Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure

Regulation of signal duration and the statistical dynamics of kinase activation by scaffold proteins

Scaffolding proteins that direct the assembly of multiple kinases into a spatially localized signaling complex are often essential for the maintenance of an appropriate biological response. Although scaffolds are widely believed to have dramatic effects on the dynamics of signal propagation, the mechanisms that underlie these consequences are not well understood. Here, Monte Carlo simulations of a model kinase cascade are used to investigate how the temporal characteristics of signaling cascades can be influenced by the presence of scaffold proteins. Specifically, we examine the effects of spatially localizing kinase components on a scaffold on signaling dynamics. The simulations indicate that a major effect that scaffolds exert on the dynamics of cell signaling is to control how the activation of protein kinases is distributed over time. Scaffolds can influence the timing of kinase activation by allowing for kinases to become activated over a broad range of times, thus allowing for signaling at both early and late times. Scaffold concentrations that result in optimal signal amplitude also result in the broadest distributions of times over which kinases are activated. These calculations provide insights into one mechanism that describes how the duration of a signal can potentially be regulated in a scaffold mediated protein kinase cascade. Our results illustrate another complexity in the broad array of control properties that emerge from the physical effects of spatially localizing components of kinase cascades on scaffold proteins.Comment: 12 pages, 6 figure

Evolution of scaling emergence in large-scale spatial epidemic spreading

Background: Zipf's law and Heaps' law are two representatives of the scaling concepts, which play a significant role in the study of complexity science. The coexistence of the Zipf's law and the Heaps' law motivates different understandings on the dependence between these two scalings, which is still hardly been clarified. Methodology/Principal Findings: In this article, we observe an evolution process of the scalings: the Zipf's law and the Heaps' law are naturally shaped to coexist at the initial time, while the crossover comes with the emergence of their inconsistency at the larger time before reaching a stable state, where the Heaps' law still exists with the disappearance of strict Zipf's law. Such findings are illustrated with a scenario of large-scale spatial epidemic spreading, and the empirical results of pandemic disease support a universal analysis of the relation between the two laws regardless of the biological details of disease. Employing the United States(U.S.) domestic air transportation and demographic data to construct a metapopulation model for simulating the pandemic spread at the U.S. country level, we uncover that the broad heterogeneity of the infrastructure plays a key role in the evolution of scaling emergence. Conclusions/Significance: The analyses of large-scale spatial epidemic spreading help understand the temporal evolution of scalings, indicating the coexistence of the Zipf's law and the Heaps' law depends on the collective dynamics of epidemic processes, and the heterogeneity of epidemic spread indicates the significance of performing targeted containment strategies at the early time of a pandemic disease.Comment: 24pages, 7figures, accepted by PLoS ON

The DAC system and associations with acute leukemias and myelodysplastic syndromes

Imbalances of histone acetyltransferase (HAT) and deacetylase activity (DAC) that result in deregulated gene expression are commonly observed in leukemias. These alterations provide the basis for novel therapeutic approaches that target the epigenetic mechanisms implicated in leukemogenesis. As the acetylation status of histones has been linked to transcriptional regulation of genes involved particularly in differentiation and apoptosis, DAC inhibitors (DACi) have attracted considerable attention for treatment of hematologic malignancies. DACi encompass a structurally diverse family of compounds that are being explored as single agents as well as in combination with chemotherapeutic drugs, small molecule inhibitors of signaling pathways and hypomethylating agents. While DACi have shown clear evidence of activity in acute myeloid leukemia, myelodysplastic syndromes and lymphoid malignancies, their precise role in treatment of these different entities remain to be elucidated. Successful development of these compounds as elements of novel targeted treatment strategies for leukemia will require that clinical studies be performed in conjunction with translational research including efforts to identify predictive biomarkers

Further phenotypic characterization of the primitive lineage− CD34+CD38−CD90+CD45RA− hematopoietic stem cell/progenitor cell sub-population isolated from cord blood, mobilized peripheral blood and patients with chronic myelogenous leukemia

The most primitive hematopoietic stem cell (HSC)/progenitor cell (PC) population reported to date is characterized as being Lin−CD34+CD38−CD90+CD45R. We have a long-standing interest in comparing the characteristics of hematopoietic progenitor cell populations enriched from normal subjects and patients with chronic myelogenous leukemia (CML). In order to investigate further purification of HSCs and for potential targetable differences between the very primitive normal and CML stem/PCs, we have phenotypically compared the normal and CML Lin−CD34+CD38−CD90+CD45RA− HSC/PC populations. The additional antigens analyzed were HLA-DR, the receptor tyrosine kinases c-kit and Tie2, the interleukin-3 cytokine receptor, CD33 and the activation antigen CD69, the latter of which was recently reported to be selectively elevated in cell lines expressing the Bcr-Abl tyrosine kinase. Notably, we found a strikingly low percentage of cells from the HSC/PC sub-population isolated from CML patients that were found to express the c-kit receptor (<1%) compared with the percentages of HSC/PCs expressing the c-kitR isolated from umbilical cord blood (50%) and mobilized peripheral blood (10%). Surprisingly, Tie2 receptor expression within the HSC/PC subset was extremely low from both normal and CML samples. Using in vivo transplantation studies, we provide evidence that HLA-DR, c-kitR, Tie2 and IL-3R may not be suitable markers for further partitioning of HSCs from the Lin−CD34+CD38−CD90+CD45RA− sub-population