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Efficient experimental design for uncertainty reduction in gene regulatory networks.

Dehghannasiri R, Yoon BJ, Dougherty ER - BMC Bioinformatics (2015)

Bottom Line: MOCU quantifies the expected increase of cost resulting from uncertainty.We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks.Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost.

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ABSTRACT

Background: An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first.

Results: The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks.

Conclusions: Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/.

No MeSH data available.


Evaluating the effectiveness of the proposed cost function for 8-gene networks with 4 uncertain regulations. The average gain of conducting the chosen experiments by the proposed approximate method with respect to the random experiments is shown. (a) Deleting one gene. (b) Deleting two genes. (c) Deleting three genes. (d) Deleting four genes.
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Figure 5: Evaluating the effectiveness of the proposed cost function for 8-gene networks with 4 uncertain regulations. The average gain of conducting the chosen experiments by the proposed approximate method with respect to the random experiments is shown. (a) Deleting one gene. (b) Deleting two genes. (c) Deleting three genes. (d) Deleting four genes.

Mentions: Figure 5 shows performance evaluation for 8-gene networks with k = 4 uncertain regulations, deleting up to four genes from the original networks. Again, this figure verifies the promising performance of the proposed cost function. It can be observed that when gene sets possessing larger cost are deleted, the resulting average gain decreases. For example, when we delete 4 genes for the optimal method and for the approximate method decreases from 0.0352 to 0.0175 if we delete the 35th set of 4 genes according to the cost function instead of the first set. We also provide the box plots for the performance of the approximate method for 8-gene networks in the Additional file 1.


Efficient experimental design for uncertainty reduction in gene regulatory networks.

Dehghannasiri R, Yoon BJ, Dougherty ER - BMC Bioinformatics (2015)

Evaluating the effectiveness of the proposed cost function for 8-gene networks with 4 uncertain regulations. The average gain of conducting the chosen experiments by the proposed approximate method with respect to the random experiments is shown. (a) Deleting one gene. (b) Deleting two genes. (c) Deleting three genes. (d) Deleting four genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4597030&req=5

Figure 5: Evaluating the effectiveness of the proposed cost function for 8-gene networks with 4 uncertain regulations. The average gain of conducting the chosen experiments by the proposed approximate method with respect to the random experiments is shown. (a) Deleting one gene. (b) Deleting two genes. (c) Deleting three genes. (d) Deleting four genes.
Mentions: Figure 5 shows performance evaluation for 8-gene networks with k = 4 uncertain regulations, deleting up to four genes from the original networks. Again, this figure verifies the promising performance of the proposed cost function. It can be observed that when gene sets possessing larger cost are deleted, the resulting average gain decreases. For example, when we delete 4 genes for the optimal method and for the approximate method decreases from 0.0352 to 0.0175 if we delete the 35th set of 4 genes according to the cost function instead of the first set. We also provide the box plots for the performance of the approximate method for 8-gene networks in the Additional file 1.

Bottom Line: MOCU quantifies the expected increase of cost resulting from uncertainty.We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks.Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first.

Results: The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks.

Conclusions: Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/.

No MeSH data available.