288 research outputs found
A Bivariate Measure of Redundant Information
We define a measure of redundant information based on projections in the
space of probability distributions. Redundant information between random
variables is information that is shared between those variables. But in
contrast to mutual information, redundant information denotes information that
is shared about the outcome of a third variable. Formalizing this concept, and
being able to measure it, is required for the non-negative decomposition of
mutual information into redundant and synergistic information. Previous
attempts to formalize redundant or synergistic information struggle to capture
some desired properties. We introduce a new formalism for redundant information
and prove that it satisfies all the properties necessary outlined in earlier
work, as well as an additional criterion that we propose to be necessary to
capture redundancy. We also demonstrate the behaviour of this new measure for
several examples, compare it to previous measures and apply it to the
decomposition of transfer entropy.Comment: 16 pages, 15 figures, 1 table, added citation to Griffith et al 2012,
Maurer et al 199
The Value of Information for Populations in Varying Environments
The notion of information pervades informal descriptions of biological
systems, but formal treatments face the problem of defining a quantitative
measure of information rooted in a concept of fitness, which is itself an
elusive notion. Here, we present a model of population dynamics where this
problem is amenable to a mathematical analysis. In the limit where any
information about future environmental variations is common to the members of
the population, our model is equivalent to known models of financial
investment. In this case, the population can be interpreted as a portfolio of
financial assets and previous analyses have shown that a key quantity of
Shannon's communication theory, the mutual information, sets a fundamental
limit on the value of information. We show that this bound can be violated when
accounting for features that are irrelevant in finance but inherent to
biological systems, such as the stochasticity present at the individual level.
This leads us to generalize the measures of uncertainty and information usually
encountered in information theory
Integrated information increases with fitness in the evolution of animats
One of the hallmarks of biological organisms is their ability to integrate
disparate information sources to optimize their behavior in complex
environments. How this capability can be quantified and related to the
functional complexity of an organism remains a challenging problem, in
particular since organismal functional complexity is not well-defined. We
present here several candidate measures that quantify information and
integration, and study their dependence on fitness as an artificial agent
("animat") evolves over thousands of generations to solve a navigation task in
a simple, simulated environment. We compare the ability of these measures to
predict high fitness with more conventional information-theoretic processing
measures. As the animat adapts by increasing its "fit" to the world,
information integration and processing increase commensurately along the
evolutionary line of descent. We suggest that the correlation of fitness with
information integration and with processing measures implies that high fitness
requires both information processing as well as integration, but that
information integration may be a better measure when the task requires memory.
A correlation of measures of information integration (but also information
processing) and fitness strongly suggests that these measures reflect the
functional complexity of the animat, and that such measures can be used to
quantify functional complexity even in the absence of fitness data.Comment: 27 pages, 8 figures, one supplementary figure. Three supplementary
video files available on request. Version commensurate with published text in
PLoS Comput. Bio
Cell Lineage Analysis of the Mammalian Female Germline
Fundamental aspects of embryonic and post-natal development, including maintenance of the mammalian female germline, are largely unknown. Here we employ a retrospective, phylogenetic-based method for reconstructing cell lineage trees utilizing somatic mutations accumulated in microsatellites, to study female germline dynamics in mice. Reconstructed cell lineage trees can be used to estimate lineage relationships between different cell types, as well as cell depth (number of cell divisions since the zygote). We show that, in the reconstructed mouse cell lineage trees, oocytes form clusters that are separate from hematopoietic and mesenchymal stem cells, both in young and old mice, indicating that these populations belong to distinct lineages. Furthermore, while cumulus cells sampled from different ovarian follicles are distinctly clustered on the reconstructed trees, oocytes from the left and right ovaries are not, suggesting a mixing of their progenitor pools. We also observed an increase in oocyte depth with mouse age, which can be explained either by depth-guided selection of oocytes for ovulation or by post-natal renewal. Overall, our study sheds light on substantial novel aspects of female germline preservation and development
DNA repair, genome stability and cancer: a historical perspective
The multistep process of cancer progresses over many years. The prevention of mutations by DNA repair pathways led to an early appreciation of a role for repair in cancer avoidance. However, the broader role of the DNA damage response (DDR) emerged more slowly. In this Timeline article, we reflect on how our understanding of the steps leading to cancer developed, focusing on the role of the DDR. We also consider how our current knowledge can be exploited for cancer therapy
Adequacy of terrestrial fauna surveys for the preparation of Environmental Impact Assessments in the mining industry of Western Australia
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