Modeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity

Takemoto, Kazuhiro; Oosawa, Chikoo

doi:10.1016/j.mbs.2006.11.002

Cited by 22 publications

(15 citation statements)

References 43 publications

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“…[37][38][39][40][41]43,44,46,51,98 For each residue in protein structure, the total number of surrounding residues can be considered as the degree of a node in a network. Based on statistical analysis of residue clusters in native globular proteins, contact degree distribution asymptotically follows a power law P(n cnt ) $ n Àg cnt .…”

Section: Scale-free Residue Cluster Networkmentioning

confidence: 99%

“…33,36 Recently, intensive studies show that many complex networks share two properties: scale-free and high degree of clustering. [37][38][39][40][41][42][43][44][45][46][47][48][49][50][51] Scale-free networks exhibit a power law degree distribution. A preferential attachment mechanism was proposed to explain the emergence of power law distribution.…”

Section: Introductionmentioning

confidence: 99%

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Understanding on the residue contact network using the log‐normal cluster model and the multilevel wheel diagram

Sun

2010

Biopolymers

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Residue clusters play essential role in stabilizing protein structures in the form of complex networks. We show that the cluster sizes in a native protein follow the log-normal distribution for a dataset consisting of 424 proteins. To our knowledge, this is the first time of such fitting for the native structures. Based on log-normal model, the asymptotically increasing mean cluster sizes produce a critical protein chain length of about 200 amino acids, beyond which length most globular proteins have nearly the same mean cluster sizes. This suggests that the larger proteins use a different packing mechanism than the smaller proteins. We confirmed the scale-free property of the residue contact network for most of the protein structures in the dataset, although the violations were observed for the tightly packed proteins. Residue cluster network wheel (RCNW) is proposed to visualize the relationship between the multiple properties of the residue network such as the cluster size, the residue types and contacts, and the flexibility of the residue. We noticed that the residues with large cluster size have smaller Calpha displacement measured using the normal mode analysis.

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Section: Scale-free Residue Cluster Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding on the residue contact network using the log‐normal cluster model and the multilevel wheel diagram

Sun

2010

Biopolymers

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“…More recently the modelling and analysis of massive systems and data sets arising from the uptake of Web 2.0 applications have started to look at network analysis techniques in this regard; taking insights gained through the study of gene regulatory and metabolic networks, as in, for example, [28]. Biological research has provided motivations for dealing with these types of systems, exhibiting the application of dynamic processes on complex networks.…”

Section: Application To Improve Robustness In Load Balancingmentioning

confidence: 99%

Distributed redundancy and robustness in complex systems

Randles

Lamb

Odat

et al. 2011

Journal of Computer and System Sciences

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The uptake and increasing prevalence of Web 2.0 applications, promoting new largescale and complex systems such as Cloud computing and the emerging Internet of Services/Things, requires tools and techniques to analyse and model methods to ensure the robustness of these new systems. This paper reports on assessing and improving complex system resilience using distributed redundancy, termed degeneracy in biological systems, to endow large-scale complicated computer systems with the same robustness that emerges in complex biological and natural systems. However, in order to promote an evolutionary approach, through emergent self-organisation, it is necessary to specify the systems in an 'open-ended' manner where not all states of the system are prescribed at designtime. In particular an observer system is used to select robust topologies, within system components, based on a measurement of the first non-zero Eigen value in the Laplacian spectrum of the components' network graphs; also known as the algebraic connectivity. It is shown, through experimentation on a simulation, that increasing the average algebraic connectivity across the components, in a network, leads to an increase in the variety of individual components termed distributed redundancy; the capacity for structurally distinct components to perform an identical function in a particular context. The results are applied to a specific application where active clustering of like services is used to aid load balancing in a highly distributed network. Using the described procedure is shown to improve performance and distribute redundancy.

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“…Each network motif can perform a specific information processing task such as filtering out spurious input fluctuation, generating temporal programs of expression or accelerating the throughput of the network. (Alon, 2003;Takemoto and Oosawa, 2007).…”

Section: General Properties Of Graphsmentioning

confidence: 99%