1,006 research outputs found
Impact of limited solvent capacity on metabolic rate, enzyme activities, and metabolite concentrations of S. cerevisiae glycolysis
The cell's cytoplasm is crowded by its various molecular components, resulting in a limited solvent capacity for the allocation of new proteins, thus constraining various cellular processes such as metabolism. Here we study the impact of the limited solvent capacity constraint on the metabolic rate, enzyme activities, and metabolite concentrations using a computational model of Saccharomyces cerevisiae glycolysis as a case study. We show that given the limited solvent capacity constraint, the optimal enzyme activities and the metabolite concentrations necessary to achieve a maximum rate of glycolysis are in agreement with their experimentally measured values. Furthermore, the predicted maximum glycolytic rate determined by the solvent capacity constraint is close to that measured in vivo. These results indicate that the limited solvent capacity is a relevant constraint acting on S. cerevisiae at physiological growth conditions, and that a full kinetic model together with the limited solvent capacity constraint can be used to predict both metabolite concentrations and enzyme activities in vivo. © 2008 Vazquez et al
Statistical Agent Based Modelization of the Phenomenon of Drug Abuse
We introduce a statistical agent based model to describe the phenomenon of
drug abuse and its dynamical evolution at the individual and global level. The
agents are heterogeneous with respect to their intrinsic inclination to drugs,
to their budget attitude and social environment. The various levels of drug use
were inspired by the professional description of the phenomenon and this
permits a direct comparison with all available data. We show that certain
elements have a great importance to start the use of drugs, for example the
rare events in the personal experiences which permit to overcame the barrier of
drug use occasionally. The analysis of how the system reacts to perturbations
is very important to understand its key elements and it provides strategies for
effective policy making. The present model represents the first step of a
realistic description of this phenomenon and can be easily generalized in
various directions.Comment: 12 pages, 5 figure
Semi-Markov Graph Dynamics
In this paper, we outline a model of graph (or network) dynamics based on two
ingredients. The first ingredient is a Markov chain on the space of possible
graphs. The second ingredient is a semi-Markov counting process of renewal
type. The model consists in subordinating the Markov chain to the semi-Markov
counting process. In simple words, this means that the chain transitions occur
at random time instants called epochs. The model is quite rich and its possible
connections with algebraic geometry are briefly discussed. Moreover, for the
sake of simplicity, we focus on the space of undirected graphs with a fixed
number of nodes. However, in an example, we present an interbank market model
where it is meaningful to use directed graphs or even weighted graphs.Comment: 25 pages, 4 figures, submitted to PLoS-ON
Ising model for distribution networks
An elementary Ising spin model is proposed for demonstrating cascading
failures (break-downs, blackouts, collapses, avalanches, ...) that can occur in
realistic networks for distribution and delivery by suppliers to consumers. A
ferromagnetic Hamiltonian with quenched random fields results from policies
that maximize the gap between demand and delivery. Such policies can arise in a
competitive market where firms artificially create new demand, or in a solidary
environment where too high a demand cannot reasonably be met. Network failure
in the context of a policy of solidarity is possible when an initially active
state becomes metastable and decays to a stable inactive state. We explore the
characteristics of the demand and delivery, as well as the topological
properties, which make the distribution network susceptible of failure. An
effective temperature is defined, which governs the strength of the activity
fluctuations which can induce a collapse. Numerical results, obtained by Monte
Carlo simulations of the model on (mainly) scale-free networks, are
supplemented with analytic mean-field approximations to the geometrical random
field fluctuations and the thermal spin fluctuations. The role of hubs versus
poorly connected nodes in initiating the breakdown of network activity is
illustrated and related to model parameters
Pricing in Social Networks with Negative Externalities
We study the problems of pricing an indivisible product to consumers who are
embedded in a given social network. The goal is to maximize the revenue of the
seller. We assume impatient consumers who buy the product as soon as the seller
posts a price not greater than their values of the product. The product's value
for a consumer is determined by two factors: a fixed consumer-specified
intrinsic value and a variable externality that is exerted from the consumer's
neighbors in a linear way. We study the scenario of negative externalities,
which captures many interesting situations, but is much less understood in
comparison with its positive externality counterpart. We assume complete
information about the network, consumers' intrinsic values, and the negative
externalities. The maximum revenue is in general achieved by iterative pricing,
which offers impatient consumers a sequence of prices over time.
We prove that it is NP-hard to find an optimal iterative pricing, even for
unweighted tree networks with uniform intrinsic values. Complementary to the
hardness result, we design a 2-approximation algorithm for finding iterative
pricing in general weighted networks with (possibly) nonuniform intrinsic
values. We show that, as an approximation to optimal iterative pricing, single
pricing can work rather well for many interesting cases, but theoretically it
can behave arbitrarily bad
Characterizing Interdisciplinarity of Researchers and Research Topics Using Web Search Engines
Researchers' networks have been subject to active modeling and analysis.
Earlier literature mostly focused on citation or co-authorship networks
reconstructed from annotated scientific publication databases, which have
several limitations. Recently, general-purpose web search engines have also
been utilized to collect information about social networks. Here we
reconstructed, using web search engines, a network representing the relatedness
of researchers to their peers as well as to various research topics.
Relatedness between researchers and research topics was characterized by
visibility boost-increase of a researcher's visibility by focusing on a
particular topic. It was observed that researchers who had high visibility
boosts by the same research topic tended to be close to each other in their
network. We calculated correlations between visibility boosts by research
topics and researchers' interdisciplinarity at individual level (diversity of
topics related to the researcher) and at social level (his/her centrality in
the researchers' network). We found that visibility boosts by certain research
topics were positively correlated with researchers' individual-level
interdisciplinarity despite their negative correlations with the general
popularity of researchers. It was also found that visibility boosts by
network-related topics had positive correlations with researchers' social-level
interdisciplinarity. Research topics' correlations with researchers'
individual- and social-level interdisciplinarities were found to be nearly
independent from each other. These findings suggest that the notion of
"interdisciplinarity" of a researcher should be understood as a
multi-dimensional concept that should be evaluated using multiple assessment
means.Comment: 20 pages, 7 figures. Accepted for publication in PLoS On
Minimization of the receiver cost in an all-optical ring with a limited number of wavelengths
A new all-optical node architecture, known as \emph{Packet Optical Add-Drop Multiplexer} (POADM), may lead to a considerable cost reduction for the infrastructure of the all-optical metropolitan rings if associated with proper dimensioning studies. We present a dimensioning problem which consists of minimizing the total number of receivers located in POADMs for a metropolitan all-optical ring with a fixed number of wavelengths and a given traffic matrix. We prove that this problem is NP-complete and provide a heuristic. The heuristic principle is to match and to group transmissions instead of considering them independently. We justify the transmission group matching approach by confronting the results of our algorithm with its simplified version. The results obtained allow us to recommend the heuristic in the planning of POADM configurations in all-optical rings with a limited number of wavelengths
Correlated dynamics in egocentric communication networks
We investigate the communication sequences of millions of people through two
different channels and analyze the fine grained temporal structure of
correlated event trains induced by single individuals. By focusing on
correlations between the heterogeneous dynamics and the topology of egocentric
networks we find that the bursty trains usually evolve for pairs of individuals
rather than for the ego and his/her several neighbors thus burstiness is a
property of the links rather than of the nodes. We compare the directional
balance of calls and short messages within bursty trains to the average on the
actual link and show that for the trains of voice calls the imbalance is
significantly enhanced, while for short messages the balance within the trains
increases. These effects can be partly traced back to the technological
constrains (for short messages) and partly to the human behavioral features
(voice calls). We define a model that is able to reproduce the empirical
results and may help us to understand better the mechanisms driving technology
mediated human communication dynamics.Comment: 7 pages, 6 figure
Who is the best player ever? A complex network analysis of the history of professional tennis
We consider all matches played by professional tennis players between 1968
and 2010, and, on the basis of this data set, construct a directed and weighted
network of contacts. The resulting graph shows complex features, typical of
many real networked systems studied in literature. We develop a diffusion
algorithm and apply it to the tennis contact network in order to rank
professional players. Jimmy Connors is identified as the best player of the
history of tennis according to our ranking procedure. We perform a complete
analysis by determining the best players on specific playing surfaces as well
as the best ones in each of the years covered by the data set. The results of
our technique are compared to those of two other well established methods. In
general, we observe that our ranking method performs better: it has a higher
predictive power and does not require the arbitrary introduction of external
criteria for the correct assessment of the quality of players. The present work
provides a novel evidence of the utility of tools and methods of network theory
in real applications.Comment: 10 pages, 4 figures, 4 table
Statistically validated networks in bipartite complex systems
Many complex systems present an intrinsic bipartite nature and are often
described and modeled in terms of networks [1-5]. Examples include movies and
actors [1, 2, 4], authors and scientific papers [6-9], email accounts and
emails [10], plants and animals that pollinate them [11, 12]. Bipartite
networks are often very heterogeneous in the number of relationships that the
elements of one set establish with the elements of the other set. When one
constructs a projected network with nodes from only one set, the system
heterogeneity makes it very difficult to identify preferential links between
the elements. Here we introduce an unsupervised method to statistically
validate each link of the projected network against a null hypothesis taking
into account the heterogeneity of the system. We apply our method to three
different systems, namely the set of clusters of orthologous genes (COG) in
completely sequenced genomes [13, 14], a set of daily returns of 500 US
financial stocks, and the set of world movies of the IMDb database [15]. In all
these systems, both different in size and level of heterogeneity, we find that
our method is able to detect network structures which are informative about the
system and are not simply expression of its heterogeneity. Specifically, our
method (i) identifies the preferential relationships between the elements, (ii)
naturally highlights the clustered structure of investigated systems, and (iii)
allows to classify links according to the type of statistically validated
relationships between the connected nodes.Comment: Main text: 13 pages, 3 figures, and 1 Table. Supplementary
information: 15 pages, 3 figures, and 2 Table
- …