scholarly journals Neural Differentiation Dynamics Controlled by Multiple Feedback Loops in a Comprehensive Molecular Interaction Network

Processes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 166
Author(s):  
Tsuyoshi Iwasaki ◽  
Ryo Takiguchi ◽  
Takumi Hiraiwa ◽  
Takahiro G. Yamada ◽  
Kazuto Yamazaki ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Neural Differentiation ◽  
Large Scale ◽  
Model Simulation ◽  
Interaction Network ◽  
Feedback Loops ◽  
Characteristic Change ◽  
Large Network ◽  
Large Scale Network ◽  
Scale Network

Mathematical model simulation is a useful method for understanding the complex behavior of a living system. The construction of mathematical models using comprehensive information is one of the techniques of model construction. Such a comprehensive knowledge-based network tends to become a large-scale network. As a result, the variation of analyses is limited to a particular kind of analysis because of the size and complexity of the model. To analyze a large-scale regulatory network of neural differentiation, we propose a contractive method that preserves the dynamic behavior of a large network. The method consists of the following two steps: comprehensive network building and network reduction. The reduction phase can extract network loop structures from a large-scale regulatory network, and the subnetworks were combined to preserve the dynamics of the original large-scale network. We confirmed that the extracted loop combination reproduced the known dynamics of HES1 and ASCL1 before and after differentiation, including oscillation and equilibrium of their concentrations. The model also reproduced the effects of the overexpression and knockdown of the Id2 gene. Our model suggests that the characteristic change in HES1 and ASCL1 expression in the large-scale regulatory network is controlled by a combination of four feedback loops, including a large loop, which has not been focused on. The model extracted by our method has the potential to reveal the critical mechanisms of neural differentiation. The method is applicable to other biological events.

scholarly journals Neural differentiation dynamics controlled by multiple feedback loop motifs in a comprehensive molecular interaction network

2019 ◽  
Author(s):  
Tsuyoshi Iwasaki ◽  
Ryo Takiguchi ◽  
Takumi Hiraiwa ◽  
Takahiro G Yamada ◽  
Kazuto Yamazaki ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Hybrid Method ◽  
Neural Differentiation ◽  
Large Scale ◽  
Cell Model ◽  
Random Network ◽  
Computational Simulation ◽  
Characteristic Change ◽  
Large Scale Network ◽  
Scale Network

Abstract Background: Computational simulation using mathematical models is a useful method for understanding the complex behavior of a living system. The majority of studies using mathematical models to reveal biological mechanisms use one of the two main approaches: the bottom-up or the top-down approach. When we aim to analyze a large-scale network, such as a comprehensive knowledge-integrated model of a target phenomenon, for example a whole-cell model, the variation of analyses is limited to particular kind of analysis because of the size and complexity of the model. Results: To analyze a large-scale network of neural differentiation, we developed a hybrid method that combines both approaches. To construct a mathematical model, we extracted network motifs, subgraph structures that recur more often in a metabolic network or gene regulatory network than in a random network, from a large-scale network, detected regulatory motifs among them, and combined these motifs. We confirmed that the model reproduced the known dynamics of HES1 and ASCL1 before and after differentiation, including oscillation and equilibrium of their concentrations. The model also reproduced the effects of overexpression and knockdown of the Id2 gene. Our model suggests that the characteristic change in HES1 and ASCL1 expression in the large-scale network is controlled by a combination of four feedback loops, including a novel large loop discovered in this study. Conclusion: The model extracted by our hybrid method has the potential to reveal the critical mechanisms of neural differentiation. The hybrid method is applicable to other biological events.

scholarly journals Neural differentiation dynamics controlled by multiple feedback loop motifs in a comprehensive molecular interaction network

2019 ◽  
Author(s):  
Tsuyoshi Iwasaki ◽  
Ryo Takiguchi ◽  
Takumi Hiraiwa ◽  
Takahiro G Yamada ◽  
Kazuto Yamazaki ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Hybrid Method ◽  
Neural Differentiation ◽  
Large Scale ◽  
Cell Model ◽  
Random Network ◽  
Computational Simulation ◽  
Characteristic Change ◽  
Large Scale Network ◽  
Scale Network

Abstract Background: Computational simulation using mathematical models is a useful method for understanding the complex behavior of a living system. The majority of studies using mathematical models to reveal biological mechanisms use one of the two main approaches: the bottom-up or the top-down approach. When we aim to analyze a large-scale network, such as a comprehensive knowledge-integrated model of a target phenomenon, for example a whole-cell model, the variation of analyses is limited to particular kind of analysis because of the size and complexity of the model. Results: To analyze a large-scale network of neural differentiation, we developed a hybrid method that combines both approaches. To construct a mathematical model, we extracted network motifs, subgraph structures that recur more often in a metabolic network or gene regulatory network than in a random network, from a large-scale network, detected regulatory motifs among them, and combined these motifs. We confirmed that the model reproduced the known dynamics of HES1 and ASCL1 before and after differentiation, including oscillation and equilibrium of their concentrations. The model also reproduced the effects of overexpression and knockdown of the Id2 gene. Our model suggests that the characteristic change in HES1 and ASCL1 expression in the large-scale network is controlled by a combination of four feedback loops, including a novel large loop discovered in this study. Conclusion: The model extracted by our hybrid method has the potential to reveal the critical mechanisms of neural differentiation. The hybrid method is applicable to other biological events.

Scalings of first-return time for random walks on generalized and weighted transfractal networks

2019 ◽  
Vol 33 (26) ◽  
pp. 1950306
Author(s):  
Qin Liu ◽  
Weigang Sun ◽  
Suyu Liu
Keyword(s):  
Random Walks ◽  
Large Scale ◽  
Probability Generating Function ◽  
Return Time ◽  
Network Size ◽  
Large Network ◽  
Large Scale Network ◽  
Scale Network ◽  
The Mean ◽  
First Return Time

The first-return time (FRT) is an effective measurement of random walks. Presently, it has attracted considerable attention with a focus on its scalings with regard to network size. In this paper, we propose a family of generalized and weighted transfractal networks and obtain the scalings of the FRT for a prescribed initial hub node. By employing the self-similarity of our networks, we calculate the first and second moments of FRT by the probability generating function and obtain the scalings of the mean and variance of FRT with regard to network size. For a large network, the mean FRT scales with the network size at the sublinear rate. Further, the efficiency of random walks relates strongly with the weight factor. The smaller the weight, the better the efficiency bears. Finally, we show that the variance of FRT decreases with more number of initial nodes, implying that our method is more effective for large-scale network size and the estimation of the mean FRT is more reliable.

scholarly journals Large-Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Houxian Zhang ◽  
Zhaolan Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Large Scale Network ◽  
Scale Network

No relevant reports have been reported on the optimization of a large-scale network plan with more than 200 works due to the complexity of the problem and the huge amount of computation. In this paper, an improved particle swarm optimization algorithm via optimization of initial particle swarm (OIPSO) is first explained by the stochastic processes theory. Then two optimization examples are solved using this method which are the optimization of resource-leveling with fixed duration and the optimization of resources constraints with shortest project duration in a large network plan with 223 works. Through these two examples, under the same number of iterations, it is proven that the improved algorithm (OIPSO) can accelerate the optimization speed and improve the optimization effect of particle swarm optimization (PSO).

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network
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