Biomedical subjects

Soon-Hyung Yook

Publications and source records attributed to Soon-Hyung Yook.

4 recordsLinked to original sources

PubMed2006

Diffusive capture process on complex networks.

We study the dynamical properties of a diffusing lamb captured by a diffusing lion on the complex networks with various sizes of N. We find that the lifetime {T} of a lamb scales as {T} approximately N and the survival probability S(N-->infinity, t) becomes finite on scale-free networks with degree exponent gamma > 3. However, S(N, t) for gamma < 3 has a long-living tail on tree-structured scale-free networks and decays exponentially on looped scale-free networks. This suggests that the second moment of degree distribution {k2} is the relevant factor for the dynamical properties in the diffusive capture process. We numerically find that the normalized number of capture events at a node with degree k, n(k), decreases as n(k) approximately k(-sigma). When gamma < 3, n(k) still increases anomalously for k approximately kmax, where kmax is the maximum value of k of given networks with size N. We analytically show that n(k) satisfies the relation n(k) approximately {k2}P(k) for any degree distribution P(k) and the total number of capture events Ntot is proportional to {k2}, which causes the gamma -dependent behavior of S(N, t) and {T}.

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PubMed2005

Self-similar scale-free networks and disassortativity.

Self-similar networks with scale-free degree distribution have recently attracted much attention, since these apparently incompatible properties were reconciled in [C. Song, S. Havlin, and H. A. Makse, Nature 433, 392 (2005)] by an appropriate box-counting method that enters the measurement of the fractal dimension. We study two genetic regulatory networks (Saccharomyces cerevisiae [N. M. Luscombe, M. M. Babu, H. Yu, M. Snyder, S. Teichmann, and M. Gerstein, Nature 431, 308 (2004)] and Escherichia coli [http://www.ccg.unam.mx/Computational_Genomics/regulondb/DataSets/RegulonNetDataSets.html and http://www.gbf.de/SystemsBiology]) and show their self-similar and scale-free features, in extension to the datasets studied by [C. Song, S. Havlin, and H. A. Makse, Nature 433, 392 (2005)]. Moreover, by a number of numerical results we support the conjecture that self-similar scale-free networks are not assortative. From our simulations so far these networks seem to be disassortative instead. We also find that the qualitative feature of disassortativity is scale-invariant under renormalization, but it appears as an intrinsic feature of the renormalization prescription, as even assortative networks become disassortative after a sufficient number of renormalization steps.

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PubMed2004

Functional and topological characterization of protein interaction networks.

The elucidation of the cell's large-scale organization is a primary challenge for post-genomic biology, and understanding the structure of protein interaction networks offers an important starting point for such studies. We compare four available databases that approximate the protein interaction network of the yeast, Saccharomyces cerevisiae, aiming to uncover the network's generic large-scale properties and the impact of the proteins' function and cellular localization on the network topology. We show how each database supports a scale-free, topology with hierarchical modularity, indicating that these features represent a robust and generic property of the protein interactions network. We also find strong correlations between the network's structure and the functional role and subcellular localization of its protein constituents, concluding that most functional and/or localization classes appear as relatively segregated subnetworks of the full protein interaction network. The uncovered systematic differences between the four protein interaction databases reflect their relative coverage for different functional and localization classes and provide a guide for their utility in various bioinformatics studies.

Algorithms↗

PubMed2002

Modeling the Internet's large-scale topology.

Network generators that capture the Internet's large-scale topology are crucial for the development of efficient routing protocols and modeling Internet traffic. Our ability to design realistic generators is limited by the incomplete understanding of the fundamental driving forces that affect the Internet's evolution. By combining several independent databases capturing the time evolution, topology, and physical layout of the Internet, we identify the universal mechanisms that shape the Internet's router and autonomous system level topology. We find that the physical layout of nodes form a fractal set, determined by population density patterns around the globe. The placement of links is driven by competition between preferential attachment and linear distance dependence, a marked departure from the currently used exponential laws. The universal parameters that we extract significantly restrict the class of potentially correct Internet models and indicate that the networks created by all available topology generators are fundamentally different from the current Internet.

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