149 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
Stability of the splay state in pulse--coupled networks
The stability of the dynamical states characterized by a uniform firing rate
({\it splay states}) is analyzed in a network of globally coupled leaky
integrate-and-fire neurons. This is done by reducing the set of differential
equations to a map that is investigated in the limit of large network size. We
show that the stability of the splay state depends crucially on the ratio
between the pulse--width and the inter-spike interval. More precisely, the
spectrum of Floquet exponents turns out to consist of three components: (i) one
that coincides with the predictions of the mean-field analysis [Abbott-van
Vreesvijk, 1993]; (ii) a component measuring the instability of
"finite-frequency" modes; (iii) a number of "isolated" eigenvalues that are
connected to the characteristics of the single pulse and may give rise to
strong instabilities (the Floquet exponent being proportional to the network
size). Finally, as a side result, we find that the splay state can be stable
even for inhibitory coupling.Comment: 13 pages, 10 figures, submitted for pubblication to Physical Review
A generative model for protein contact networks
In this paper we present a generative model for protein contact networks. The
soundness of the proposed model is investigated by focusing primarily on
mesoscopic properties elaborated from the spectra of the graph Laplacian. To
complement the analysis, we study also classical topological descriptors, such
as statistics of the shortest paths and the important feature of modularity.
Our experiments show that the proposed model results in a considerable
improvement with respect to two suitably chosen generative mechanisms,
mimicking with better approximation real protein contact networks in terms of
diffusion properties elaborated from the Laplacian spectra. However, as well as
the other considered models, it does not reproduce with sufficient accuracy the
shortest paths structure. To compensate this drawback, we designed a second
step involving a targeted edge reconfiguration process. The ensemble of
reconfigured networks denotes improvements that are statistically significant.
As a byproduct of our study, we demonstrate that modularity, a well-known
property of proteins, does not entirely explain the actual network architecture
characterizing protein contact networks. In fact, we conclude that modularity,
intended as a quantification of an underlying community structure, should be
considered as an emergent property of the structural organization of proteins.
Interestingly, such a property is suitably optimized in protein contact
networks together with the feature of path efficiency.Comment: 18 pages, 67 reference
Multifractal Characterization of Protein Contact Networks
The multifractal detrended fluctuation analysis of time series is able to
reveal the presence of long-range correlations and, at the same time, to
characterize the self-similarity of the series. The rich information derivable
from the characteristic exponents and the multifractal spectrum can be further
analyzed to discover important insights about the underlying dynamical process.
In this paper, we employ multifractal analysis techniques in the study of
protein contact networks. To this end, initially a network is mapped to three
different time series, each of which is generated by a stationary unbiased
random walk. To capture the peculiarities of the networks at different levels,
we accordingly consider three observables at each vertex: the degree, the
clustering coefficient, and the closeness centrality. To compare the results
with suitable references, we consider also instances of three well-known
network models and two typical time series with pure monofractal and
multifractal properties. The first result of notable interest is that time
series associated to proteins contact networks exhibit long-range correlations
(strong persistence), which are consistent with signals in-between the typical
monofractal and multifractal behavior. Successively, a suitable embedding of
the multifractal spectra allows to focus on ensemble properties, which in turn
gives us the possibility to make further observations regarding the considered
networks. In particular, we highlight the different role that small and large
fluctuations of the considered observables play in the characterization of the
network topology
Heterogeneous Mean Field for neural networks with short term plasticity
We report about the main dynamical features of a model of leaky-integrate-and
fire excitatory neurons with short term plasticity defined on random massive
networks. We investigate the dynamics by a Heterogeneous Mean-Field formulation
of the model, that is able to reproduce dynamical phases characterized by the
presence of quasi-synchronous events. This formulation allows one to solve also
the inverse problem of reconstructing the in-degree distribution for different
network topologies from the knowledge of the global activity field. We study
the robustness of this inversion procedure, by providing numerical evidence
that the in-degree distribution can be recovered also in the presence of noise
and disorder in the external currents. Finally, we discuss the validity of the
heterogeneous mean-field approach for sparse networks, with a sufficiently
large average in-degree
Average synaptic activity and neural networks topology: a global inverse problem
The dynamics of neural networks is often characterized by collective behavior
and quasi-synchronous events, where a large fraction of neurons fire in short
time intervals, separated by uncorrelated firing activity. These global
temporal signals are crucial for brain functioning. They strongly depend on the
topology of the network and on the fluctuations of the connectivity. We propose
a heterogeneous mean--field approach to neural dynamics on random networks,
that explicitly preserves the disorder in the topology at growing network
sizes, and leads to a set of self-consistent equations. Within this approach,
we provide an effective description of microscopic and large scale temporal
signals in a leaky integrate-and-fire model with short term plasticity, where
quasi-synchronous events arise. Our equations provide a clear analytical
picture of the dynamics, evidencing the contributions of both periodic (locked)
and aperiodic (unlocked) neurons to the measurable average signal. In
particular, we formulate and solve a global inverse problem of reconstructing
the in-degree distribution from the knowledge of the average activity field.
Our method is very general and applies to a large class of dynamical models on
dense random networks
Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins
In this paper, we study the structure and dynamical properties of protein
contact networks with respect to other biological networks, together with
simulated archetypal models acting as probes. We consider both classical
topological descriptors, such as the modularity and statistics of the shortest
paths, and different interpretations in terms of diffusion provided by the
discrete heat kernel, which is elaborated from the normalized graph Laplacians.
A principal component analysis shows high discrimination among the network
types, either by considering the topological and heat kernel based vector
characterizations. Furthermore, a canonical correlation analysis demonstrates
the strong agreement among those two characterizations, providing thus an
important justification in terms of interpretability for the heat kernel.
Finally, and most importantly, the focused analysis of the heat kernel provides
a way to yield insights on the fact that proteins have to satisfy specific
structural design constraints that the other considered networks do not need to
obey. Notably, the heat trace decay of an ensemble of varying-size proteins
denotes subdiffusion, a peculiar property of proteins
Differential neural dynamics underling pragmatic and semantic affordance processing in macaque ventral premotor cortex
Premotor neurons play a fundamental role in transforming physical properties of observed objects, such as size and shape, into motor plans for grasping them, hence contributing to "pragmatic" affordance processing. Premotor neurons can also contribute to "semantic" affordance processing, as they can discharge differently even to pragmatically identical objects depending on their behavioural relevance for the observer (i.e. edible or inedible objects). Here, we compared the response of monkey ventral premotor area F5 neurons tested during pragmatic (PT) or semantic (ST) visuomotor tasks. Object presentation responses in ST showed shorter latency and lower object selectivity than in PT. Furthermore, we found a difference between a transient representation of semantic affordances and a sustained representation of pragmatic affordances at both the single neuron and population level. Indeed, responses in ST returned to baseline within 0.5 s whereas in PT they showed the typical sustained visual-to-motor activity during Go trials. In contrast, during No-go trials, the time course of pragmatic and semantic information processing was similar. These findings suggest that premotor cortex generates different dynamics depending on pragmatic and semantic information provided by the context in which the to-be-grasped object is presented
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