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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.

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.


Evaluating the effectiveness of the proposed cost function for 7-gene networks with k uncertain regulations. The average gain of conducting the chosen experiments by the proposed approximate method with respect to the random experiments when deleting different genes is shown. (a) Deleting one gene. (b) Deleting two genes. (c) Deleting three genes.
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Figure 3: Evaluating the effectiveness of the proposed cost function for 7-gene networks with k uncertain regulations. The average gain of conducting the chosen experiments by the proposed approximate method with respect to the random experiments when deleting different genes is shown. (a) Deleting one gene. (b) Deleting two genes. (c) Deleting three genes.

Mentions: Figure 3 shows the average gain ρ for networks with n = 7 genes and k = 2, 3, 4, 5 uncertain regulations. For each k, we delete 1, 2, and 3 genes. Given the deletion of p genes, to evaluate the effectiveness of the proposed cost function in (14), we rank all p-gene combinations based on this cost function and compare the performance of the proposed approximate method when deleting each of these sets. For example in Figure 3(a), there are 6 different choices for a single gene to be deleted or in Figure 3(b) there are C(6, 2) = 15 different selections for two genes to be deleted. In all subfigures in 3, the average gain when the order of the deleted set is 0 corresponds to optimal experimental design [10]. This figure shows that for different number of uncertain regulations and different number of deleted genes, deleting those sets that correspond to a lower cost function results in larger average ρ. Denoting average ρ by , for k = 5, where for the optimal method, if we delete the gene with minimum cost, then , but if we delete the gene with maximum cost, then . When deleting two genes, corresponding to the best pair of genes (corresponding to the minimum cost) but for the pair corresponding to the largest cost (15th set) . When deleting three genes, for the best set of deleted genes and for the worst set .


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 7-gene networks with k uncertain regulations. The average gain of conducting the chosen experiments by the proposed approximate method with respect to the random experiments when deleting different genes is shown. (a) Deleting one gene. (b) Deleting two genes. (c) Deleting three genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Evaluating the effectiveness of the proposed cost function for 7-gene networks with k uncertain regulations. The average gain of conducting the chosen experiments by the proposed approximate method with respect to the random experiments when deleting different genes is shown. (a) Deleting one gene. (b) Deleting two genes. (c) Deleting three genes.
Mentions: Figure 3 shows the average gain ρ for networks with n = 7 genes and k = 2, 3, 4, 5 uncertain regulations. For each k, we delete 1, 2, and 3 genes. Given the deletion of p genes, to evaluate the effectiveness of the proposed cost function in (14), we rank all p-gene combinations based on this cost function and compare the performance of the proposed approximate method when deleting each of these sets. For example in Figure 3(a), there are 6 different choices for a single gene to be deleted or in Figure 3(b) there are C(6, 2) = 15 different selections for two genes to be deleted. In all subfigures in 3, the average gain when the order of the deleted set is 0 corresponds to optimal experimental design [10]. This figure shows that for different number of uncertain regulations and different number of deleted genes, deleting those sets that correspond to a lower cost function results in larger average ρ. Denoting average ρ by , for k = 5, where for the optimal method, if we delete the gene with minimum cost, then , but if we delete the gene with maximum cost, then . When deleting two genes, corresponding to the best pair of genes (corresponding to the minimum cost) but for the pair corresponding to the largest cost (15th set) . When deleting three genes, for the best set of deleted genes and for the worst set .

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.