Flexible and Robust Networks

@article{Vakulenko2012FlexibleAR,
  title={Flexible and Robust Networks},
  author={Sergei Vakulenko and Ovidiu Radulescu},
  journal={Journal of bioinformatics and computational biology},
  year={2012},
  volume={10 2},
  pages={
          1241011
        }
}
We consider networks with two types of nodes. The v-nodes, called centers, are hyperconnected and interact with one another via many u-nodes, called satellites. This centralized architecture, widespread in gene networks, possesses two fundamental properties. Namely, this organization creates feedback loops that are capable of generating practically any prescribed patterning dynamics, chaotic or periodic, or having a number of equilibrium states. Moreover, this organization is robust with… 

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References

SHOWING 1-10 OF 47 REFERENCES
Boolean dynamics of networks with scale-free topology
Statistical mechanics of complex networks
TLDR
A simple model based on these two principles was able to reproduce the power-law degree distribution of real networks, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network.
Approximation of dynamical systems by continuous time recurrent neural networks
Complex Networks: from Graph Theory to Biology
TLDR
The aim of this text is to show the central role played by networks in complex system science, with a recent work on the influence of network topology on the dynamics of coupled excitable units and the insights it provides about network emerging features, robustness of network behaviors, and the notion of static or dynamic motif.
Models of biological pattern formation
TLDR
The aim of the seminar is to demonstrate that it is possible to formulate models in a mathematical precise way that describe essential steps in spite of the appearing complexity of this process.
Hierarchical modularity of nested bow-ties in metabolic networks
TLDR
The highly modularized bow-tie pattern is present at different levels and scales, and in different chemical and spatial modules of metabolic networks, which is likely the result of the evolutionary process rather than a random accident.
Metabolic stability and epigenesis in randomly constructed genetic nets.
Neural networks and physical systems with emergent collective computational abilities.
  • J. Hopfield
  • Computer Science
    Proceedings of the National Academy of Sciences of the United States of America
  • 1982
TLDR
A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Mathematical biology
TLDR
The aim of this study was to investigate the bifurcations and attractors of the nonlinear dynamics model of the saccadic system, in order to obtain a classification of the simulated oculomotor behaviours.
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