Effective Boolean dynamics analysis to identify functionally important genes in large-scale signaling networks

Trinh, Hung-Cuong; Kwon, Yung-Keun

doi:10.1016/j.biosystems.2015.07.007

Cited by 6 publications

(6 citation statements)

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“…Based on these advantages, RMut package can be used in various applications. For example, we can identify some essential components [1, 2] by examining the sensitivity values of the interested components. In addition, it can be used to predict genetic interactions [3] by comparing the sensitivity value of a double gene mutation from the value predicted from single mutations, and reveal the network intervention [4] by applying the state-flip mutation subject to a single gene.…”

Section: Discussionmentioning

confidence: 99%

“…These node-based mutations have been employed in a variety of studies, but without comparing them with other types of mutations. For instance, knockout and overexpression mutations [1, 28], or rule-flip and state-flip mutations [2, 22] were used to predict essential components in signaling networks. Another study used the knockout mutation to predict mutant phenotypes of fission yeast [62].…”

Section: Case Studiesmentioning

confidence: 99%

“…Many different types of mutations have been used to investigate dynamic behaviors of biological networks; these have focused on essential components identification [1, 2], genetic interactions prediction [3], network intervention [4], and the relationship between dynamic and structural properties [5–7]. In addition, many computational tools have been developed to support in silico simulations based on these mutations.…”

Section: Introductionmentioning

confidence: 99%

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RMut: R package for a Boolean sensitivity analysis against various types of mutations

Trinh

Kwon

2019

PLoS ONE

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There have been many in silico studies based on a Boolean network model to investigate network sensitivity against gene or interaction mutations. However, there are no proper tools to examine the network sensitivity against many different types of mutations, including user-defined ones. To address this issue, we developed RMut, which is an R package to analyze the Boolean network-based sensitivity by efficiently employing not only many well-known node-based and edgetic mutations but also novel user-defined mutations. In addition, RMut can specify the mutation area and the duration time for more precise analysis. RMut can be used to analyze large-scale networks because it is implemented in a parallel algorithm using the OpenCL library. In the first case study, we observed that the real biological networks were most sensitive to overexpression/state-flip and edge-addition/-reverse mutations among node-based and edgetic mutations, respectively. In the second case study, we showed that edgetic mutations can predict drug-targets better than node-based mutations. Finally, we examined the network sensitivity to double edge-removal mutations and found an interesting synergistic effect. Taken together, these findings indicate that RMut is a flexible R package to efficiently analyze network sensitivity to various types of mutations. RMut is available at https://github.com/csclab/RMut.

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Section: Discussionmentioning

confidence: 99%

Section: Case Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RMut: R package for a Boolean sensitivity analysis against various types of mutations

Trinh

Kwon

2019

PLoS ONE

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“…In addition, we specified all

and

values independently and uniformly at random between 0 and 1. We note that many biological networks were successfully represented by NCFs [ 50 , 51 , 52 ], and NCFs also properly fit biological experiments’ data [ 49 ] including pleiotropy analysis [ 11 ]. Those support that NCFs can describe the network dynamics considerably similarly to those real biological networks.…”

Section: Methodsmentioning

confidence: 99%

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model

Mazaya

Kwon

2022

Biomolecules

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Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases. Although some biological experiments have been proposed, still little is known about pleiotropy on gene–gene dynamics, since most previous studies have been based on correlation analysis. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene–gene dynamical characteristics. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores (sPS), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. In addition, we found that many types of functionally important genes tend to have higher sPS values than other genes; in other words, they are more pleiotropic. We investigated the relations of sPS with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. This implies that the structural characteristics are principles to identify new pleiotropic genes. Finally, we found some biological evidence showing that sPS analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene–phenotype relations.

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“…In this paper, each NCF is randomized by specifying every I m and O m between 0 and 1 uniformly at random. We note that many molecular interactions were successfully represented by NCFs [ 43 – 45 ].…”

Section: Methodsmentioning

confidence: 99%

Construction and analysis of gene-gene dynamics influence networks based on a Boolean model

2017

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BackgroundIdentification of novel gene-gene relations is a crucial issue to understand system-level biological phenomena. To this end, many methods based on a correlation analysis of gene expressions or structural analysis of molecular interaction networks have been proposed. They have a limitation in identifying more complicated gene-gene dynamical relations, though.ResultsTo overcome this limitation, we proposed a measure to quantify a gene-gene dynamical influence (GDI) using a Boolean network model and constructed a GDI network to indicate existence of a dynamical influence for every ordered pair of genes. It represents how much a state trajectory of a target gene is changed by a knockout mutation subject to a source gene in a gene-gene molecular interaction (GMI) network. Through a topological comparison between GDI and GMI networks, we observed that the former network is denser than the latter network, which implies that there exist many gene pairs of dynamically influencing but molecularly non-interacting relations. In addition, a larger number of hub genes were generated in the GDI network. On the other hand, there was a correlation between these networks such that the degree value of a node was positively correlated to each other. We further investigated the relationships of the GDI value with structural properties and found that there are negative and positive correlations with the length of a shortest path and the number of paths, respectively. In addition, a GDI network could predict a set of genes whose steady-state expression is affected in E. coli gene-knockout experiments. More interestingly, we found that the drug-targets with side-effects have a larger number of outgoing links than the other genes in the GDI network, which implies that they are more likely to influence the dynamics of other genes. Finally, we found biological evidences showing that the gene pairs which are not molecularly interacting but dynamically influential can be considered for novel gene-gene relationships.ConclusionTaken together, construction and analysis of the GDI network can be a useful approach to identify novel gene-gene relationships in terms of the dynamical influence.Electronic supplementary materialThe online version of this article (10.1186/s12918-017-0509-y) contains supplementary material, which is available to authorized users.

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Effective Boolean dynamics analysis to identify functionally important genes in large-scale signaling networks

Cited by 6 publications

References 72 publications

RMut: R package for a Boolean sensitivity analysis against various types of mutations

RMut: R package for a Boolean sensitivity analysis against various types of mutations

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model

Construction and analysis of gene-gene dynamics influence networks based on a Boolean model

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