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Investigations into the relationship between feedback loops and functional importance of a signal transduction network based on Boolean network modeling.

Kwon YK, Choi SS, Cho KH - BMC Bioinformatics (2007)

Bottom Line: In this respect, we considered a feedback loop which is ubiquitously found in various biological networks.These results led us to infer that such a strong positive correlation between the NuFBL and the importance of a network node might be an intrinsic principle of biological networks in view of network dynamics.This result also suggests the existence of unknown feedback loops around functionally important nodes in biological networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Republic of Korea. kwon@soar.snu.ac.kr

ABSTRACT

Background: A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain the functional importance of network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need to investigate another topological measure that can better describe the functional importance of network nodes. In this respect, we considered a feedback loop which is ubiquitously found in various biological networks.

Results: We discovered that the number of feedback loops (NuFBL) is a crucial measure for evaluating the importance of a network node and verified this through a signal transduction network in the hippocampal CA1 neuron of mice as well as through generalized biological network models represented by Boolean networks. In particular, we observed that the proteins with a larger NuFBL are more likely to be essential and to evolve slowly in the hippocampal CA1 neuronal signal transduction network. Then, from extensive simulations based on the Boolean network models, we proved that a network node with the larger NuFBL is likely to be more important as the mutations of the initial state or the update rule of such a node made the network converge to a different attractor. These results led us to infer that such a strong positive correlation between the NuFBL and the importance of a network node might be an intrinsic principle of biological networks in view of network dynamics.

Conclusion: The presented analysis on topological characteristics of biological networks showed that the number of feedback loops is positively correlated with the functional importance of network nodes. This result also suggests the existence of unknown feedback loops around functionally important nodes in biological networks.

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Correlation between the functional importance of proteins and the NuFBL. (a) The NuFBL's were plotted against the mutant phenotypes of the proteins in the network where proteins were classified according to the previous report [1]. (b) The NuFBL's were plotted against the evolutionary rate [1] (dN/dS) of proteins which were grouped into five different classes according to their evolutionary rates. For each protein group, the average and the confidence interval for 95% confidence level of the NuFBL are shown on the y-axis (see additional data file 4 for further details).
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Figure 1: Correlation between the functional importance of proteins and the NuFBL. (a) The NuFBL's were plotted against the mutant phenotypes of the proteins in the network where proteins were classified according to the previous report [1]. (b) The NuFBL's were plotted against the evolutionary rate [1] (dN/dS) of proteins which were grouped into five different classes according to their evolutionary rates. For each protein group, the average and the confidence interval for 95% confidence level of the NuFBL are shown on the y-axis (see additional data file 4 for further details).

Mentions: We considered the large signal transduction network of the hippocampal CA1 neuron of mice to examine the NuFBL as a new network measure [6]. We first confirmed the previous observation that proteins with a higher connectivity are more likely to be lethal and to have a slower evolutionary rate (data not shown). It has been considered that the lethal proteins are more essential than other proteins showing no obvious phenotype when deleted [1]. Also, it has been known that functionally important proteins are under a strong regulatory constraint resulting in relatively slow evolution [24,25]. Similarly, to examine whether the NuFBL of a protein is related to its functional importance, the NuFBL was plotted against the degree of phenotype and the evolutionary rate for grouped proteins as described in Methods. In Fig. 1, it was observed that more essential proteins (Fig. 1a) and more slowly evolving proteins (Fig. 1b) tend to have a larger NuFBL, which suggests that functionally important proteins in the signal transduction network are more likely to be regulated by many feedback loops. On the contrary, the nonessential proteins indicated by "Not obvious" phenotype group showed a very small NuFBL and they are less likely to be regulated by feedback loops. Note that most of the proteins except those with the slowest evolutionary rate have little difference in the NuFBL.


Investigations into the relationship between feedback loops and functional importance of a signal transduction network based on Boolean network modeling.

Kwon YK, Choi SS, Cho KH - BMC Bioinformatics (2007)

Correlation between the functional importance of proteins and the NuFBL. (a) The NuFBL's were plotted against the mutant phenotypes of the proteins in the network where proteins were classified according to the previous report [1]. (b) The NuFBL's were plotted against the evolutionary rate [1] (dN/dS) of proteins which were grouped into five different classes according to their evolutionary rates. For each protein group, the average and the confidence interval for 95% confidence level of the NuFBL are shown on the y-axis (see additional data file 4 for further details).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2100072&req=5

Figure 1: Correlation between the functional importance of proteins and the NuFBL. (a) The NuFBL's were plotted against the mutant phenotypes of the proteins in the network where proteins were classified according to the previous report [1]. (b) The NuFBL's were plotted against the evolutionary rate [1] (dN/dS) of proteins which were grouped into five different classes according to their evolutionary rates. For each protein group, the average and the confidence interval for 95% confidence level of the NuFBL are shown on the y-axis (see additional data file 4 for further details).
Mentions: We considered the large signal transduction network of the hippocampal CA1 neuron of mice to examine the NuFBL as a new network measure [6]. We first confirmed the previous observation that proteins with a higher connectivity are more likely to be lethal and to have a slower evolutionary rate (data not shown). It has been considered that the lethal proteins are more essential than other proteins showing no obvious phenotype when deleted [1]. Also, it has been known that functionally important proteins are under a strong regulatory constraint resulting in relatively slow evolution [24,25]. Similarly, to examine whether the NuFBL of a protein is related to its functional importance, the NuFBL was plotted against the degree of phenotype and the evolutionary rate for grouped proteins as described in Methods. In Fig. 1, it was observed that more essential proteins (Fig. 1a) and more slowly evolving proteins (Fig. 1b) tend to have a larger NuFBL, which suggests that functionally important proteins in the signal transduction network are more likely to be regulated by many feedback loops. On the contrary, the nonessential proteins indicated by "Not obvious" phenotype group showed a very small NuFBL and they are less likely to be regulated by feedback loops. Note that most of the proteins except those with the slowest evolutionary rate have little difference in the NuFBL.

Bottom Line: In this respect, we considered a feedback loop which is ubiquitously found in various biological networks.These results led us to infer that such a strong positive correlation between the NuFBL and the importance of a network node might be an intrinsic principle of biological networks in view of network dynamics.This result also suggests the existence of unknown feedback loops around functionally important nodes in biological networks.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bio and Brain Engineering and KI for the BioCentury, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, Daejeon, 305-701, Republic of Korea. kwon@soar.snu.ac.kr

ABSTRACT

Background: A number of studies on biological networks have been carried out to unravel the topological characteristics that can explain the functional importance of network nodes. For instance, connectivity, clustering coefficient, and shortest path length were previously proposed for this purpose. However, there is still a pressing need to investigate another topological measure that can better describe the functional importance of network nodes. In this respect, we considered a feedback loop which is ubiquitously found in various biological networks.

Results: We discovered that the number of feedback loops (NuFBL) is a crucial measure for evaluating the importance of a network node and verified this through a signal transduction network in the hippocampal CA1 neuron of mice as well as through generalized biological network models represented by Boolean networks. In particular, we observed that the proteins with a larger NuFBL are more likely to be essential and to evolve slowly in the hippocampal CA1 neuronal signal transduction network. Then, from extensive simulations based on the Boolean network models, we proved that a network node with the larger NuFBL is likely to be more important as the mutations of the initial state or the update rule of such a node made the network converge to a different attractor. These results led us to infer that such a strong positive correlation between the NuFBL and the importance of a network node might be an intrinsic principle of biological networks in view of network dynamics.

Conclusion: The presented analysis on topological characteristics of biological networks showed that the number of feedback loops is positively correlated with the functional importance of network nodes. This result also suggests the existence of unknown feedback loops around functionally important nodes in biological networks.

Show MeSH