Minimisation of metabolic networks defines a new functional class of genes.

Construction of minimal metabolic networks (MMNs) contributes both to our understanding of the origins of metabolism and to the efficiency of biotechnological processes by preventing the diversion of flux away from product formation. We have designed MMNs using a novel in silico synthetic biology pipeline that removes genes encoding enzymes and transporters from genome-scale metabolic models. The resulting minimal gene-set still ensures both viability and high growth rates.

Evolutionary game selection creates cooperative environments.

The emergence of collective cooperation in competitive environments is a well-known phenomenon in biology, economics, and social systems. While most evolutionary game models focus on the evolution of strategies for a fixed game, how strategic decisions coevolve with the environment has so far mostly been overlooked. Here, we consider a game selection model where not only the strategies but also the game can change over time following evolutionary principles.

Reconstructing higher-order interactions in coupled dynamical systems.

Higher-order interactions play a key role for the operation and function of a complex system. However, how to identify them is still an open problem. Here, we propose a method to fully reconstruct the structural connectivity of a system of coupled dynamical units, identifying both pairwise and higher-order interactions from the system time evolution.

Higher-order correlations reveal complex memory in temporal hypergraphs.

Many real-world complex systems are characterized by interactions in groups that change in time. Current temporal network approaches, however, are unable to describe group dynamics, as they are based on pairwise interactions only. Here, we use time-varying hypergraphs to describe such systems, and we introduce a framework based on higher-order correlations to characterize their temporal organization.

Explosive Cooperation in Social Dilemmas on Higher-Order Networks.

Understanding how cooperative behaviors can emerge from competitive interactions is an open problem in biology and social sciences. While interactions are usually modeled as pairwise networks, the units of many real-world systems can also interact in groups of three or more. Here, we introduce a general framework to extend pairwise games to higher-order networks.

Collapse transition in epidemic spreading subject to detection with limited resources.

Compartmental models are the most widely used framework for modeling infectious diseases. These models have been continuously refined to incorporate all the realistic mechanisms that can shape the course of an epidemic outbreak. Building on a compartmental model that accounts for early detection and isolation of infectious individuals through testing, in this article we focus on the viability of detection processes under limited availability of testing resources, and we study how the latter impacts on the detection rate.

The making of the oral microbiome in Agta hunter-gatherers.

Ecological and genetic factors have influenced the composition of the human microbiome during our evolutionary history. We analysed the oral microbiota of the Agta, a hunter-gatherer population where some members have adopted an agricultural diet. We show that age is the strongest factor modulating the microbiome, probably through immunosenescence since we identified an increase in the number of species classified as pathogens with age.

Agta hunter-gatherer oral microbiomes are shaped by contact network structure.

Here we investigate the effects of extensive sociality and mobility on the oral microbiome of 138 Agta hunter-gatherers from the Philippines. Our comparisons of microbiome composition showed that the Agta are more similar to Central African BaYaka hunter-gatherers than to neighbouring farmers. We also defined the Agta social microbiome as a set of 137 oral bacteria (only 7% of 1980 amplicon sequence variants) significantly influenced by social contact (quantified through wireless sensors of short-range interactions).

MultiplexSAGE: A Multiplex Embedding Algorithm for Inter-Layer Link Prediction.

Research on graph representation learning has received great attention in recent years. However, most of the studies so far have focused on the embedding of single-layer graphs. The few studies dealing with the problem of representation learning of multilayer structures rely on the strong hypothesis that the inter-layer links are known, and this limits the range of possible applications.

DEGGs: an R package with shiny app for the identification of differentially expressed gene-gene interactions in high-throughput sequencing data.

Summary: The discovery of differential gene-gene correlations across phenotypical groups can help identify the activation/deactivation of critical biological processes underlying specific conditions. The presented R package, provided with a count and design matrix, extract networks of group-specific interactions that can be interactively explored through a shiny user-friendly interface. For each gene-gene link, differential statistical significance is provided through robust linear regression with an interaction term.

Dual communities in spatial networks.

Both human-made and natural supply systems, such as power grids and leaf venation networks, are built to operate reliably under changing external conditions. Many of these spatial networks exhibit community structures. Here, we show that a relatively strong connectivity between the parts of a network can be used to define a different class of communities: dual communities.

An AI approach for managing financial systemic risk via bank bailouts by taxpayers.

Bank bailouts are controversial governmental decisions, putting taxpayers' money at risk to avoid a domino effect through the network of claims between financial institutions. Yet very few studies address quantitatively the convenience of government investments in failing banks from the taxpayers' standpoint. We propose a dynamic financial network framework incorporating bailout decisions as a Markov Decision Process and an artificial intelligence technique that learns the optimal bailout actions to minimise the expected taxpayers' losses.

Network analysis of synovial RNA sequencing identifies gene-gene interactions predictive of response in rheumatoid arthritis.

Background: To determine whether gene-gene interaction network analysis of RNA sequencing (RNA-Seq) of synovial biopsies in early rheumatoid arthritis (RA) can inform our understanding of RA pathogenesis and yield improved treatment response prediction models.

Methods: We utilized four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We extracted specific gene-gene interaction networks in synovial RNA-Seq to characterize histologically defined pathotypes in early RA and leverage these synovial specific gene-gene networks to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC).

Social synchronization of brain activity increases during eye-contact.

Humans make eye-contact to extract information about other people's mental states, recruiting dedicated brain networks that process information about the self and others. Recent studies show that eye-contact increases the synchronization between two brains but do not consider its effects on activity within single brains. Here we investigate how eye-contact affects the frequency and direction of the synchronization within and between two brains and the corresponding network characteristics.

Non-Markovian temporal networks with auto- and cross-correlated link dynamics.

Many of the biological, social and man-made networks around us are inherently dynamic, with their links switching on and off over time. The evolution of these networks is often observed to be non-Markovian, and the dynamics of their links are often correlated. Hence, to accurately model these networks, predict their evolution, and understand how information and other relevant quantities propagate over them, the inclusion of both memory and dynamical dependencies between links is key.

The shape of memory in temporal networks.

How to best define, detect and characterize network memory, i.e. the dependence of a network's structure on its past, is currently a matter of debate.

Lack of practical identifiability may hamper reliable predictions in COVID-19 epidemic models.

Compartmental models are widely adopted to describe and predict the spreading of infectious diseases. The unknown parameters of these models need to be estimated from the data. Furthermore, when some of the model variables are not empirically accessible, as in the case of asymptomatic carriers of coronavirus disease 2019 (COVID-19), they have to be obtained as an outcome of the model.

Evolutionary Game Model of Group Choice Dilemmas on Hypergraphs.

We introduce an evolutionary game on hypergraphs in which decisions between a risky alternative and a safe one are taken in social groups of different sizes. The model naturally reproduces choice shifts, namely the differences between the preference of individual decision makers and the consensual choice of a group, that have been empirically observed in choice dilemmas. In particular, a deviation from the Nash equilibrium toward the risky strategy occurs when the dynamics takes place on heterogeneous hypergraphs.

Dynamical efficiency for multimodal time-varying transportation networks.

Spatial systems that experience congestion can be modeled as weighted networks whose weights dynamically change over time with the redistribution of flows. This is particularly true for urban transportation networks. The aim of this work is to find appropriate network measures that are able to detect critical zones for traffic congestion and bottlenecks in a transportation system.

Memory order decomposition of symbolic sequences.

We introduce a general method for the study of memory in symbolic sequences based on higher-order Markov analysis. The Markov process that best represents a sequence is expressed as a mixture of matrices of minimal orders, enabling the definition of the so-called memory profile, which unambiguously reflects the true order of correlations. The method is validated by recovering the memory profiles of tunable synthetic sequences.

Network isolators inhibit failure spreading in complex networks.

In our daily lives, we rely on the proper functioning of supply networks, from power grids to water transmission systems. A single failure in these critical infrastructures can lead to a complete collapse through a cascading failure mechanism. Counteracting strategies are thus heavily sought after.

Interacting Discovery Processes on Complex Networks.

Innovation is the driving force of human progress. Recent urn models reproduce well the dynamics through which the discovery of a novelty may trigger further ones, in an expanding space of opportunities, but neglect the effects of social interactions. Here we focus on the mechanisms of collective exploration, and we propose a model in which many urns, representing different explorers, are coupled through the links of a social network and exploit opportunities coming from their contacts.

Evolutionary dynamics of higher-order interactions in social networks.

We live and cooperate in networks. However, links in networks only allow for pairwise interactions, thus making the framework suitable for dyadic games, but not for games that are played in larger groups. Here, we study the evolutionary dynamics of a public goods game in social systems with higher-order interactions.

The evolution of knowledge within and across fields in modern physics.

The exchange of knowledge across different areas and disciplines plays a key role in the process of knowledge creation, and can stimulate innovation and the emergence of new fields. We develop here a quantitative framework to extract significant dependencies among scientific disciplines and turn them into a time-varying network whose nodes are the different fields, while the weighted links represent the flow of knowledge from one field to another at a given period of time. Drawing on a comprehensive data set on scientific production in modern physics and on the patterns of citations between articles published in the various fields in the last 30 years, we are then able to map, over time, how the ideas developed in a given field in a certain time period have influenced later discoveries in the same field or in other fields.