Basic Spin Properties and the Bloch Equations

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145288/1/cpmib0300.pd

Are scale-free networks robust to measurement errors?

BACKGROUND: Many complex random networks have been found to be scale-free. Existing literature on scale-free networks has rarely considered potential false positive and false negative links in the observed networks, especially in biological networks inferred from high-throughput experiments. Therefore, it is important to study the impact of these measurement errors on the topology of the observed networks. RESULTS: This article addresses the impact of erroneous links on network topological inference and explores possible error mechanisms for scale-free networks with an emphasis on Saccharomyces cerevisiae protein interaction networks. We study this issue by both theoretical derivations and simulations. We show that the ignorance of erroneous links in network analysis may lead to biased estimates of the scale parameter and recommend robust estimators in such scenarios. Possible error mechanisms of yeast protein interaction networks are explored by comparisons between real data and simulated data. CONCLUSION: Our studies show that, in the presence of erroneous links, the connectivity distribution of scale-free networks is still scale-free for the middle range connectivities, but can be greatly distorted for low and high connecitivities. It is more appropriate to use robust estimators such as the least trimmed mean squares estimator to estimate the scale parameter γ under such circumstances. Moreover, we show by simulation studies that the scale-free property is robust to some error mechanisms but untenable to others. The simulation results also suggest that different error mechanisms may be operating in the yeast protein interaction networks produced from different data sources. In the MIPS gold standard protein interaction data, there appears to be a high rate of false negative links, and the false negative and false positive rates are more or less constant across proteins with different connectivities. However, the error mechanism of yeast two-hybrid data may be very different, where the overall false negative rate is low and the false negative rates tend to be higher for links involving proteins with more interacting partners

Basic Spin Properties and the Bloch Equations

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145203/1/cpmib0300.pd

Change point analysis of histone modifications reveals epigenetic blocks linking to physical domains

Histone modification is a vital epigenetic mechanism for transcriptional control in eukaryotes. High-throughput techniques have enabled whole-genome analysis of histone modifications in recent years. However, most studies assume one combination of histone modification invariantly translates to one transcriptional output regardless of local chromatin environment. In this study we hypothesize that, the genome is organized into local domains that manifest similar enrichment pattern of histone modification, which leads to orchestrated regulation of expression of genes with relevant biological functions. We propose a multivariate Bayesian Change Point (BCP) model to segment the Drosophila melanogaster genome into consecutive blocks on the basis of combinatorial patterns of histone marks. By modeling the sparse distribution of histone marks with a zero-inflated Gaussian mixture, our partitions capture local BLOCKs that manifest relatively homogeneous enrichment pattern of histone marks. We further characterized BLOCKs by their transcription levels, distribution of genes, degree of co-regulation and GO enrichment. Our results demonstrate that these BLOCKs, although inferred merely from histone modifications, reveal strong relevance with physical domains, which suggests their important roles in chromatin organization and coordinated gene regulation

International Monetary Power and China’s Response

International monetary power has become a new area of interest since the outbreak of the global financial crisis in 2008. The USA, the EU and China will pursue global monetary power in the future, and the USD, the EURO and the RMB will become the most important world reserve currencies. The pattern of international monetary power will shift from a unilateral-hegemony structure to a triangle-balance structure. This shift has a very strong influence on changes in the current international political system. As the largest emerging economy, China will speed up the globalization of the RMB to meet the challenges of the international monetary system