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Predicting cell cycle regulated genes by causal interactions.

Emmert-Streib F, Dehmer M - PLoS ONE (2009)

Bottom Line: However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms.The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes.Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Biomedical Sciences, Queen's University Belfast, Belfast, United Kingdom. v@bio-complexity.com

ABSTRACT
The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e.g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first.

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Expression profile for STE12 for time series data from Spellman et al. [32].
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pone-0006633-g009: Expression profile for STE12 for time series data from Spellman et al. [32].

Mentions: Finally, in Fig. 9–13 we present a visualization of the expression profiles (obtained from Cyclebase) of the five genes predicted to be periodic. The time series used are from Spellman et al. [32]. In addition we provide in table 4 the p-values assigned by Cyclebase [30] for periodicity (second column) and for regulation (third column) of the five genes. The p-values for periodicity for PIP2 (Fig. 12) and ADR1 (Fig. 13) are below 0.05. Also, the p-values for regulation for STE12 and SRD1 are below 0.05. The reason why they are not declared as periodic is because their complementary p-value (either for regulation or periodicity) is much higher than 0.05. A possible reason for this is the high variability of the time series data with respect to different experiments. This variability makes it also very difficult to assign an unique peak time to these time series and, hence, for conventional methods based solely on the shape of a signal to clarify this situation. The only gene that has neither a low p-value for periodicity nor for regulation is RPH1 (Fig. 10). However, as one can see from Fig. 10 there are pronounced peaks occurring at certain phases of the cell cycle but these peaks are not precisely reproducible for different cycles and also experiments. This might be an indicator, if this gene is truly cell cycle regulated, of the redundancy of this gene meaning it is not involved in an unique signaling path but occurs on a parallel pathway that is not used during every cell cycle. This would provide a plausible explanation of the observed variability in the expression profile for different cell cycles as well as different experiments.


Predicting cell cycle regulated genes by causal interactions.

Emmert-Streib F, Dehmer M - PLoS ONE (2009)

Expression profile for STE12 for time series data from Spellman et al. [32].
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2723924&req=5

pone-0006633-g009: Expression profile for STE12 for time series data from Spellman et al. [32].
Mentions: Finally, in Fig. 9–13 we present a visualization of the expression profiles (obtained from Cyclebase) of the five genes predicted to be periodic. The time series used are from Spellman et al. [32]. In addition we provide in table 4 the p-values assigned by Cyclebase [30] for periodicity (second column) and for regulation (third column) of the five genes. The p-values for periodicity for PIP2 (Fig. 12) and ADR1 (Fig. 13) are below 0.05. Also, the p-values for regulation for STE12 and SRD1 are below 0.05. The reason why they are not declared as periodic is because their complementary p-value (either for regulation or periodicity) is much higher than 0.05. A possible reason for this is the high variability of the time series data with respect to different experiments. This variability makes it also very difficult to assign an unique peak time to these time series and, hence, for conventional methods based solely on the shape of a signal to clarify this situation. The only gene that has neither a low p-value for periodicity nor for regulation is RPH1 (Fig. 10). However, as one can see from Fig. 10 there are pronounced peaks occurring at certain phases of the cell cycle but these peaks are not precisely reproducible for different cycles and also experiments. This might be an indicator, if this gene is truly cell cycle regulated, of the redundancy of this gene meaning it is not involved in an unique signaling path but occurs on a parallel pathway that is not used during every cell cycle. This would provide a plausible explanation of the observed variability in the expression profile for different cell cycles as well as different experiments.

Bottom Line: However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms.The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes.Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology, School of Biomedical Sciences, Queen's University Belfast, Belfast, United Kingdom. v@bio-complexity.com

ABSTRACT
The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e.g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first.

Show MeSH