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Constraint-based analysis of gene interactions using restricted boolean networks and time-series data.

Higa CH, Louzada VH, Andrade TP, Hashimoto RF - BMC Proc (2011)

Bottom Line: The second data set is derived from experiments performed using HeLa cells.The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered.It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Mathematics and Statistics, University of Sao Paulo, Rua do Matao 1010, 05508-090, Sao Paulo - SP, Brazil. ronaldo@ime.usp.br.

ABSTRACT

Background: A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it.

Results: We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered.

Conclusions: The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

No MeSH data available.


Connection frequencies for the Budding Yeast artificial data - 2 Results for the genes Swi5, Cdc20, Clb5, Sic1, Clb1 and Mcm1.
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Figure 3: Connection frequencies for the Budding Yeast artificial data - 2 Results for the genes Swi5, Cdc20, Clb5, Sic1, Clb1 and Mcm1.

Mentions: For each gene xi, the algorithm generates a collection of consistent rows Ri using the time-series data (the 13 states presented in Table 5) to generate the constraints of the CSP. If we compute the frequency of the types of connections, we are able to assigning probabilities of connection for each pair of genes. In Figures 2 and 3 we show the frequency of different types of connections to each gene xi from all other genes. From these figures, we can see that the algorithm was capable of identifying 11 determined connections and 13 partially determined connections. The results are shown in Figure 4. Note that, in this figure, the arrows do not necessarily indicate activation.


Constraint-based analysis of gene interactions using restricted boolean networks and time-series data.

Higa CH, Louzada VH, Andrade TP, Hashimoto RF - BMC Proc (2011)

Connection frequencies for the Budding Yeast artificial data - 2 Results for the genes Swi5, Cdc20, Clb5, Sic1, Clb1 and Mcm1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3090763&req=5

Figure 3: Connection frequencies for the Budding Yeast artificial data - 2 Results for the genes Swi5, Cdc20, Clb5, Sic1, Clb1 and Mcm1.
Mentions: For each gene xi, the algorithm generates a collection of consistent rows Ri using the time-series data (the 13 states presented in Table 5) to generate the constraints of the CSP. If we compute the frequency of the types of connections, we are able to assigning probabilities of connection for each pair of genes. In Figures 2 and 3 we show the frequency of different types of connections to each gene xi from all other genes. From these figures, we can see that the algorithm was capable of identifying 11 determined connections and 13 partially determined connections. The results are shown in Figure 4. Note that, in this figure, the arrows do not necessarily indicate activation.

Bottom Line: The second data set is derived from experiments performed using HeLa cells.The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered.It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Mathematics and Statistics, University of Sao Paulo, Rua do Matao 1010, 05508-090, Sao Paulo - SP, Brazil. ronaldo@ime.usp.br.

ABSTRACT

Background: A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it.

Results: We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered.

Conclusions: The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

No MeSH data available.