Arcadia: a visualization tool for metabolic pathways

Summary: Arcadia translates text-based descriptions of biological networks (SBML files) into standardized diagrams (SBGN PD maps). Users can view the same model from different perspectives and easily alter the layout to emulate traditional textbook representations

Sea level variability in the Arctic Ocean from AOMIP models

Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 112 (2007): C04S08, doi:10.1029/2006JC003916.Monthly sea levels from five Arctic Ocean Model Intercomparison Project (AOMIP) models are analyzed and validated against observations in the Arctic Ocean. The AOMIP models are able to simulate variability of sea level reasonably well, but several improvements are needed to reduce model errors. It is suggested that the models will improve if their domains have a minimum depth less than 10 m. It is also recommended to take into account forcing associated with atmospheric loading, fast ice, and volume water fluxes representing Bering Strait inflow and river runoff. Several aspects of sea level variability in the Arctic Ocean are investigated based on updated observed sea level time series. The observed rate of sea level rise corrected for the glacial isostatic adjustment at 9 stations in the Kara, Laptev, and East Siberian seas for 1954–2006 is estimated as 0.250 cm/yr. There is a well pronounced decadal variability in the observed sea level time series. The 5-year running mean sea level signal correlates well with the annual Arctic Oscillation (AO) index and the sea level atmospheric pressure (SLP) at coastal stations and the North Pole. For 1954–2000 all model results reflect this correlation very well, indicating that the long-term model forcing and model reaction to the forcing are correct. Consistent with the influences of AO-driven processes, the sea level in the Arctic Ocean dropped significantly after 1990 and increased after the circulation regime changed from cyclonic to anticyclonic in 1997. In contrast, from 2000 to 2006 the sea level rose despite the stabilization of the AO index at its lowest values after 2000.This research is supported by the National Science Foundation Office of Polar Programs (under cooperative agreements OPP- 0002239 and OPP- 0327664) with the International Arctic Research Center, University of Alaska Fairbanks, and by the Climate Change Prediction Program of the Department of Energy’s Office of Biological and Environmental Research. The development of the UW model is also supported by NASA grants NNG04GB03G and NNG04GH52G and NSF grants OPP-0240916 and OPP-0229429

The Systems Biology Graphical Notation

Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling. © 2009 Nature America, Inc

When do diffusion-limited trajectories become memoryless?

Stochastic description of cellular dynamics by the chemical master equation assumes the exponential distribution of intervals between reaction events. Diffusion-limited reactions violate this assumption. Using the example of the target search we investigate the conditions under which a peaked waiting-time distribution can be approximated by the exponential function. We link the steady-state flux and the dynamic property of the diffusion, the mean first-passage time

Comparison of the dynamics of substrate access channels in three cytochrome P450s reveals different opening mechanisms and a novel functional role for a buried arginine

Understanding the mechanism and specificity of substrate binding in the cytochrome P450 (P450) superfamily is an important step toward explaining its key role in drug metabolism, toxicity, xenobiotic degradation, and several biosynthetic pathways. Here we investigate the ligand exit pathways and mechanisms of P450cam (CYP101), P450BM-3 (CYP102), and P450eryF (CYP107A1) by using random expulsion molecular dynamics and classical molecular dynamics simulations. Although several different pathways are found for each protein, one pathway is common to all three. The mechanism of ligand exit along this pathway is, however, quite different in the three different proteins. For P450cam, small backbone conformational changes, in combination with aromatic side chain rotation, allow for the passage of the rather rigid, compact, and hydrophobic substrate, camphor. In P450BM-3, larger transient backbone changes are observed on ligand exit. R47, situated at the entrance to the channel, appears important in guiding negatively charged fatty acid substrates in and out of the active site. In P450eryF, an isolated buried arginine, R185, stabilized by four hydrogen bonds to backbone carbonyl oxygen atoms, is located in the exit channel and is identified as having a particularly unusual functionality, dynamically gating channel opening. The results for these three P450s suggest that the channel opening mechanisms are adjusted to the physico-chemical properties of the substrate and can kinetically modulate protein-substrate specificity