Spatial Segregation of BMP/Smad Signaling Affects Osteoblast Differentiation in C2C12 Cells

BACKGROUND: Bone morphogenetic proteins (BMPs) are involved in a plethora of cellular processes in embryonic development and adult tissue homeostasis. Signaling specificity is achieved by dynamic processes involving BMP receptor oligomerization and endocytosis. This allows for spatiotemporal control of Smad dependent and non-Smad pathways. In this study, we investigate the spatiotemporal regulation within the BMP-induced Smad transcriptional pathway. METHODOLOGY/PRINCIPAL FINDINGS: Here we discriminate between Smad signaling events that are dynamin-dependent (i.e., require an intact endocytic pathway) and dynamin-independent. Inhibition of dynamin-dependent endocytosis in fluorescence microscopy and fractionation studies revealed a delay in Smad1/5/8 phosphorylation and nuclear translocation after BMP-2 stimulation of C2C12 cells. Using whole genome microarray and qPCR analysis, we identified two classes of BMP-2 induced genes that are differentially affected by inhibition of endocytosis. Thus, BMP-2 induced gene expression of Id1, Id3, Dlx2 and Hey1 is endocytosis-dependent, whereas BMP-2 induced expression of Id2, Dlx3, Zbtb2 and Krt16 is endocytosis-independent. Furthermore, we demonstrate that short term inhibition of endocytosis interferes with osteoblast differentiation as measured by alkaline phosphatase (ALP) production and qPCR analysis of osteoblast marker gene expression. CONCLUSIONS/SIGNIFICANCE: Our study demonstrates that dynamin-dependent endocytosis is crucial for the concise spatial activation of the BMP-2 induced signaling cascade. Inhibition of endocytic processes during BMP-2 stimulation leads to altered Smad1/5/8 signaling kinetics and results in differential target gene expression. We show that interfering with the BMP-2 induced transcriptional network by endocytosis inhibition results in an attenuation of osteoblast differentiation. This implies that selective sensitivity of gene expression to endocytosis provides an additional mechanism for the cell to respond to BMP in a context specific manner. Moreover, we suggest a novel Smad dependent signal cascade induced by BMP-2, which does not require endocytosis

Simulation and inference for stochastic processes with YUIMA: a comprehensive R framework for SDEs and other stochastic processes

The YUIMA package is the first comprehensive R framework based on S4 classes and methods which allows for the simulation of stochastic differential equations driven by Wiener process, Lévy processes or fractional Brownian motion, as well as CARMA processes. The package performs various central statistical analyses such as quasi maximum likelihood estimation, adaptive Bayes estimation, structural change point analysis, hypotheses testing, asynchronous covariance estimation, lead-lag estimation, LASSO model selection, and so on. YUIMA also supports stochastic numerical analysis by fast computation of the expected value of functionals of stochastic processes through automatic asymptotic expansion by means of the Malliavin calculus. All models can be multidimensional, multiparametric or non parametric.The book explains briefly the underlying theory for simulation and inference of several classes of stochastic processes and then presents both simulation experiments and applications to real data. Although these processes have been originally proposed in physics and more recently in finance, they are becoming popular also in biology due to the fact the time course experimental data are now available. The YUIMA  package, already available on CRAN, can be freely downloaded and this companion book will make the user able to start his or her analysis from the first page. Contains both theory and code with step-by-step examples and figures Uses YUIMA package to implement the latest techniques available in the literature of inference for stochastic processes Shows how to create the description of very abstract models in the same way they are described in theoretical papers but with an extremely easy interface Stefano M. Iacus, PhD, is full professor of statistics the Department of Economics, Management and Quantitative Methods at the University of Milan. He has been a member of the R Core Team (1999-2014) for the development of the R statistical environment and now member of the R Foundation. His research interests include inference for stochastic processes, simulation, computational statistics, causal inference, text mining, and sentiment analysis.  Nakahiro Yoshida, PhD, is a professor at the Graduate School of Mathematical Sciences, University of Tokyo. He is working in theoretical statistics, probability theory, computational statistics, and financial data analysis. He was awarded the Japan Statistical Society Award in 2009 and the Analysis Prize from the Mathematical Society of Japan in 2006

Numerical Analysis of Volatility Change Point Estimators for Discretely Sampled Stochastic Differential Equations

In this paper, we review recent advances on change point estimation for the volatility component of stochastic differential equations under different discrete sampling schemes. We consider both ergodic and non-ergodic cases, and present a Monte Carlo study on the change point estimator to compare the three methods under different setups. Copyright 2010 The Authors Economic Notes 2010 Banca Monte dei Paschi di Siena SpA.

The measurement of the fragment emission angles in the reactions of < 135 MeV/u 12C and 16O in tissue equivalent targets

Different stacks of tissue equivalent targets interleaved with CR-39 nuclear track detectors were irradiated with 135 MeV/u 12C and 16O ions. After track etching and measurement by an automatic image analysis system, a method of trajectory tracing was applied. The trajectories of particles were reconstructed using a pattern-matching method based on a comparison of relative track coordinates.\nFragmentation products have a lower charge and can be clearly distinguished by their smaller track sizes. The track positions within the individual trajectory are additionally fitted to a straight line, from which we can determine the direction of the traversing ion within a path of a few mm. We performed an angular distribution study of the fragments, which we compared to simulation results obtained by a simple particle emission model. For the fitted parameters the model showed agreement within the margins of error but failed to describe the energy dependence and thus should be further improved and upgraded