We analyze the global structure and evolution of human gene coexpression networks driven by new gene integration. When the Pearson correlation coefficient is greater than or equal to 0.5, we find that the coexpression network consists of 334 small components and one "giant" connected subnet comprising of 6317 interacting genes. This network shows the properties of power-law degree distribution and small-world. The average clustering coefficient of younger genes is larger than that of the elderly genes (0.6685 vs. 0.5762). Particularly, we find that the younger genes with a larger degree also show a property of hierarchical architecture. The younger genes play an important role in the overall pivotability of the network and this network contains few redundant duplicate genes. Moreover, we find that gene duplication and orphan genes are two dominant evolutionary forces in shaping this network. Both the duplicate genes and orphan genes develop new links through a "rich-gets-richer" mechanism. With the gradual integration of new genes into the ancestral network, most of the topological structure features of the network would gradually increase. However, the exponent of degree distribution and modularity coefficient of the whole network do not change significantly, which implies that the evolution of coexpression networks maintains the hierarchical and modular structures in human ancestors.
It is a conventionally held dogma that the genetic basis underlying development is conserved in a long evolutionary time scale. Ample experiments based on mutational, biochemical, functional, and complementary knockdown/knockout approaches have revealed the unexpectedly important role of recently evolved new genes in the development of Drosophila. The recent progress in the genome-wide experimental testing of gene effects and improvements in the computational identification of new genes ( < 40 million years ago, Mya) open the door to investigate the evolution of gene essentiality with a phylogenetically high resolution
Embodiments of the invention are directed to methods of determining the prognosis of a breast cancer patient by evaluating a specified set of genes. Specifically, methods may comprise calculating a prognosis score based on a particular algorithm. Also disclosed are compositions, kits and methods for treating cancer in a subject in need thereof are disclosed involving one or more upstream activators and/or downstream effectors of TET1.
Here we report experimental platforms for driving the cyanobacterial circadian clock both in vivo and in vitro. We find that the phase of the circadian rhythm follows a simple scaling law in light-dark cycles, tracking midday across conditions with variable day length. The core biochemical oscillator comprised of the Kai proteins behaves similarly when driven by metabolic pulses in vitro, indicating that such dynamics are intrinsic to these proteins. We develop a general mathematical framework based on instantaneous transformation of the clock cycle by external cues, and it successfully predicts clock behavior under many cycling environments.
We investigate the geometric structure of a nonequilibrium process and its geodesic solutions. By employing an exactly solvable model of a driven dissipative system (generalized nonautonomous Ornstein-Uhlenbeck process), we compute the time-dependent probability density functions (PDFs) and investigate the evolution of this system in a statistical metric space where the distance between two points (the so-called information length) quantifies the change in information along a trajectory of the PDFs.
There has been increasing awareness in the wider biological community of the role of clonal phenotypic heterogeneity in playing key roles in phenomena such as cellular bet-hedging and decision making, as in the case of the phage-λ lysis/lysogeny and B. Subtilis competence/vegetative pathways. Here, we report on the effect of stochasticity in growth rate, cellular memory/intermittency, and its relation to phenotypic heterogeneity.
The application of gene expression array technology to breast cancer has emphasized the heterogeneity of this disease and also provided new tools to classify breast cancers into subtypes based on gene expression patterns. Ideally each subtype would reflect distinct molecular characteristics corresponding to discrete cancer phenotypes. This information could be used to gain prognostic insight and, eventually, to predict response to therapy.