scholarly journals Computing Signal Transduction in Signaling Networks modeled as Boolean Networks, Petri Nets, and Hypergraphs

2018 ◽  
Author(s):  
Luis Sordo Vieira ◽  
Paola Vera-Licona
Keyword(s):  
Signal Transduction ◽  
Petri Nets ◽  
Intracellular Signaling ◽  
Incidence Matrix ◽  
Boolean Networks ◽  
Signaling Network ◽  
Signaling Networks ◽  
Signal Transduction Networks ◽  
Elementary Modes ◽  
Simple Paths

AbstractMathematical frameworks circumventing the need of mechanistic detail to build models of signal transduction networks include graphs, hypergraphs, Boolean Networks, and Petri Nets. Predicting how a signal transduces in a signaling network is essential to understand cellular functions and disease. Different formalisms exist to describe how a signal transduces in a given intracellular signaling network represented in the aforementioned modeling frameworks: elementary signaling modes, T-invariants, extreme pathway analysis, elementary flux modes, and simple paths. How do these formalisms compare?We present an overview of how signal transduction networks have been modelled using graphs, hypergraphs, Boolean Networks, and Petri Nets in the literature. We provide a review of the different formalisms for capturing signal transduction in a given model of an intracellular signaling network. We also discuss the existing translations between the different modeling frameworks, and the relationships between their corresponding signal transduction representations that have been described in the literature. Furthermore, as a new formalism of signal transduction, we show how minimal functional routes proposed for signaling networks modeled as Boolean Networks can be captured by computing topological factories, a methodology found in the metabolic networks literature. We further show that in the case of signaling networks represented with an acyclic B-hypergraph structure, the definitions are equivalent. In signaling networks represented as directed graphs, it has been shown that computations of elementary modes via its incidence matrix correspond to computations of simple paths and feedback loops. We show that computing elementary modes based on the incidence matrix of a B-hypergraph fails to capture minimal functional routes.

scholarly journals Pulses and waves of contractility

2017 ◽  
Vol 216 (12) ◽  
pp. 3899-3901 ◽  
Author(s):  
Min Wu
Keyword(s):  
Signal Transduction ◽  
Signaling Networks ◽  
Adherent Cells ◽  
Cell Contractility ◽  
Cell Biol ◽  
Signal Transduction Networks

The nature of signal transduction networks in the regulation of cell contractility is not entirely clear. In this study, Graessl et al. (2017. J. Cell Biol. https://doi.org/10.1083/jcb.201706052) visualized and characterized pulses and waves of Rho activation in adherent cells and proposed excitable Rho signaling networks underlying cell contractility.

Principles of Ubiquitin-Dependent Signaling

2018 ◽  
Vol 34 (1) ◽  
pp. 137-162 ◽  
Author(s):  
Eugene Oh ◽  
David Akopian ◽  
Michael Rape
Keyword(s):  
Cell Division ◽  
Posttranslational Modification ◽  
Intracellular Signaling ◽  
Signaling Network ◽  
Signaling Networks ◽  
E3 Ligases ◽  
Ubiquitin System ◽  
Ubiquitin Binding

Ubiquitylation is an essential posttranslational modification that controls cell division, differentiation, and survival in all eukaryotes. By combining multiple E3 ligases (writers), ubiquitin-binding effectors (readers), and de-ubiquitylases (erasers) with functionally distinct ubiquitylation tags, the ubiquitin system constitutes a powerful signaling network that is employed in similar ways from yeast to humans. Here, we discuss conserved principles of ubiquitin-dependent signaling that illustrate how this posttranslational modification shapes intracellular signaling networks to establish robust development and homeostasis throughout the eukaryotic kingdom.

scholarly journals The Multifaceted Roles of USP15 in Signal Transduction

2021 ◽  
Vol 22 (9) ◽  
pp. 4728
Author(s):  
Tanuza Das ◽  
Eun Joo Song ◽  
Eunice EunKyeong Kim
Keyword(s):  
Signal Transduction ◽  
Signaling Pathways ◽  
Cellular Networks ◽  
Clinical Applications ◽  
Regulatory Mechanisms ◽  
Signaling Networks ◽  
Post Translational Modification ◽  
Comprehensive Overview ◽  
E3 Ligases ◽  
Disease Markers

Ubiquitination and deubiquitination are protein post-translational modification processes that have been recognized as crucial mediators of many complex cellular networks, including maintaining ubiquitin homeostasis, controlling protein stability, and regulating several signaling pathways. Therefore, some of the enzymes involved in ubiquitination and deubiquitination, particularly E3 ligases and deubiquitinases, have attracted attention for drug discovery. Here, we review recent findings on USP15, one of the deubiquitinases, which regulates diverse signaling pathways by deubiquitinating vital target proteins. Even though several basic previous studies have uncovered the versatile roles of USP15 in different signaling networks, those have not yet been systematically and specifically reviewed, which can provide important information about possible disease markers and clinical applications. This review will provide a comprehensive overview of our current understanding of the regulatory mechanisms of USP15 on different signaling pathways for which dynamic reverse ubiquitination is a key regulator.

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.

Synaptic Plasticity In Vitro and In Silico: Insights into an Intracellular Signaling Maze

Physiology ◽  
2006 ◽  
Vol 21 (4) ◽  
pp. 289-296 ◽  
Author(s):  
Sriram M. Ajay ◽  
Upinder S. Bhalla
Keyword(s):  
Experimental Data ◽  
Synaptic Plasticity ◽  
Neuronal Activity ◽  
In Silico ◽  
Intracellular Signaling ◽  
Signaling Networks ◽  
Functional Roles ◽  
Current State ◽  
History Of

Synaptic plasticity provides a record of neuronal activity and is a likely basis for memory. The early apparent simplicity of the process of synaptic plasticity has been lost in a flood of experimental data that now implicates some 200 signaling molecules in cellular memory. It is now clear that these signaling networks perform surprisingly sophisticated cellular decisions that weigh factors such as input patterns, location of stimulus, history of activity, and context. Computer models have followed experiments into this maze of molecular detail, often matching closely with their experimental counterparts, but perhaps losing simplicity in the process. Here, we suggest that the merger of models and experiment have begun to restore the earlier simplicity by outlining a few key functional roles for signaling networks in synaptic plasticity. In this review, we discuss the current state of understanding of synaptic plasticity in terms of models and experiments.

scholarly journals Subcellular optogenetic activation of Cdc42 controls local and distal signaling to drive immune cell migration

2016 ◽  
Vol 27 (9) ◽  
pp. 1442-1450 ◽  
Author(s):  
Patrick R. O’Neill ◽  
Vani Kalyanaraman ◽  
N. Gautam
Keyword(s):  
Cell Migration ◽  
Intracellular Signaling ◽  
Immune Cell ◽  
Leading Edge ◽  
Signaling Networks ◽  
Membrane Protrusions ◽  
A Cell ◽  
Immune Cell Migration ◽  
Directional Cell Migration ◽  
Rhoa Signaling

Migratory immune cells use intracellular signaling networks to generate and orient spatially polarized responses to extracellular cues. The monomeric G protein Cdc42 is believed to play an important role in controlling the polarized responses, but it has been difficult to determine directly the consequences of localized Cdc42 activation within an immune cell. Here we used subcellular optogenetics to determine how Cdc42 activation at one side of a cell affects both cell behavior and dynamic molecular responses throughout the cell. We found that localized Cdc42 activation is sufficient to generate polarized signaling and directional cell migration. The optically activated region becomes the leading edge of the cell, with Cdc42 activating Rac and generating membrane protrusions driven by the actin cytoskeleton. Cdc42 also exerts long-range effects that cause myosin accumulation at the opposite side of the cell and actomyosin-mediated retraction of the cell rear. This process requires the RhoA-activated kinase ROCK, suggesting that Cdc42 activation at one side of a cell triggers increased RhoA signaling at the opposite side. Our results demonstrate how dynamic, subcellular perturbation of an individual signaling protein can help to determine its role in controlling polarized cellular responses.

scholarly journals Exploring Extreme Signaling Failures in Intracellular Molecular Networks

2021 ◽  
Author(s):  
Mustafa Ozen ◽  
Effat S. Emamian ◽  
Ali Abdi
Keyword(s):  
Intracellular Signaling ◽  
Molecular Mechanisms ◽  
Incorrect Response ◽  
Complex Trait ◽  
Molecular Networks ◽  
Signaling Networks ◽  
Initial Increase ◽  
Network Failure ◽  
Human Disorders ◽  
Cell Signaling Networks

AbstractDeveloping novel methods for the analysis of intracellular signaling networks is essential for understanding interconnected biological processes that underlie complex human disorders. A fundamental goal of this research is to quantify the vulnerability of a signaling network to the dysfunction of one or multiple molecules, when the dysfunction is defined as an incorrect response to the input signals. In this study, we propose an efficient algorithm to identify the extreme signaling failures that can induce the most detrimental impact on the physiological function of a molecular network. The algorithm basically finds the molecules, or groups of molecules, with the maximum vulnerability, i.e., the highest probability of causing the network failure, when they are dysfunctional. We propose another algorithm that efficiently accounts for signaling feedbacks in this analysis. The algorithms are tested on two experimentally verified ERBB and T cell signaling networks. Surprisingly, results reveal that as the number of concurrently dysfunctional molecules increases, the maximum vulnerability values quickly reach to a plateau following an initial increase. This suggests the specificity of vulnerable molecule (s) involved, as a specific number of faulty molecules cause the most detrimental damage to the function of the network. Increasing a random number of simultaneously faulty molecules does not further deteriorate the function of the network. Such a group of specific molecules whose dysfunction causes the extreme signaling failures can better elucidate the molecular mechanisms underlying the pathogenesis of complex trait disorders, and can offer new insights for the development of novel therapeutics.

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