Fault Diagnosis Engineering in Molecular Signaling Networks: An Overview and Applications in Target Discovery

2010 ◽  
Vol 7 (5) ◽  
pp. 1111-1123 ◽  
Author(s):  
Ali Abdi ◽  
Effat S. Emamian
Keyword(s):  
Fault Diagnosis ◽  
Signaling Networks ◽  
Molecular Signaling ◽  
Target Discovery

scholarly journals Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tien-Dzung Tran ◽  
Duc-Tinh Pham
Keyword(s):  
Anticancer Drug ◽  
Drug Target ◽  
Target Genes ◽  
Signaling Network ◽  
Competitive Dynamics ◽  
Signaling Networks ◽  
Molecular Signaling ◽  
Dynamics Model ◽  
Cancer Signaling ◽  
The Impact

AbstractEach cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.

scholarly journals Molecular Signaling Networks That Choreograph Epimorphic Fin Regeneration in Zebrafish – A Mini-Review

Gerontology ◽  
2010 ◽  
Vol 56 (2) ◽  
pp. 231-240 ◽  
Author(s):  
Tamara L. Tal ◽  
Jill A. Franzosa ◽  
Robert L. Tanguay
Keyword(s):  
Signaling Networks ◽  
Molecular Signaling ◽  
Fin Regeneration

scholarly journals Inferring the structures of signaling motifs from paired dynamic traces of single cells

2019 ◽  
Author(s):  
R. A. Haggerty ◽  
Jeremy E. Purvis
Keyword(s):  
Time Series ◽  
Single Cell ◽  
Single Cells ◽  
Underlying Mechanism ◽  
Repeated Measurements ◽  
Signaling Networks ◽  
Molecular Signaling ◽  
Signaling Mechanism ◽  
Signaling Dynamics ◽  
Cell To Cell Variability

AbstractIndividual cells show variability in their signaling dynamics that often correlates with phenotypic responses, indicating that cell-to-cell variability is not merely noise but can have functional consequences. Based on this observation, we reasoned that cell-to-cell variability under the same treatment condition could be explained by a single signaling motif that deterministically maps different upstream signals into a corresponding set of downstream responses. If this assumption holds, then repeated measurements of upstream and downstream signaling dynamics in single cells could provide information about the underlying signaling motif for a given pathway, even when no prior knowledge of that motif exists. To test these two hypotheses, we developed a computer algorithm called MISC (Motif Inference from Single Cells) that infers the underlying signaling motif from paired time-series measurements from individual cells. When applied to measurements of transcription factor and reporter gene expression in the yeast stress response, MISC predicted signaling motifs that were consistent with previous mechanistic models of transcription. The ability to detect the underlying mechanism became less certain when a cell’s upstream signal was randomly paired with another cell’s downstream response, demonstrating how averaging time-series measurements across a population obscures information about the underlying signaling mechanism. In some cases, motif predictions improved as more cells were added to the analysis. These results provide evidence that mechanistic information about cellular signaling networks can be embedded within the dynamical patterns of single cells.Author SummaryCells use molecular signaling networks to translate dynamically changing stimuli into appropriate downstream responses. Specialized network structures, or motifs, allow cells to properly decode a variety of temporal input signals. In this paper, we present an algorithm that infers signaling motifs from multiple examples of an upstream signal paired with its downstream response in the same cell. We compare the predictive power of single-cell versus averaged time-series traces and the incremental benefit of adding more single-cell traces to the algorithm. We use this approach to understand how yeast respond to environmental stresses.

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