arXiv subjects

M. Ángeles Serrano

Publications and source records attributed to M. Ángeles Serrano.

53 records · Page 3Linked to original sources

arXiv2017

Navigability of temporal networks in hyperbolic space

Information routing is one of the main tasks in many complex networks with a communication function. Maps produced by embedding the networks in hyperbolic space can assist this task enabling the implementation of efficient navigation strategies. However, only static maps have been considered so far, while navigation in more realistic situations, where the network structure may vary in time, remain largely unexplored. Here, we analyze the navigability of real networks by using greedy routing in hyperbolic space, where the nodes are subject to a stochastic activation-inactivation dynamics. We find that such dynamics enhances navigability with respect to the static case. Interestingly, there exists an optimal intermediate activation value, which ensures the best trade-off between the increase in the number of successful paths and a limited growth of their length. Contrary to expectations, the enhanced navigability is robust even when the most connected nodes inactivate with very high probability. Finally, our results indicate that some real networks are ultranavigable and remain highly navigable even if the network structure is extremely unsteady. These findings have important implications for the design and evaluation of efficient routing protocols that account for the temporal nature of real complex networks.

physics.soc-ph↗

arXiv2017

Soft communities in similarity space

The $\mathbb{S}^1$ model has been a central geometric model in the development of the field of network geometry. It has been mainly studied in its homogeneous regime, in which angular coordinates are independently and uniformly scattered on the circle. We now investigate if the model can generate networks with targeted topological features and soft communities, that is, heterogeneous angular distributions. Under these circumstances, hidden degrees must depend on angular coordinates and we propose a method to estimate them. We conclude that the model can be topologically invariant with respect to the soft-community structure. Our results might have important implications, both in expanding the scope of the model beyond the independent hidden variables limit and in the embedding of real-world networks.

physics.soc-ph↗

arXiv2017

Metabolic plasticity in synthetic lethal mutants: viability at higher cost

The most frequent form of pairwise synthetic lethality (SL) in metabolic networks is known as plasticity synthetic lethality (PSL). It occurs when the simultaneous inhibition of paired functional and silent metabolic reactions or genes is lethal, while the default of the functional reaction or gene in the pair is backed up by the activation of the silent one. Based on a complex systems approach and by using computational techniques on bacterial genome-scale metabolic reconstructions, we found that the failure of the functional PSL partner triggers a critical reorganization of fluxes to ensure viability in the mutant which not only affects the SL pair but a significant fraction of other interconnected reactions forming what we call a SL cluster. Interestingly, SL clusters show a strong entanglement both in terms of silent coessential reactions, which band together to form backup systems, and of other functional and silent reactions in the metabolic network. This strong overlap, also detected at the level of genes, mitigates the acquired vulnerabilities and increased structural and functional costs that pay for the robustness provided by essential plasticity. Finally, the participation of coessential reactions and genes in different SL clusters is very heterogeneous and those at the intersection of many SL clusters could serve as supertargets for more efficient drug action in the treatment of complex diseases and to elucidate improved strategies directed to reduce undesired resistance to chemicals in pathogens.

q-bio.MN↗

arXiv2017

Multiscale unfolding of real networks by geometric renormalization

Multiple scales coexist in complex networks. However, the small world property makes them strongly entangled. This turns the elucidation of length scales and symmetries a defiant challenge. Here, we define a geometric renormalization group for complex networks and use the technique to investigate networks as viewed at different scales. We find that real networks embedded in a hidden metric space show geometric scaling, in agreement with the renormalizability of the underlying geometric model. This allows us to unfold real scale-free networks in a self-similar multilayer shell which unveils the coexisting scales and their interplay. The multiscale unfolding offers a basis for a new approach to explore critical phenomena and universality in complex networks, and affords us immediate practical applications, like high-fidelity smaller-scale replicas of large networks and a multiscale navigation protocol in hyperbolic space which boosts the success of single-layer versions.

cond-mat.dis-nn↗

arXiv2017

Geometric correlations mitigate the extreme vulnerability of multiplex networks against targeted attacks

We show that real multiplex networks are unexpectedly robust against targeted attacks on high degree nodes, and that hidden interlayer geometric correlations predict this robustness. Without geometric correlations, multiplexes exhibit an abrupt breakdown of mutual connectivity, even with interlayer degree correlations. With geometric correlations, we instead observe a multistep cascading process leading into a continuous transition, which apparently becomes fully continuous in the thermodynamic limit. Our results are important for the design of efficient protection strategies and of robust interacting networks in many domains.

physics.soc-ph↗

arXiv2017

The hidden geometry of weighted complex networks

The topology of many real complex networks has been conjectured to be embedded in hidden metric spaces, where distances between nodes encode their likelihood of being connected. Besides of providing a natural geometrical interpretation of their complex topologies, this hypothesis yields the recipe for sustainable Internet's routing protocols, sheds light on the hierarchical organization of biochemical pathways in cells, and allows for a rich characterization of the evolution of international trade. We present empirical evidence that this geometric interpretation also applies to the weighted organisation of real complex networks. We introduce a very general and versatile model and use it to quantify the level of coupling between their topology, their weights, and an underlying metric space. Our model accurately reproduces both their topology and their weights, and our results suggest that the formation of connections and the assignment of their magnitude are ruled by different processes.

physics.soc-ph↗

arXiv2016

Noise-Induced Polarization Switch in Single and Multiplex Complex Networks

The combination of bistability and noise is ubiquitous in complex systems, from biological to social interactions, and has important implications for their functioning and resilience. We analyze a simple three-state model for bistability in networks under varying unbiased noise. In a fully connected network increasing noise yields a collapse of bistability to an unpolarised state. In contrast, in complex networks noise can abruptly switch the polarization state in an irreversible way. When two networks are combined through increasing multiplex coupling, one is dominant and progressively imposes its state on the other, offsetting or promoting the ability of noise to switch polarization. Our results show that dynamical correlations and asymmetry in dynamical processes in networks are sufficient for allowing unbiased noise to produce abrupt irreversible transitions between extremes, which can be neutralized or enhanced by multiplex coupling.

nlin.AO↗

arXiv2016

The hidden hyperbolic geometry of international trade: World Trade Atlas 1870-2013

Here, we present the World Trade Atlas 1870-2013, a collection of annual world trade maps in which distance combines economic size and the different dimensions that affect international trade beyond mere geography. Trade distances, which are based on a gravity model predicting the existence of significant trade channels, are such that the closer countries are in trade space, the greater their chance of becoming connected. The atlas provides us with information regarding the long-term evolution of the international trade system and demonstrates that, in terms of trade, the world is not flat but hyperbolic, as a reflection of its complex architecture. The departure from flatness has been increasing since World War I, meaning that differences in trade distances are growing and trade networks are becoming more hierarchical. Smaller-scale economies are moving away from other countries except for the largest economies; meanwhile those large economies are increasing their chances of becoming connected worldwide. At the same time, Preferential Trade Agreements do not fit in perfectly with natural communities within the trade space and have not necessarily reduced internal trade barriers. We discuss an interpretation in terms of globalization, hierarchization, and localization; three simultaneous forces that shape the international trade system.

physics.soc-ph↗

arXiv2016

Detecting the Escherichia coli metabolic backbone

The heterogeneity of reaction fluxes present in a metabolic network within a single flux state can be exploited to construct the so-called backbone as a reduced version of metabolism. The backbone maintains all significant fluxes producing or consuming metabolites while displaying a substantially decreased number of interconnections and, hence, it becomes a useful tool to extract primary metabolic routes. Here, we disclose the metabolic backbone of Escherichia coli using the computationally predicted fluxes which maximize the growth rate in glucose minimal medium, and we compare it with the backbone of Mycoplasma pneumoniae, a much simpler organism. We find that the central core in both backbones is mainly composed of reactions in ancient pathways, still playing at present a key role in energy metabolism. In E. coli, the analysis of the backbone reveals that the synthesis of nucleotides and the metabolism of lipids form smaller cores which rely critically on energy metabolism; but not conversely. At the same time, an analysis of the dependence of this backbone on media composition leads to the identification of pathways sensitive to environmental changes. The metabolic backbone of an organism is thus useful to trace simultaneously both its evolution and adaptation fingerprints.

q-bio.MN↗

arXiv2015

Mapping high-growth phenotypes in the flux space of microbial metabolism

Experimental and empirical observations on cell metabolism cannot be understood as a whole without their integration into a consistent systematic framework. However, the characterization of metabolic flux phenotypes is typically reduced to the study of a single optimal state, like maximum biomass yield that is by far the most common assumption. Here we confront optimal growth solutions to the whole set of feasible flux phenotypes (FFP), which provides a benchmark to assess the likelihood of optimal and high-growth states and their agreement with experimental results. In addition, FFP maps are able to uncover metabolic behaviors, such as aerobic fermentation accompanying exponential growth on sugars at nutrient excess conditions, that are unreachable using standard models based on optimality principles. The information content of the full FFP space provides us with a map to explore and evaluate metabolic behavior and capabilities, and so it opens new avenues for biotechnological and biomedical applications.

q-bio.MN↗

arXiv2014

Regulation of burstiness by network-driven activation

We prove that complex networks of interactions have the capacity to regulate and buffer unpredictable fluctuations in production events. We show that non-bursty network-driven activation dynamics can effectively regulate the level of burstiness in the production of nodes, which can be enhanced or reduced. Burstiness can be induced even when the endogenous inter-event time distribution of nodes' production is non-bursty. We found that hubs tend to be less controllable than low degree nodes, which are more susceptible to the networked regulatory effects. Our results have important implications for the analysis and engineering of bursty activity in a range of systems, from telecommunication networks to transcription and translation of genes into proteins in cells.

physics.soc-ph↗

arXiv2014

Essential plasticity and redundancy of metabolism unveiled by synthetic lethality analysis

We unravel how functional plasticity and redundancy are essential mechanisms underlying the ability to survive of metabolic networks. We perform an exhaustive computational screening of synthetic lethal reaction pairs in Escherichia coli in a minimal medium and we find that synthetic lethal pairs divide in two different groups depending on whether the synthetic lethal interaction works as a backup or as a parallel use mechanism, the first corresponding to essential plasticity and the second to essential redundancy. In E. coli, the analysis of pathways entanglement through essential redundancy supports the view that synthetic lethality affects preferentially a single function or pathway. In contrast, essential plasticity, the dominant class, tends to be inter-pathway but strongly localized and unveils Cell Envelope Biosynthesis as an essential backup for Membrane Lipid Metabolism. When comparing E. coli and Mycoplasma pneumoniae, we find that the metabolic networks of the two organisms exhibit a large difference in the relative importance of plasticity and redundancy which is consistent with the conjecture that plasticity is a sophisticated mechanism that requires a complex organization. Finally, coessential reaction pairs are explored in different environmental conditions to uncover the interplay between the two mechanisms. We find that synthetic lethal interactions and their classification in plasticity and redundancy are basically insensitive to medium composition, and are highly conserved even when the environment is enriched with nonessential compounds or overconstrained to decrease maximum biomass formation.

q-bio.MN↗

arXiv2013

Negative-feedback self-regulation contributes to robust and high-fidelity transmembrane signal transduction

We present a minimal motif model for transmembrane cell signaling. The model assumes signaling events taking place in spatially distributed nanoclusters regulated by a birth/death dynamics. The combination of these spatio-temporal aspects can be modulated to provide a robust and high-fidelity response behavior without invoking sophisticated modeling of the signaling process as a sequence of cascade reactions and fine-tuned parameters. Our results show that the fact that the distributed signaling events take place in nanoclusters with a finite lifetime regulated by local production is sufficient to obtain a robust and high-fidelity response.

q-bio.CB↗

arXiv2012

Assessing the significance of knockout cascades in metabolic networks

Complex networks have been shown to be robust against random structural perturbations, but vulnerable against targeted attacks. Robustness analysis usually simulates the removal of individual or sets of nodes, followed by the assessment of the inflicted damage. For complex metabolic networks, it has been suggested that evolutionary pressure may favor robustness against reaction removal. However, the removal of a reaction and its impact on the network may as well be interpreted as selective regulation of pathway activities, suggesting a tradeoff between the efficiency of regulation and vulnerability. Here, we employ a cascading failure algorithm to simulate the removal of single and pairs of reactions from the metabolic networks of two organisms, and estimate the significance of the results using two different null models: degree preserving and mass-balanced randomization. Our analysis suggests that evolutionary pressure promotes larger cascades of non-viable reactions, and thus favors the ability of efficient metabolic regulation at the expense of robustness.

q-bio.MN↗

arXiv2012

Predicting effects of structural stress in a genome-reduced model bacterial metabolism

We studied in silico effects of structural stress in Mycoplasma pneumoniae, a genome-reduced model bacterial organism, by tracking the damage propagating on its metabolic network after a deleterious perturbation. First, we analyzed failure cascades spreading from individual reactions and pairs of reactions and compared the results to those in Staphylococcus aureus and Escherichia coli. To alert to the potential damage caused by the failure of individual reactions, we propose a generic predictor based on local information that identifies target reactions for structural vulnerability. With respect to the simultaneous failure of pairs of reactions, we detected strong non-linear amplification effects that can be predicted by the presence of specific motifs in the intersection of single cascades. We further connected the metabolic and gene co-expression networks of M. pneumoniae through enzyme activities, and studied the consequences of knocking out individual genes and clusters of genes. Damage caused by single gene knockouts reveals a strong correlation between genome-scale cascades of large impact and gene essentiality. At the same time, we found that genes controlling high-damage reactions tend to operate in functional isolation, as a metabolic protection mechanism. We conclude that the architecture of M. pneumoniae, both at the level of metabolism and genome, seems to have evolved towards increased structural robustness, similarly to other more complex model bacterial organisms, despite its reduced genome size and its greater metabolic network linearity. Our approach, although motivated biochemically, is generic enough to be of potential use toward analyzing and predicting spreading of structural stress in any bipartite complex network.

q-bio.MN↗

arXiv2012

Epidemic spreading on interconnected networks

Many real networks are not isolated from each other but form networks of networks, often interrelated in non trivial ways. Here, we analyze an epidemic spreading process taking place on top of two interconnected complex networks. We develop a heterogeneous mean field approach that allows us to calculate the conditions for the emergence of an endemic state. Interestingly, a global endemic state may arise in the coupled system even though the epidemics is not able to propagate on each network separately, and even when the number of coupling connections is small. Our analytic results are successfully confronted against large-scale numerical simulations.

cond-mat.dis-nn↗

arXiv2010

Network-based confidence scoring system for genome-scale metabolic reconstructions

Reliability on complex biological networks reconstructions remains a concern. Although observations are getting more and more precise, the data collection process is yet error prone and the proofs display uneven certitude. In the case of metabolic networks, the currently employed confidence scoring system rates reactions according to a discretized small set of labels denoting different levels of experimental evidence or model-based likelihood. Here, we propose a computational network-based system of reaction scoring that exploits the complex hierarchical structure and the statistical regularities of the metabolic network as a bipartite graph. We use the example of Escherichia coli metabolism to illustrate our methodology. Our model is adjusted to the observations in order to derive connection probabilities between individual metabolite-reaction pairs and, after validation, we integrate individual link information to assess the reliability of each reaction in probabilistic terms. This network-based scoring system breaks the degeneracy of currently employed scores, enables further confirmation of modeling results, uncovers very specific reactions that could be functionally or evolutionary important, and identifies prominent experimental targets for further verification. We foresee a wide range of potential applications of our approach given the natural network bipartivity of many biological interactions.

q-bio.MN↗