In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Datasets
2.2. Boolean Network Model
2.3. Computation of In-Silico Pleiotropic Scores
- For each initial state we obtain two attractors and in the wild-type and the -mutant networks, respectively. For convenience, let and .
- We compute a distance between and defined as follows:
- Lastly, we compute the dynamics influence of on denoted by by averaging out over a set of initial states in as follows:
2.4. A Standardized Measure of Degree of Pleiotropy
2.5. Structural Characteristics of Pleiotropic Genes
- A feedback loop (FBL) means a sequence chain of nodes where any node is not repeated except the starting and the end nodes [59,61]. In a given network , an FBL is a closed simple cycle in which all nodes except the starting and ending nodes are not revisited; in other words, a path of a length is represented by a sequence of ordered nodes with no repeated nodes except and . Hence, the is called a feedback loop if . It was known that FBLs play important roles for controlling dynamics behavior of signaling networks [61,62,63,64].
- Centrality properties including the closeness defined as the reciprocal of the average distance from a node to every other node [4], the betweenness defined as the ability of a gene to control communication between genes through the shortest paths [65], the stress based on enumeration of the shortest paths [66], and the eigenvector represented by the principal eigenvector of the adjacency matrix of the network, where each node affects all of its neighbors [52].
2.6. Random Network Generation
2.7. Parallel Computation
2.8. Statistical Analysis
3. Results
3.1. Comparison of with the Observational Pleiotropy
3.2. Relation of and the Functional Importance Genes
3.3. Relation of and the Structural Characteristics
3.4. Biological Evidence of Pleiotropic Genes Based on
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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No. | Gene Name | HPO-Associated | |
---|---|---|---|
1 | COL2A1 | 842 | 22 |
2 | FGFR1 | 1045 | 7 |
3 | FGFR2 | 1137 | 3 |
4 | FGFR3 | 1006 | 5 |
5 | LIMK1 | 733 | 2 |
6 | NRAS | 728 | 1 |
7 | PIK3CA | 751 | 9 |
8 | PRKAR1A | 697 | 1 |
9 | PTEN | 838 | 107 |
10 | TGFBR2 | 586 | 78 |
Total | 8363 | 235 |
No. | Gene Name | HPO | NuCancer | DT | ES | TSG | OCG | DG | Deg | In-Deg | Out-Deg | NuFBL | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | ABL1 | 1 | 0.6 | −0.35 | 23 | 1 | 1 | 0 | 1 | 1 | 27 | 15 | 12 | 4588 |
2 | PIK3CA | 1 | 0.7 | −0.06 | 19 | 1 | 1 | 0 | 1 | 1 | 51 | 45 | 6 | 40,101 |
3 | EGFR | 1 | 0.45 | 0.11 | 21 | 1 | 1 | 0 | 1 | 1 | 73 | 41 | 32 | 225 |
4 | SERPINA1 | 1 | 0.21 | −0.34 | 60 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 |
5 | CAMK2B | 1 | 0.44 | −0.04612 | 0 | 1 | 0 | 0 | 0 | 1 | 15 | 10 | 5 | 19,612 |
6 | PPP1CB | 1 | 0.55 | −0.06861 | 1 | 0 | 1 | 0 | 0 | 1 | 28 | 3 | 25 | 30,526 |
7 | CAMK2A | 1 | 0.63 | −0.30841 | 0 | 1 | 1 | 0 | 0 | 1 | 15 | 10 | 5 | 19,612 |
8 | NRAS | 1 | 0. 78 | −0.40583 | 1 | 0 | 1 | 0 | 1 | 1 | 44 | 28 | 16 | 20,154 |
9 | CHP1 | 1 | 0.45 | −0.08359 | 0 | 1 | 1 | 0 | 0 | 1 | 10 | 8 | 2 | 2970 |
10 | PLA2G6 | 1 | 1 | −0.42082 | 303 | 1 | 1 | 0 | 0 | 1 | 10 | 10 | 0 | 0 |
11 | IGFBP3 | 0 | 0.45 | 0.08 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 |
12 | PRKCA | 0 | 0.48 | 0.06 | 0 | 1 | 1 | 0 | 1 | 1 | 24 | 7 | 17 | 10,054 |
13 | ITGAM | 0 | 0.86 | −0.4 | 0 | 0 | 0 | 0 | 0 | 1 | 9 | 3 | 6 | 260 |
14 | ROCK2 | 0 | 0.59 | −0.12 | 0 | 1 | 1 | 0 | 0 | 1 | 7 | 3 | 4 | 39,206 |
15 | PPP1CC | 0 | 0.42 | 0.09 | 0 | 1 | 1 | 0 | 0 | 1 | 28 | 3 | 28 | 30,526 |
16 | PPP1CA | 0 | 0.52 | −0.02 | 0 | 1 | 1 | 1 | 0 | 1 | 28 | 3 | 25 | 30,526 |
17 | PRKAA1 | 0 | 0.40 | −0.2035 | 0 | 1 | 1 | 1 | 0 | 1 | 3 | 0 | 3 | 0 |
18 | PPP1R12A | 0 | 0.73 | −0.27844 | 0 | 0 | 1 | 0 | 0 | 1 | 15 | 3 | 12 | 7624 |
19 | CDK2 | 0 | 0.61 | −0.36836 | 0 | 1 | 1 | 1 | 0 | 1 | 10 | 3 | 7 | 4 |
20 | PPP1CC | 0 | 0.451327 | 0.043803 | 0 | 1 | 1 | 0 | 0 | 1 | 28 | 3 | 25 | 30,526 |
21 | RALBP1 | 0 | 0.428571 | −0.00116 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 2 | 4 | 648 |
22 | CBLB | 0 | 0.6045 | −0.02364 | 1 | 0 | 1 | 0 | 1 | 1 | 60 | 4 | 56 | 1202 |
23 | WNT11 | 0 | 0.5455 | −0.34588 | 0 | 0 | 1 | 1 | 0 | 1 | 17 | 7 | 10 | 0 |
24 | CAMK2D | 0 | 0.75 | −0.27844 | 0 | 1 | 0 | 0 | 0 | 1 | 15 | 10 | 5 | 19,612 |
25 | CRK | 0 | 1 | −0.42082 | 0 | 0 | 1 | 0 | 1 | 1 | 45 | 31 | 14 | 5391 |
26 | CALML5 | 0 | 0.4455 | 0.036309 | 0 | 0 | 0 | 0 | 0 | 1 | 36 | 9 | 27 | 11,451 |
27 | GRIA1 | 0 | 0.90625 | −0.39834 | 0 | 1 | 1 | 0 | 0 | 1 | 10 | 9 | 1 | 13,272 |
28 | GRIA2 | 0 | 0.619048 | −0.18102 | 0 | 1 | 1 | 0 | 0 | 1 | 10 | 9 | 1 | 13,272 |
29 | GNA12 | 0 | 0.317647 | 0.013827 | 1 | 0 | 1 | 0 | 1 | 1 | 23 | 6 | 17 | 2528 |
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Mazaya, M.; Kwon, Y.-K. In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model. Biomolecules 2022, 12, 1139. https://doi.org/10.3390/biom12081139
Mazaya M, Kwon Y-K. In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model. Biomolecules. 2022; 12(8):1139. https://doi.org/10.3390/biom12081139
Chicago/Turabian StyleMazaya, Maulida, and Yung-Keun Kwon. 2022. "In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model" Biomolecules 12, no. 8: 1139. https://doi.org/10.3390/biom12081139
APA StyleMazaya, M., & Kwon, Y.-K. (2022). In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model. Biomolecules, 12(8), 1139. https://doi.org/10.3390/biom12081139