Fundamental limits on the suppression of molecular fluctuations.

Negative feedback is common in biological processes and can increase a system's stability to internal and external perturbations. But at the molecular level, control loops always involve signalling steps with finite rates for random births and deaths of individual molecules. Here we show, by developing mathematical tools that merge control and information theory with physical chemistry, that seemingly mild constraints on these rates place severe limits on the ability to suppress molecular fluctuations. Specifically, the minimum standard deviation in abundances decreases with the quartic root of the number of signalling events, making it extremely expensive to increase accuracy. Our results are formulated in terms of experimental observables, and existing data show that cells use brute force when noise suppression is essential; for example, regulatory genes are transcribed tens of thousands of times per cell cycle. The theory challenges conventional beliefs about biochemical accuracy and presents an approach to the rigorous analysis of poorly characterized biological systems.

Attenuation of self-generated tactile sensations is predictive, not postdictive.

When one finger touches the other, the resulting tactile sensation is perceived as weaker than the same stimulus externally imposed. This attenuation of sensation could result from a predictive process that subtracts the expected sensory consequences of the action, or from a postdictive process that alters the perception of sensations that are judged after the event to be self-generated. In this study we observe attenuation even when the fingers unexpectedly fail to make contact, supporting a predictive process. This predictive attenuation of self-generated sensation may have evolved to enhance the perception of sensations with an external cause.

On-chip deposition of carbon nanotubes using CMOS microhotplates.

The direct deposition of carbon nanotubes on CMOS microhotplates is demonstrated in this paper. Tungsten microhotplates, fabricated on thin SOI membranes aside CMOS control circuitry, are used to locally grow carbon nanotubes by chemical vapour deposition. Unlike bulk heating of the entire chip, which could cause degradation to CMOS devices and interconnects due to high growth temperatures in excess of 500 °C, this novel technique allows carbon nanotubes to be grown on-chip in localized regions. The microfabricated heaters are thermally isolated from the rest of the CMOS chip as they are on the membranes. This allows carbon nanotubes to be grown alongside CMOS circuitry on the same wafer without any external heating, thus enabling new applications (e.g. smart gas sensing) where the integration of CMOS and carbon nanotubes is required.

A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series.

Understanding the regulatory mechanisms that are responsible for an organism's response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates, and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 observed time points. In classification experiments, our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.