Intrinsically disordered proteins and conformational noise Implications in cancer

Intrinsically disordered proteins, IDPs, are proteins that lack a rigid 3D structure under physiological conditions, at least in vitro. Despite the lack of structure, IDPs play important roles in biological processes and transition from disorder to order upon binding to their targets. With multiple conformational states and rapid conformational dynamics, they engage in myriad and often ``promiscuous'' interactions. These stochastic interactions between IDPs and their partners, defined here as conformational noise, is an inherent characteristic of IDP interactions. The collective effect of conformational noise is an ensemble of protein network configurations, from which the most suitable can be explored in response to perturbations, conferring protein networks with remarkable flexibility and resilience. Moreover, the ubiquitous presence of IDPs as transcriptional factors and, more generally, as hubs in protein networks, is indicative of their role in propagation of transcriptional (genetic) noise. As effectors of transcriptional and conformational noise, IDPs rewire protein networks and unmask latent interactions in response to perturbations. Thus, noise-driven activation of latent pathways could underlie state-switching events such as cellular transformation in cancer. To test this hypothesis, we created a model of a protein network with the topological characteristics of a cancer protein network and tested its response to a perturbation in presence of IDP hubs and conformational noise. Because numerous IDPs are found to be epigenetic modifiers and chromatin remodelers, we hypothesize that they could further channel noise into stable, heritable genotypic changes.

Characterizing drought using the reliability-resilience-vulnerability concept

This study borrows the measures developed for the operation of water resources systems as a means of characterizing droughts in a given region. It is argued that the common approach of assessing drought using a univariate measure (severity or reliability) is inadequate as decision makers need assessment of the other facets considered here. It is proposed that the joint distribution of reliability, resilience, and vulnerability (referred to as RRV in a reservoir operation context), assessed using soil moisture data over the study region, be used to characterize droughts. Use is made of copulas to quantify the joint distribution between these variables. As reliability and resilience vary in a nonlinear but almost deterministic way, the joint probability distribution of only resilience and vulnerability is modeled. Recognizing the negative association between the two variables, a Plackett copula is used to formulate the joint distribution. The developed drought index, referred to as the drought management index (DMI), is able to differentiate the drought proneness of a given area when compared to other areas. An assessment of the sensitivity of the DMI to the length of the data segments used in evaluation indicates relative stability is achieved if the data segments are 5years or longer. The proposed approach is illustrated with reference to the Malaprabha River basin in India, using four adjoining Climate Prediction Center grid cells of soil moisture data that cover an area of approximately 12,000 km(2). (C) 2013 American Society of Civil Engineers.

Spatially Adaptive Kernel Regression Using Risk Estimation

An important question in kernel regression is one of estimating the order and bandwidth parameters from available noisy data. We propose to solve the problem within a risk estimation framework. Considering an independent and identically distributed (i.i.d.) Gaussian observations model, we use Stein's unbiased risk estimator (SURE) to estimate a weighted mean-square error (MSE) risk, and optimize it with respect to the order and bandwidth parameters. The two parameters are thus spatially adapted in such a manner that noise smoothing and fine structure preservation are simultaneously achieved. On the application side, we consider the problem of image restoration from uniform/non-uniform data, and show that the SURE approach to spatially adaptive kernel regression results in better quality estimation compared with its spatially non-adaptive counterparts. The denoising results obtained are comparable to those obtained using other state-of-the-art techniques, and in some scenarios, superior.