Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

We discuss a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized management and prevention of cancer.Comment: 5 figs, related papers, visit lab homepage: http://www.cancer-systemsbiology.org, Seminar in Cancer Biology, 201

Coordinated functional divergence of genes after genome duplication in Arabidopsis thaliana

Gene and genome duplications have been rampant during the evolution of flowering plants. Unlike small-scale gene duplications, whole-genome duplications (WGDs) copy entire pathways or networks, and as such create the unique situation in which such duplicated pathways or networks could evolve novel functionality through the coordinated sub-or neofunctionalization of its constituent genes. Here, we describe a remarkable case of coordinated gene expression divergence following WGDs in Arabidopsis thaliana. We identified a set of 92 homoeologous gene pairs that all show a similar pattern of tissue-specific gene expression divergence following WGD, with one homoeolog showing predominant expression in aerial tissues and the other homoeolog showing biased expression in tip-growth tissues. We provide evidence that this pattern of gene expression divergence seems to involve genes with a role in cell polarity and that likely function in the maintenance of cell wall integrity. Following WGD, many of these duplicated genes evolved separate functions through subfunctionalization in growth/development and stress response. Uncoupling these processes through genome duplications likely provided important adaptations with respect to growth and morphogenesis and defense against biotic and abiotic stress

Probabilistic methods in the analysis of protein interaction networks

Imperial Users onl

Network growth models and genetic regulatory networks

We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterintuitive trends as parameters are varied, including the broadening of indegree distribution when the probability for retaining input links is decreased. We also find that both the scaling of transcription factors with genome size and the measured degree distributions for genes in yeast can be reproduced by the growth algorithm if and only if a special seed is used to initiate the process.Comment: 8 pages with 7 eps figures; uses revtex4. Added references, cleaner figure

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

Yeast Protein Interactome Topology Provides Framework for Coordinated-Functionality

The architecture of the network of protein-protein physical interactions in Saccharomyces cerevisiae is exposed through the combination of two complementary theoretical network measures, betweenness centrality and `Q-modularity'. The yeast interactome is characterized by well-defined topological modules connected via a small number of inter-module protein interactions. Should such topological inter-module connections turn out to constitute a form of functional coordination between the modules, we speculate that this coordination is occurring typically in a pair-wise fashion, rather than by way of high-degree hub proteins responsible for coordinating multiple modules. The unique non-hub-centric hierarchical organization of the interactome is not reproduced by gene duplication-and-divergence stochastic growth models that disregard global selective pressures.Comment: Final, revised version. 13 pages. Please see Nucleic Acids open access article for higher resolution figure