Probability Models for Degree Distributions of Protein Interaction Networks

The degree distribution of many biological and technological networks has been described as a power-law distribution. While the degree distribution does not capture all aspects of a network, it has often been suggested that its functional form contains important clues as to underlying evolutionary processes that have shaped the network. Generally, the functional form for the degree distribution has been determined in an ad-hoc fashion, with clear power-law like behaviour often only extending over a limited range of connectivities. Here we apply formal model selection techniques to decide which probability distribution best describes the degree distributions of protein interaction networks. Contrary to previous studies this well defined approach suggests that the degree distribution of many molecular networks is often better described by distributions other than the popular power-law distribution. This, in turn, suggests that simple, if elegant, models may not necessarily help in the quantitative understanding of complex biological processes.

ABC-SysBio-approximate Bayesian computation in Python with GPU support.

Motivation: The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions. Results: Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework. ABC-SysBio combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection. It is designed to work with models written in Systems Biology Markup Language (SBML). Deterministic and stochastic models can be analyzed in ABC-SysBio

Anisotropy of Growth of the Close-Packed Surfaces of Silver

The growth morphology of clean silver exhibits a profound anisotropy: The growing surface of Ag(111) is typically very rough while that of Ag(100) is smooth and flat. This serious and important difference is unexpected, not understood, and hitherto not observed for any other metal. Using density functional theory calculations of self-diffusion on flat and stepped Ag(100) we find, for example, that at flat regions a hopping mechanism is favored, while across step edges diffusion proceeds by an exchange process. The calculated microscopic parameters explain the experimentally reported growth properties.Comment: RevTeX, 4 pages, 3 figures in uufiles form, to appear in Phys. Rev. Let

Atomistic modelling of large-scale metal film growth fronts

We present simulations of metallization morphologies under ionized sputter deposition conditions, obtained by a new theoretical approach. By means of molecular dynamics simulations using a carefully designed interaction potential, we analyze the surface adsorption, reflection, and etching reactions taking place during Al physical vapor deposition, and calculate their relative probability. These probabilities are then employed in a feature-scale cellular-automaton simulator, which produces calculated film morphologies in excellent agreement with scanning-electron-microscopy data on ionized sputter deposition.Comment: RevTeX 4 pages, 2 figure

PV-0327 Patient-specific motion management and adaptive respiratory gating in Pancreatic SBRT

Digitalitzat per Artypla