281 research outputs found
All Else Being Equal Be Empowered
The original publication is available at www.springerlink.com . Copyright Springer DOI : 10.1007/11553090_75The classical approach to using utility functions suffers from the drawback of having to design and tweak the functions on a case by case basis. Inspired by examples from the animal kingdom, social sciences and games we propose empowerment, a rather universal function, defined as the information-theoretic capacity of an agent’s actuation channel. The concept applies to any sensorimotoric apparatus. Empowerment as a measure reflects the properties of the apparatus as long as they are observable due to the coupling of sensors and actuators via the environment.Peer reviewe
Information-theoretic analysis of multivariate single - cell signaling responses using SLEMI
Mathematical methods of information theory constitute essential tools to
describe how stimuli are encoded in activities of signaling effectors.
Exploring the information-theoretic perspective, however, remains conceptually,
experimentally and computationally challenging. Specifically, existing
computational tools enable efficient analysis of relatively simple systems,
usually with one input and output only. Moreover, their robust and readily
applicable implementations are missing. Here, we propose a novel algorithm to
analyze signaling data within the framework of information theory. Our approach
enables robust as well as statistically and computationally efficient analysis
of signaling systems with high-dimensional outputs and a large number of input
values. Analysis of the NF-kB single - cell signaling responses to TNF-a
uniquely reveals that the NF-kB signaling dynamics improves discrimination of
high concentrations of TNF-a with a modest impact on discrimination of low
concentrations. Our readily applicable R-package, SLEMI - statistical learning
based estimation of mutual information, allows the approach to be used by
computational biologists with only elementary knowledge of information theory
Information flow and optimization in transcriptional control
In the simplest view of transcriptional regulation, the expression of a gene
is turned on or off by changes in the concentration of a transcription factor
(TF). We use recent data on noise levels in gene expression to show that it
should be possible to transmit much more than just one regulatory bit.
Realizing this optimal information capacity would require that the dynamic
range of TF concentrations used by the cell, the input/output relation of the
regulatory module, and the noise levels of binding and transcription satisfy
certain matching relations. This parameter-free prediction is in good agreement
with recent experiments on the Bicoid/Hunchback system in the early Drosophila
embryo, and this system achieves ~90% of its theoretical maximum information
transmission.Comment: 5 pages, 4 figure
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