All rights reserved.”
“When a person standing upright raises an arm on cue, muscles of the left and right sides of the body exhibit changes prior to and specific to the responding arm. We had standing participants perform a visual lexical decision task (“”is this letter string a word?”"), responding yes by raising one arm and no by raising the other arm. We recorded onset of the arm movement and onset of electromyographic activity in thigh, trunk, and shoulder muscles. We observed BGJ398 the expected

responding arm specificity and found that the onset difference favoring word decisions was evident in similar magnitude at all measurement sites, with the difference at the levels of thigh, trunk and shoulder muscles available 225, 189, and 120 ms, respectively, prior to its manifestation at the level of arm movement. We discuss including (a) whole body reaction time along with event-related potentials in determining the decision-response, brain-body temporal relation and (b) response RepSox price execution along with response initiation in investigating mental chronometry. (C) 2011 Elsevier Ireland Ltd. All rights reserved.”
“Several approaches have been used in the past to model heterogeneity in bacterial cell populations, with each approach focusing on different source(s) of heterogeneity. However, a holistic approach

that integrates all the major sources into a comprehensive framework applicable to cell populations is still lacking.

In this work we present the mathematical formulation of a cell population master equation (CPME) that describes cell population dynamics and takes into account the

major sources of heterogeneity, namely stochasticity in reaction, DNA-duplication, and division, as well as the random buy GSK2118436 partitioning of species contents into the two daughter cells. The formulation also takes into account cell growth and respects the discrete nature of the molecular contents and cell numbers. We further develop a Monte Carlo algorithm for the simulation of the stochastic processes considered here. To benchmark our new framework, we first use it to quantify the effect of each source of heterogeneity on the intrinsic and the extrinsic phenotypic variability for the well-known two-promoter system used experimentally by Elowitz et al. (2002). We finally apply our framework to a more complicated system and demonstrate how the interplay between noisy gene expression and growth inhibition due to protein accumulation at the single cell level can result in complex behavior at the cell population level.

The generality of our framework makes it suitable for studying a vast array of artificial and natural genetic networks. Using our Monte Carlo algorithm, cell population distributions can be predicted for the genetic architecture of interest, thereby quantifying the effect of stochasticity in intracellular reactions or the variability in the rate of physiological processes such as growth and division.