu

ABSTRACT

Background: Although a great deal is known about one gene or protein and its functions under different environmental conditions, little information is available about the complex behaviour of biological networks subject to different environmental perturbations. Observing differential expressions of one or more genes between normal and abnormal cells has been a mainstream method of discovering pertinent genes in diseases and therefore valuable drug targets. However, to date, no such method exists for elucidating and quantifying the differential dynamical behaviour of genetic regulatory networks, which can have greater impact on phenotypes than individual genes.

Results: We propose to redress the deficiency by formulating the functional study of biological networks as a control problem of dynamical systems. We developed mathematical methods to study the stability, the controllability, and the steady-state behaviour, as well as the transient responses of biological networks under different environmental perturbations. We applied our framework to three real-world datasets: the SOS DNA repair network in E. coli under different dosages of radiation, the GSH redox cycle in mice lung exposed to either poisonous air or normal air, and the MAPK pathway in mammalian cell lines exposed to three types of HIV type I Vpr, a wild type and two mutant types; and we found that the three genetic networks exhibited fundamentally different dynamical properties in normal and abnormal cells.

Conclusion: Difference in stability, relative stability, degrees of controllability, and transient responses between normal and abnormal cells means considerable difference in dynamical behaviours and different functioning of cells. Therefore differential dynamical properties can be a valuable tool in biomedical research.

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Estimated expression levels and error in estimation. For each gene of the SOS system, we have superimposed the estimated expression levels on measured expression levels and plotted the error in estimation in a separate panel. We have done this for the low radiation level data set in Figure 4. The estimations generally show good behaviors. Figs. 4a, 4b, 4c, 4d, 4e, 4f, 4g are for the low radiation level data set, and plotted genes lexA, polB, umuD, uvrD, uvrA, uvrY, and ruvA, respectively. Each gene has two plots; the bottom panel shows estimated expression level superimposed on measured expression level, while the top panel is the estimation error.
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Figure 4: Estimated expression levels and error in estimation. For each gene of the SOS system, we have superimposed the estimated expression levels on measured expression levels and plotted the error in estimation in a separate panel. We have done this for the low radiation level data set in Figure 4. The estimations generally show good behaviors. Figs. 4a, 4b, 4c, 4d, 4e, 4f, 4g are for the low radiation level data set, and plotted genes lexA, polB, umuD, uvrD, uvrA, uvrY, and ruvA, respectively. Each gene has two plots; the bottom panel shows estimated expression level superimposed on measured expression level, while the top panel is the estimation error.

Mentions: For the SOS system, x2 is the discretized first derivative of x1, whereas x1 is the expression level of gene lexA, x3 gene polB, x4 gene umuD, x5 gene uvrD, x6 gene uvrA, x7 gene uvrY, and x8 gene ruvA. The outputs are the measured expression levels of the seven genes listed above, and the input is gene recA. In Fig. 4 and Fig. 5, we included the estimated outputs and the measured outputs superimposed into one plot, as well as estimation errors in a separate panel for each gene. From the plots we can see that the estimated trajectory largely follows measured values. The estimated system parameters are listed below for the low level of radiation:


Dynamical pathway analysis.

Xiong H, Choe Y - BMC Syst Biol (2008)

Estimated expression levels and error in estimation. For each gene of the SOS system, we have superimposed the estimated expression levels on measured expression levels and plotted the error in estimation in a separate panel. We have done this for the low radiation level data set in Figure 4. The estimations generally show good behaviors. Figs. 4a, 4b, 4c, 4d, 4e, 4f, 4g are for the low radiation level data set, and plotted genes lexA, polB, umuD, uvrD, uvrA, uvrY, and ruvA, respectively. Each gene has two plots; the bottom panel shows estimated expression level superimposed on measured expression level, while the top panel is the estimation error.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2268661&req=5

Figure 4: Estimated expression levels and error in estimation. For each gene of the SOS system, we have superimposed the estimated expression levels on measured expression levels and plotted the error in estimation in a separate panel. We have done this for the low radiation level data set in Figure 4. The estimations generally show good behaviors. Figs. 4a, 4b, 4c, 4d, 4e, 4f, 4g are for the low radiation level data set, and plotted genes lexA, polB, umuD, uvrD, uvrA, uvrY, and ruvA, respectively. Each gene has two plots; the bottom panel shows estimated expression level superimposed on measured expression level, while the top panel is the estimation error.
Mentions: For the SOS system, x2 is the discretized first derivative of x1, whereas x1 is the expression level of gene lexA, x3 gene polB, x4 gene umuD, x5 gene uvrD, x6 gene uvrA, x7 gene uvrY, and x8 gene ruvA. The outputs are the measured expression levels of the seven genes listed above, and the input is gene recA. In Fig. 4 and Fig. 5, we included the estimated outputs and the measured outputs superimposed into one plot, as well as estimation errors in a separate panel for each gene. From the plots we can see that the estimated trajectory largely follows measured values. The estimated system parameters are listed below for the low level of radiation:

Bottom Line: However, to date, no such method exists for elucidating and quantifying the differential dynamical behaviour of genetic regulatory networks, which can have greater impact on phenotypes than individual genes.We developed mathematical methods to study the stability, the controllability, and the steady-state behaviour, as well as the transient responses of biological networks under different environmental perturbations.Therefore differential dynamical properties can be a valuable tool in biomedical research.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Texas A&M University, College Station, TX 77843, USA. hxiong@cs.tamu.edu

ABSTRACT

Background: Although a great deal is known about one gene or protein and its functions under different environmental conditions, little information is available about the complex behaviour of biological networks subject to different environmental perturbations. Observing differential expressions of one or more genes between normal and abnormal cells has been a mainstream method of discovering pertinent genes in diseases and therefore valuable drug targets. However, to date, no such method exists for elucidating and quantifying the differential dynamical behaviour of genetic regulatory networks, which can have greater impact on phenotypes than individual genes.

Results: We propose to redress the deficiency by formulating the functional study of biological networks as a control problem of dynamical systems. We developed mathematical methods to study the stability, the controllability, and the steady-state behaviour, as well as the transient responses of biological networks under different environmental perturbations. We applied our framework to three real-world datasets: the SOS DNA repair network in E. coli under different dosages of radiation, the GSH redox cycle in mice lung exposed to either poisonous air or normal air, and the MAPK pathway in mammalian cell lines exposed to three types of HIV type I Vpr, a wild type and two mutant types; and we found that the three genetic networks exhibited fundamentally different dynamical properties in normal and abnormal cells.

Conclusion: Difference in stability, relative stability, degrees of controllability, and transient responses between normal and abnormal cells means considerable difference in dynamical behaviours and different functioning of cells. Therefore differential dynamical properties can be a valuable tool in biomedical research.

Show MeSH