732 research outputs found
Different promoter affinities account for specificity in MYC-dependent gene regulation
Enhanced expression of the MYC transcription factor is observed in the majority of tumors. Two seemingly conflicting models have been proposed for its function: one proposes that MYC enhances expression of all genes, while the other model suggests gene-specific regulation. Here, we have explored the hypothesis that specific gene expression profiles arise since promoters differ in affinity for MYC and high-affinity promoters are fully occupied by physiological levels of MYC. We determined cellular MYC levels and used RNA- and ChIP-sequencing to correlate promoter occupancy with gene expression at different concentrations of MYC. Mathematical modeling showed that binding affinities for interactions of MYC with DNA and with core promoter-bound factors, such as WDR5, are sufficient to explain promoter occupancies observed in vivo. Importantly, promoter affinity stratifies different biological processes that are regulated by MYC, explaining why tumor-specific MYC levels induce specific gene expression programs and alter defined biological properties of cells
Experiencing the world with archetypal symbols: A new form of aesthetics.
According to the theories of symbolic interactionism, phenomenology of perception and archetypes, we argue that symbols play the key role in translating the information from the physical world to the human experience, and archetypes are the universal knowledge of cognition that generates the background of human experience (the life-world). Therefore, we propose a conceptual framework that depicts how people experience the world with symbols, and how archetypes relate the deepest level of human experience. This framework indicates a new direction of research on memory and emotion, and also suggests that archetypal symbolism can be a new resource of aesthetic experience design.Postprint (published version
A Geometric Fractal Growth Model for Scale Free Networks
We introduce a deterministic model for scale-free networks, whose degree
distribution follows a power-law with the exponent . At each time step,
each vertex generates its offsprings, whose number is proportional to the
degree of that vertex with proportionality constant m-1 (m>1). We consider the
two cases: first, each offspring is connected to its parent vertex only,
forming a tree structure, and secondly, it is connected to both its parent and
grandparent vertices, forming a loop structure. We find that both models
exhibit power-law behaviors in their degree distributions with the exponent
. Thus, by tuning m, the degree exponent can be
adjusted in the range, . We also solve analytically a mean
shortest-path distance d between two vertices for the tree structure, showing
the small-world behavior, that is, , where N is
system size, and is the mean degree. Finally, we consider the case
that the number of offsprings is the same for all vertices, and find that the
degree distribution exhibits an exponential-decay behavior
Constrained spin dynamics description of random walks on hierarchical scale-free networks
We study a random walk problem on the hierarchical network which is a
scale-free network grown deterministically. The random walk problem is mapped
onto a dynamical Ising spin chain system in one dimension with a nonlocal spin
update rule, which allows an analytic approach. We show analytically that the
characteristic relaxation time scale grows algebraically with the total number
of nodes as . From a scaling argument, we also show the
power-law decay of the autocorrelation function C_{\bfsigma}(t)\sim
t^{-\alpha}, which is the probability to find the Ising spins in the initial
state {\bfsigma} after time steps, with the state-dependent non-universal
exponent . It turns out that the power-law scaling behavior has its
origin in an quasi-ultrametric structure of the configuration space.Comment: 9 pages, 6 figure
Transition from fractal to non-fractal scalings in growing scale-free networks
Real networks can be classified into two categories: fractal networks and
non-fractal networks. Here we introduce a unifying model for the two types of
networks. Our model network is governed by a parameter . We obtain the
topological properties of the network including the degree distribution,
average path length, diameter, fractal dimensions, and betweenness centrality
distribution, which are controlled by parameter . Interestingly, we show
that by adjusting , the networks undergo a transition from fractal to
non-fractal scalings, and exhibit a crossover from `large' to small worlds at
the same time. Our research may shed some light on understanding the evolution
and relationships of fractal and non-fractal networks.Comment: 7 pages, 3 figures, definitive version accepted for publication in
EPJ
Development of Proficiency Testing for Detection of Irradiated Food: Project E01068. Results of First Round PSL Trials, September 2005
The influence of gene expression time delays on Gierer-Meinhardt pattern formation systems
There are numerous examples of morphogen gradients controlling long range signalling in developmental and cellular systems. The prospect of two such interacting morphogens instigating long range self-organisation in biological systems via a Turing bifurcation has been explored, postulated, or implicated in the context of numerous developmental processes. However, modelling investigations of cellular systems typically neglect the influence of gene expression on such dynamics, even though transcription and translation are observed to be important in morphogenetic systems. In particular, the influence of gene expression on a large class of Turing bifurcation models, namely those with pure kinetics such as the Gierer–Meinhardt system, is unexplored. Our investigations demonstrate that the behaviour of the Gierer–Meinhardt model profoundly changes on the inclusion of gene expression dynamics and is sensitive to the sub-cellular details of gene expression. Features such as concentration blow up, morphogen oscillations and radical sensitivities to the duration of gene expression are observed and, at best, severely restrict the possible parameter spaces for feasible biological behaviour. These results also indicate that the behaviour of Turing pattern formation systems on the inclusion of gene expression time delays may provide a means of distinguishing between possible forms of interaction kinetics. Finally, this study also emphasises that sub-cellular and gene expression dynamics should not be simply neglected in models of long range biological pattern formation via morphogens
Development of Proficiency Testing for Detection of Irradiated Food: Project E01068. Results of Second Round PSL and TL Trials, September 2006
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