12,589 research outputs found
Toward a multilevel representation of protein molecules: comparative approaches to the aggregation/folding propensity problem
This paper builds upon the fundamental work of Niwa et al. [34], which
provides the unique possibility to analyze the relative aggregation/folding
propensity of the elements of the entire Escherichia coli (E. coli) proteome in
a cell-free standardized microenvironment. The hardness of the problem comes
from the superposition between the driving forces of intra- and inter-molecule
interactions and it is mirrored by the evidences of shift from folding to
aggregation phenotypes by single-point mutations [10]. Here we apply several
state-of-the-art classification methods coming from the field of structural
pattern recognition, with the aim to compare different representations of the
same proteins gathered from the Niwa et al. data base; such representations
include sequences and labeled (contact) graphs enriched with chemico-physical
attributes. By this comparison, we are able to identify also some interesting
general properties of proteins. Notably, (i) we suggest a threshold around 250
residues discriminating "easily foldable" from "hardly foldable" molecules
consistent with other independent experiments, and (ii) we highlight the
relevance of contact graph spectra for folding behavior discrimination and
characterization of the E. coli solubility data. The soundness of the
experimental results presented in this paper is proved by the statistically
relevant relationships discovered among the chemico-physical description of
proteins and the developed cost matrix of substitution used in the various
discrimination systems.Comment: 17 pages, 3 figures, 46 reference
Generative models of the human connectome
The human connectome represents a network map of the brain's wiring diagram
and the pattern into which its connections are organized is thought to play an
important role in cognitive function. The generative rules that shape the
topology of the human connectome remain incompletely understood. Earlier work
in model organisms has suggested that wiring rules based on geometric
relationships (distance) can account for many but likely not all topological
features. Here we systematically explore a family of generative models of the
human connectome that yield synthetic networks designed according to different
wiring rules combining geometric and a broad range of topological factors. We
find that a combination of geometric constraints with a homophilic attachment
mechanism can create synthetic networks that closely match many topological
characteristics of individual human connectomes, including features that were
not included in the optimization of the generative model itself. We use these
models to investigate a lifespan dataset and show that, with age, the model
parameters undergo progressive changes, suggesting a rebalancing of the
generative factors underlying the connectome across the lifespan.Comment: 38 pages, 5 figures + 19 supplemental figures, 1 tabl
SANA NetGO: A combinatorial approach to using Gene Ontology (GO) terms to score network alignments
Gene Ontology (GO) terms are frequently used to score alignments between
protein-protein interaction (PPI) networks. Methods exist to measure the GO
similarity between two proteins in isolation, but pairs of proteins in a
network alignment are not isolated: each pairing is implicitly dependent upon
every other pairing via the alignment itself. Current methods fail to take into
account the frequency of GO terms across the networks, and attempt to account
for common GO terms in an ad hoc fashion by imposing arbitrary rules on when to
"allow" GO terms based on their location in the GO hierarchy, rather than using
readily available frequency information in the PPI networks themselves. Here we
develop a new measure, NetGO, that naturally weighs infrequent, informative GO
terms more heavily than frequent, less informative GO terms, without requiring
arbitrary cutoffs. In particular, NetGO down-weights the score of frequent GO
terms according to their frequency in the networks being aligned. This is a
global measure applicable only to alignments, independent of pairwise GO
measures, in the same sense that the edge-based EC or S3 scores are global
measures of topological similarity independent of pairwise topological
similarities. We demonstrate the superiority of NetGO by creating alignments of
predetermined quality based on homologous pairs of nodes and show that NetGO
correlates with alignment quality much better than any existing GO-based
alignment measures. We also demonstrate that NetGO provides a measure of
taxonomic similarity between species, consistent with existing taxonomic
measures--a feature not shared with existing GO-based network alignment
measures. Finally, we re-score alignments produced by almost a dozen aligners
from a previous study and show that NetGO does a better job than existing
measures at separating good alignments from bad ones
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
Topology regulates pattern formation capacity of binary cellular automata on graphs
We study the effect of topology variation on the dynamic behavior of a system
with local update rules. We implement one-dimensional binary cellular automata
on graphs with various topologies by formulating two sets of degree-dependent
rules, each containing a single parameter. We observe that changes in graph
topology induce transitions between different dynamic domains (Wolfram classes)
without a formal change in the update rule. Along with topological variations,
we study the pattern formation capacities of regular, random, small-world and
scale-free graphs. Pattern formation capacity is quantified in terms of two
entropy measures, which for standard cellular automata allow a qualitative
distinction between the four Wolfram classes. A mean-field model explains the
dynamic behavior of random graphs. Implications for our understanding of
information transport through complex, network-based systems are discussed.Comment: 16 text pages, 13 figures. To be published in Physica
Revealing networks from dynamics: an introduction
What can we learn from the collective dynamics of a complex network about its
interaction topology? Taking the perspective from nonlinear dynamics, we
briefly review recent progress on how to infer structural connectivity (direct
interactions) from accessing the dynamics of the units. Potential applications
range from interaction networks in physics, to chemical and metabolic
reactions, protein and gene regulatory networks as well as neural circuits in
biology and electric power grids or wireless sensor networks in engineering.
Moreover, we briefly mention some standard ways of inferring effective or
functional connectivity.Comment: Topical review, 48 pages, 7 figure
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