Abstract:
In this work, we present a parallel software package, GeNESiS for the modeling and simulation of the evolution of gene regulatory networks (GRNs). The software models the process of gene regulation through a combination of finite-state and stochastic models. The evolution of GRNs is then simulated by means of a genetic algorithm with the network connections represented as binary strings. The software allows users to simulate the evolution under varying selective pressures and starting conditions. We believe that the software provides a way for researchers to understand the evolutionary behavior of populations of GRNs.We believe that GeNESiS will serve as a useful tool for scientists interested in understanding the evolution of gene regulatory networks under a range of different conditions and selective pressures. Such modeling efforts can lead to a greater understanding of the network characteristics of GRNs.While a lot of interest has been focused on the modeling and simulation of Gene Regulatory Networks (GRNs) in recent years, the evolutionary mechanisms that give rise to GRNs in the first place are still largely unknown. There have been efforts at understanding particular aspects of evolution, such as the correlation between development, evolution and robustness or canalization of the network ([1,2]). Studies on the evolution of GRNs have tended to focus on certain a priori assumptions about the nature of the evolutionary force such as stabilizing selection ([3-6]) or the use of more abstract ([7]) and analytical ([8]) models. Siegal et al. [1] showed that the developmental process constrains the genetic system to produce robustness even in the absence of a selection towards optimum.In an earlier work [9], we developed a framework to analyze the effect of objective functions, input types and starting populations on the evolution of GRNs with a specific emphasis on the robustness of evolved GRNs. We observed that robustness evolves along with the networks as an e

Abstract:
Here, we performed a systematic analysis of transcription promoters and gene expression, as well as of epigenetic histone behaviors, including genomic position, stability within the chromatin, and several modifications. We found that, in humans, broad promoters, but not peak promoters, generally had significant associations with nucleosome positioning and modification. Specifically, around broad promoters histones were highly distributed and aligned in an orderly fashion. This feature was more evident with histones that were methylated or acetylated; moreover, the nucleosome positions around the broad promoters were more stable than those around the peak ones. More strikingly, the overall expression levels of genes associated with broad promoters (but not peak promoters) with modified histones were significantly higher than the levels of genes associated with broad promoters with unmodified histones.These results shed light on how epigenetic regulatory networks of histone modifications are associated with promoter architecture.Recent progress in high-throughput technologies has made it possible to collect a variety of "omics" data on transcripts and on the epigenetic behaviors of the histones that are often associated with these transcripts [1-5].Cap analysis of gene expression (CAGE) is a high-throughput method that enables large-scale identification of transcription start sites (TSSs) of eukaryotic species. This method measures gene expression levels simultaneously with TSS identification by counting the sequenced 5' ends of full-length cDNAs, termed CAGE tags [2,6]. With the development of deep sequencing methods, more high-throughput, and high resolution "tag depth" measurements have become available (DeepCAGE, nanoCAGE and CAGEscan) [1,7]. Such recent whole-cell-level pictures of quantitative transcriptomes have revealed the complex transcriptional network of mammalian species [1,2,6]. According to recent CAGE-based analyses of human TSSs, the human "promotome"

Abstract:
We report on a concerted effort aimed at understanding the origin and history of the pre-solar nanodiamonds in meteorites including the astrophysical sources of the observed isotopic abundance signatures. This includes measurement of light elements by secondary ion mass spectrometry (SIMS), analysis of additional heavy trace elements by accelerator mass spectrometry (AMS) and dynamic calculations of r-process nucleosynthesis with updated nuclear properties. Results obtained indicate: a) there is no evidence for the former presence of now extinct 26Al and 44Ti in our diamond samples other than what can be attributed to silicon carbide and other "impurities"; this does not offer support for a supernova (SN) origin but neither does it negate it; b) analysis by AMS of platinum in "bulk diamond" yields an overabundance of r-only 198Pt that at face value seems more consistent with the neutron burst than with the separation model for the origin of heavy trace elements in the diamonds, although this conclusion is not firm given analytical uncertainties; c) if the Xe-H pattern was established by an unadulterated r-process, it must have been a strong variant of the main r-process, which possibly could also account for the new observations in platinum.

Abstract:
We consider eigenvalue problems for self-adjoint Sturm-Liouville difference equations of any even order. It is well known that such problems with Dirichlet boundary conditions can be transformed into an algebraic eigenvalue problem for a banded, real-symmetric matrix, and vice versa. In this article it is shown that such a transform exists for general separated, self-adjoint boundary conditions also. But the main result is an explicit procedure (algorithm) for the numerical computation of this banded, real-symmetric matrix. This construction can be used for numerical purposes, since in the recent paper by Kratz and Tentler (2008) there is given a stable and superfast algorithm to compute the eigenvalues of banded, real-symmetric matrices. Hence, the Sturm-Liouville problems considered here may now be treated by this algorithm.

Abstract:
We consider eigenvalue problems for self-adjoint Sturm-Liouville difference equations of any even order. It is well known that such problems with Dirichlet boundary conditions can be transformed into an algebraic eigenvalue problem for a banded, real-symmetric matrix, and vice versa. In this article it is shown that such a transform exists for general separated, self-adjoint boundary conditions also. But the main result is an explicit procedure (algorithm) for the numerical computation of this banded, real-symmetric matrix. This construction can be used for numerical purposes, since in the recent paper by Kratz and Tentler (2008) there is given a stable and superfast algorithm to compute the eigenvalues of banded, real-symmetric matrices. Hence, the Sturm-Liouville problems considered here may now be treated by this algorithm.

Abstract:
We study a constrained stochastic control problem with jumps; the jump times of the controlled process are given by a Poisson process. The cost functional comprises quadratic components for an absolutely continuous control and the controlled process and an absolute value component for the control of the jump size of the process. We characterize the value function by a "polynomial" of degree two whose coefficients depend on the state of the system; these coefficients are given by a coupled system of ODEs. The problem hence reduces from solving the Hamilton Jacobi Bellman (HJB) equation (i.e., a PDE) to solving an ODE whose solution is available in closed form. The state space is separated by a time dependent boundary into a continuation region where the optimal jump size of the controlled process is positive and a stopping region where it is zero. We apply the optimization problem to a problem faced by investors in the financial market who have to liquidate a position in a risky asset and have access to a dark pool with adverse selection.

Abstract:
Basel II and Solvency 2 both use the Value-at-Risk (VaR) as the risk measure to compute the Capital Requirements. In practice, to calibrate the VaR, a normal approximation is often chosen for the unknown distribution of the yearly log returns of financial assets. This is usually justified by the use of the Central Limit Theorem (CLT), when assuming aggregation of independent and identically distributed (iid) observations in the portfolio model. Such a choice of modeling, in particular using light tail distributions, has proven during the crisis of 2008/2009 to be an inadequate approximation when dealing with the presence of extreme returns; as a consequence, it leads to a gross underestimation of the risks. The main objective of our study is to obtain the most accurate evaluations of the aggregated risks distribution and risk measures when working on financial or insurance data under the presence of heavy tail and to provide practical solutions for accurately estimating high quantiles of aggregated risks. We explore a new method, called Normex, to handle this problem numerically as well as theoretically, based on properties of upper order statistics. Normex provides accurate results, only weakly dependent upon the sample size and the tail index. We compare it with existing methods.

Abstract:
We performed a clustering of 4,481 promoters according to their surrounding H3K9ac signal and analyzed the clustered promoters for association with different sequence features. The clustering revealed three groups with major H3K9ac signal upstream, centered and downstream of the promoter. Narrow single peak promoters tend to have a concentrated activity of H3K9ac in the upstream region, while broad promoters tend to have a concentrated activity of H3K9ac and RNA polymerase II binding in the centered and downstream regions. A subset of promoters with high gene expression level, compared to subsets with low and medium gene expression, shows dramatic increase in H3K9ac activity in the upstream cluster only; this may indicate that promoters in the centered and downstream clusters are predominantly regulated at post-initiation steps. Furthermore, the upstream cluster is depleted in CpG islands and more likely to regulate un-annotated genes.Clustering core promoters according to their surrounding acetylation signal is a promising approach for the study of histone modifications. When examining promoters clustered into groups according to their surrounding H3K9 acetylation signal, we find that the relative localization and intensity of H3K9ac is very specific depending on characteristic sequence features of the promoter. Experimental data from DeepCAGE and ChIP-chip experiments using undifferentiated (monocyte) and differentiated (macrophage) THP-1 cells leads us to the same conclusions.All human somatic cells contain, in principle, the same genome sequence, a generally static store of information. The regulation of gene expression in each cell, however, is a highly dynamic process, which depends on a complex of factors including the cell cycle phase, the cell type, developmental state, intracellular signalling state, and other factors [1]. Histone modifications are one of the major mechanisms regulating gene expression, acting in combination with other mechanisms such as a