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A comparative survey of integrated learning systems
This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework
On the basic computational structure of gene regulatory networks
Gene regulatory networks constitute the first layer of the cellular
computation for cell adaptation and surveillance. In these webs, a set of
causal relations is built up from thousands of interactions between
transcription factors and their target genes. The large size of these webs and
their entangled nature make difficult to achieve a global view of their
internal organisation. Here, this problem has been addressed through a
comparative study for {\em Escherichia coli}, {\em Bacillus subtilis} and {\em
Saccharomyces cerevisiae} gene regulatory networks. We extract the minimal core
of causal relations, uncovering the hierarchical and modular organisation from
a novel dynamical/causal perspective. Our results reveal a marked top-down
hierarchy containing several small dynamical modules for \textit{E. coli} and
\textit{B. subtilis}. Conversely, the yeast network displays a single but large
dynamical module in the middle of a bow-tie structure. We found that these
dynamical modules capture the relevant wiring among both common and
organism-specific biological functions such as transcription initiation,
metabolic control, signal transduction, response to stress, sporulation and
cell cycle. Functional and topological results suggest that two fundamentally
different forms of logic organisation may have evolved in bacteria and yeast.Comment: This article is published at Molecular Biosystems, Please cite as:
Carlos Rodriguez-Caso, Bernat Corominas-Murtra and Ricard V. Sole. Mol.
BioSyst., 2009, 5 pp 1617--171
Feedbacks from the metabolic network to the genetic network reveal regulatory modules in E. coli and B. subtilis
The genetic regulatory network (GRN) plays a key role in controlling the
response of the cell to changes in the environment. Although the structure of
GRNs has been the subject of many studies, their large scale structure in the
light of feedbacks from the metabolic network (MN) has received relatively
little attention. Here we study the causal structure of the GRNs, namely the
chain of influence of one component on the other, taking into account feedback
from the MN. First we consider the GRNs of E. coli and B. subtilis without
feedback from MN and illustrate their causal structure. Next we augment the
GRNs with feedback from their respective MNs by including (a) links from genes
coding for enzymes to metabolites produced or consumed in reactions catalyzed
by those enzymes and (b) links from metabolites to genes coding for
transcription factors whose transcriptional activity the metabolites alter by
binding to them. We find that the inclusion of feedback from MN into GRN
significantly affects its causal structure, in particular the number of levels
and relative positions of nodes in the hierarchy, and the number and size of
the strongly connected components (SCCs). We then study the functional
significance of the SCCs. For this we identify condition specific feedbacks
from the MN into the GRN by retaining only those enzymes that are essential for
growth in specific environmental conditions simulated via the technique of flux
balance analysis (FBA). We find that the SCCs of the GRN augmented by these
feedbacks can be ascribed specific functional roles in the organism. Our
algorithmic approach thus reveals relatively autonomous subsystems with
specific functionality, or regulatory modules in the organism. This automated
approach could be useful in identifying biologically relevant modules in other
organisms for which network data is available, but whose biology is less well
studied.Comment: 15 figure
Recent progress on the notion of global hyperbolicity
Global hyperbolicity is a central concept in Mathematical Relativity. Here,
we review the different approaches to this concept explaining both, classical
approaches and recent results. The former includes Cauchy hypersurfaces, naked
singularities, and the space of the causal curves connecting two events. The
latter includes structural results on globally hyperbolic spacetimes, their
embeddability in Lorentz-Minkowski, and the recently revised notions of both,
causal and conformal boundaries. Moreover, two criteria for checking global
hyperbolicity are reviewed. The first one applies to general splitting
spacetimes. The second one characterizes accurately global hyperbolicity and
spacelike Cauchy hypersurfaces for standard stationary spacetimes, in terms of
a naturally associated Finsler metric.Comment: 18 pages, 1 figure. Extended and updated contribution to the meeting
"New Developments in Lorentzian Geometry" Berlin, Nov. 200
Graphical models for mediation analysis
Mediation analysis seeks to infer how much of the effect of an exposure on an
outcome can be attributed to specific pathways via intermediate variables or
mediators. This requires identification of so-called path-specific effects.
These express how a change in exposure affects those intermediate variables
(along certain pathways), and how the resulting changes in those variables in
turn affect the outcome (along subsequent pathways). However, unlike
identification of total effects, adjustment for confounding is insufficient for
identification of path-specific effects because their magnitude is also
determined by the extent to which individuals who experience large exposure
effects on the mediator, tend to experience relatively small or large mediator
effects on the outcome. This chapter therefore provides an accessible review of
identification strategies under general nonparametric structural equation
models (with possibly unmeasured variables), which rule out certain such
dependencies. In particular, it is shown which path-specific effects can be
identified under such models, and how this can be done
Bridging the gap between general probabilistic theories and the device-independent framework for nonlocality and contextuality
Characterizing quantum correlations in terms of information-theoretic
principles is a popular chapter of quantum foundations. Traditionally, the
principles adopted for this scope have been expressed in terms of conditional
probability distributions, specifying the probability that a black box produces
a certain output upon receiving a certain input. This framework is known as
"device-independent". Another major chapter of quantum foundations is the
information-theoretic characterization of quantum theory, with its sets of
states and measurements, and with its allowed dynamics. The different
frameworks adopted for this scope are known under the umbrella term "general
probabilistic theories". With only a few exceptions, the two programmes on
characterizing quantum correlations and characterizing quantum theory have so
far proceeded on separate tracks, each one developing its own methods and its
own agenda. This paper aims at bridging the gap, by comparing the two
frameworks and illustrating how the two programmes can benefit each other.Comment: 61 pages, no figures, published versio
Causal structures and causal boundaries
We give an up-to-date perspective with a general overview of the theory of
causal properties, the derived causal structures, their classification and
applications, and the definition and construction of causal boundaries and of
causal symmetries, mostly for Lorentzian manifolds but also in more abstract
settings.Comment: Final version. To appear in Classical and Quantum Gravit
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