Showing papers by "Albert-László Barabási published in 2017"

Network control principles predict neuron function in the Caenorhabditis elegans connectome

Abstract: Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

The fundamental advantages of temporal networks

Abstract: Most networked systems of scientific interest are characterized by temporal links, meaning the network’s structure changes over time. Link temporality has been shown to hinder many dynamical processes, from information spreading to accessibility, by disrupting network paths. Considering the ubiquity of temporal networks in nature, we ask: Are there any advantages of the networks’ temporality? We use an analytical framework to show that temporal networks can, compared to their static counterparts, reach controllability faster, demand orders of magnitude less control energy, and have control trajectories, that are considerably more compact than those characterizing static networks. Thus, temporality ensures a degree of flexibility that would be unattainable in static networks, enhancing our ability to control them.

Epigenomic and transcriptomic approaches in the post-genomic era: path to novel targets for diagnosis and therapy of the ischaemic heart? Position Paper of the European Society of Cardiology Working Group on Cellular Biology of the Heart.

Abstract: Despite advances in myocardial reperfusion therapies, acute myocardial ischaemia/reperfusion injury and consequent ischaemic heart failure represent the number one cause of morbidity and mortality in industrialized societies. Although different therapeutic interventions have been shown beneficial in preclinical settings, an effective cardioprotective or regenerative therapy has yet to be successfully introduced in the clinical arena. Given the complex pathophysiology of the ischaemic heart, large scale, unbiased, global approaches capable of identifying multiple branches of the signalling networks activated in the ischaemic/reperfused heart might be more successful in the search for novel diagnostic or therapeutic targets. High-throughput techniques allow high-resolution, genome-wide investigation of genetic variants, epigenetic modifications, and associated gene expression profiles. Platforms such as proteomics and metabolomics (not described here in detail) also offer simultaneous readouts of hundreds of proteins and metabolites. Isolated omics analyses usually provide Big Data requiring large data storage, advanced computational resources and complex bioinformatics tools. The possibility of integrating different omics approaches gives new hope to better understand the molecular circuitry activated by myocardial ischaemia, putting it in the context of the human ‘diseasome’. Since modifications of cardiac gene expression have been consistently linked to pathophysiology of the ischaemic heart, the integration of epigenomic and transcriptomic data seems a promising approach to identify crucial disease networks. Thus, the scope of this Position Paper will be to highlight potentials and limitations of these approaches, and to provide recommendations to optimize the search for novel diagnostic or therapeutic targets for acute ischaemia/reperfusion injury and ischaemic heart failure in the post-genomic era.

Fundamental limitations of network reconstruction from temporal data.

Abstract: Inferring properties of the interaction matrix that characterizes how nodes in a networked system directly interact with each other is a well-known network reconstruction problem. Despite a decade of extensive studies, network reconstruction remains an outstanding challenge. The fundamental limitations governing which properties of the interaction matrix (e.g. adjacency pattern, sign pattern or degree sequence) can be inferred from given temporal data of individual nodes remain unknown. Here, we rigorously derive the necessary conditions to reconstruct any property of the interaction matrix. Counterintuitively, we find that reconstructing any property of the interaction matrix is generically as difficult as reconstructing the interaction matrix itself, requiring equally informative temporal data. Revealing these fundamental limitations sheds light on the design of better network reconstruction algorithms that offer practical improvements over existing methods.

Identifying and modeling the structural discontinuities of human interactions.

Abstract: The idea of a hierarchical spatial organization of society lies at the core of seminal theories in human geography that have strongly influenced our understanding of social organization. Along the same line, the recent availability of large-scale human mobility and communication data has offered novel quantitative insights hinting at a strong geographical confinement of human interactions within neighboring regions, extending to local levels within countries. However, models of human interaction largely ignore this effect. Here, we analyze several country-wide networks of telephone calls - both, mobile and landline - and in either case uncover a systematic decrease of communication induced by borders which we identify as the missing variable in state-of-the-art models. Using this empirical evidence, we propose an alternative modeling framework that naturally stylizes the damping effect of borders. We show that this new notion substantially improves the predictive power of widely used interaction models. This increases our ability to understand, model and predict social activities and to plan the development of infrastructures across multiple scales.

Integrating personalized gene expression profiles into predictive disease-associated gene pools

Abstract: Gene expression data are routinely used to identify genes that on average exhibit different expression levels between a case and a control group. Yet, very few of such differentially expressed genes are detectably perturbed in individual patients. Here, we develop a framework to construct personalized perturbation profiles for individual subjects, identifying the set of genes that are significantly perturbed in each individual. This allows us to characterize the heterogeneity of the molecular manifestations of complex diseases by quantifying the expression-level similarities and differences among patients with the same phenotype. We show that despite the high heterogeneity of the individual perturbation profiles, patients with asthma, Parkinson and Huntington’s disease share a broadpool of sporadically disease-associated genes, and that individuals with statistically significant overlap with this pool have a 80–100% chance of being diagnosed with the disease. The developed framework opens up the possibility to apply gene expression data in the context of precision medicine, with important implications for biomarker identification, drug development, diagnosis and treatment.

Trade-offs between driving nodes and time-to-control in complex networks.

Abstract: Recent advances in control theory provide us with efficient tools to determine the minimum number of driving (or driven) nodes to steer a complex network towards a desired state. Furthermore, we often need to do it within a given time window, so it is of practical importance to understand the trade-offs between the minimum number of driving/driven nodes and the minimum time required to reach a desired state. Therefore, we introduce the notion of actuation spectrum to capture such trade-offs, which we used to find that in many complex networks only a small fraction of driving (or driven) nodes is required to steer the network to a desired state within a relatively small time window. Furthermore, our empirical studies reveal that, even though synthetic network models are designed to present structural properties similar to those observed in real networks, their actuation spectra can be dramatically different. Thus, it supports the need to develop new synthetic network models able to replicate controllability properties of real-world networks.

Network medicine : complex systems in human disease and therapeutics

Abstract: Big data, genomics, and quantitative approaches to network-based analysis are combining to advance the frontiers of medicine as never before. Network Medicine introduces this rapidly evolving field of medical research, which promises to revolutionize the diagnosis and treatment of human diseases. With contributions from leading experts that highlight the necessity of a team-based approach in network medicine, this definitive volume provides readers with a state-of-the-art synthesis of the progress being made and the challenges that remain. Medical researchers have long sought to identify single molecular defects that cause diseases, with the goal of developing silver-bullet therapies to treat them. But this paradigm overlooks the inherent complexity of human diseases and has often led to treatments that are inadequate or fraught with adverse side effects. Rather than trying to force disease pathogenesis into a reductionist model, network medicine embraces the complexity of multiple influences on disease and relies on many different types of networks: from the cellular-molecular level of protein-protein interactions to correlational studies of gene expression in biological samples. The authors offer a systematic approach to understanding complex diseases while explaining network medicine's unique features, including the application of modern genomics technologies, biostatistics and bioinformatics, and dynamic systems analysis of complex molecular networks in an integrative context. By developing techniques and technologies that comprehensively assess genetic variation, cellular metabolism, and protein function, network medicine is opening up new vistas for uncovering causes and identifying cures of disease.

Recordings of Caenorhabditis elegans locomotor behaviour following targeted ablation of single motorneurons

Abstract: We are grateful to L. Grundy, A. Brown and E. Yemini for help with analysis of tracking data, and V. Butler and the Caenorhabditis Genetics Center (funded by NIH Office of Research Infrastructure Programs P40 OD010440), for C. elegans strains. This work is supported by MRC grant number MC-A023-5PB91; Wellcome Trust grant number WT103784MA; John Templeton Foundation: Mathematical and Physical Sciences grant number PFI-777; and European Commission grant number 255 641191 (CIMPLEX). P.E.V. is supported by the Medical Research Council grant number 257 MR/K020706/1. Y.L.C. is supported by an EMBO Long Term Fellowship.

Human Perception of Performance.

Abstract: Humans are routinely asked to evaluate the performance of other individuals, separating success from failure and affecting outcomes from science to education and sports. Yet, in many contexts, the metrics driving the human evaluation process remain unclear. Here we analyse a massive dataset capturing players' evaluations by human judges to explore human perception of performance in soccer, the world's most popular sport. We use machine learning to design an artificial judge which accurately reproduces human evaluation, allowing us to demonstrate how human observers are biased towards diverse contextual features. By investigating the structure of the artificial judge, we uncover the aspects of the players' behavior which attract the attention of human judges, demonstrating that human evaluation is based on a noticeability heuristic where only feature values far from the norm are considered to rate an individual's performance.

Control energy scaling in temporal networks

Abstract: In practical terms, controlling a network requires manipulating a large number of nodes with a comparatively small number of external inputs, a process that is facilitated by paths that broadcast the influence of the (directly-controlled) driver nodes to the rest of the network. Recent work has shown that surprisingly, temporal networks can enjoy tremendous control advantages over their static counterparts despite the fact that in temporal networks such paths are seldom instantaneously available. To understand the underlying reasons, here we systematically analyze the scaling behavior of a key control cost for temporal networks--the control energy. We show that the energy costs of controlling temporal networks are determined solely by the spectral properties of an "effective" Gramian matrix, analogous to the static network case. Surprisingly, we find that this scaling is largely dictated by the first and the last network snapshot in the temporal sequence, independent of the number of intervening snapshots, the initial and final states, and the number of driver nodes. Our results uncover the intrinsic laws governing why and when temporal networks save considerable control energy over their static counterparts.

Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes

Abstract: Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of 4 putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways’ relationship to T2D.

The elegant law that governs us all

Abstract: A dog owner weighs twice as much as her German shepherd. Does she eat twice as much? Does a big city need twice as many gas stations as one that is half its size? Our first instinct is to say yes. But, alas, we are wrong. On a per-gram basis, a human requires about 25% less food than her dog, and the larger city needs only 85% more gas stations. As Geoffrey West explains in Scale, the reason behind these intriguing phenomena is a universal law known as allometry—the finding that as organisms, cities, and com­panies grow, many of their characteristics scale nonlinearly.

Viva Europa, a Land of Excellence in Research and Innovation for Health and Wellbeing

Abstract: Charles Auffraya; Michael Sagnerb; Sonia Abdelhakc; Ian Adcockd; Alvar Agustie; Margarida Amaralf; Stylianos Antonarakisg; Ross Arenah; Françoise Argouli; Rudi Ballingj; Albert-Laszlo Barabasik; Jacques Beckmannl; Anders Bjartellm; Niklas Blombergn; Thomas Bourgerono; Bertrand Boutronp; Samir Brahmachariq; Christian Bréchotr; Christopher Brightlings; Marta Cascantet; Alfredo Cesariou; Dominique Charronv; Sai-Juan Chenw; Zhu Chenx; Fan Chungy; Karine Clémentz; Ana Conesaaa; Alain Cozzoneab; Menno de Jongac; Jean-François Deleuzead; Jacques Demotesae; Alberto di Meglioaf; Ratko Djukanovicag; Ugur Dogrusozah; Elissa Epelai; Alain Fischeraj; Andrea Gelemanovicak; Carole Gobleal; Takashi Gojoboriam; Michel Goldmanan; Herman Goossensao; François Grosap; Yi-Ke Guoaq; Pierre Hainautar; David Harrisonas; Hans Hoffmannat; Leroy Hoodau; Peter Hunterav; Yves Jacobaw; Hiroaki Kitanoax; Ursula Klingmülleray; Bartha Knoppersaz; Walter Kolchba; Marion Koopmansbb; Doron Lancetbc; Martine Lavillebd; Jean-Marie Lehnbe; Francis Lévibf; Andrey Lisistsabg; Vincent Lotteaubh; Antoine Magnanbi; Bongani Mayosibj; Andres Metspalubk; Yves Moreaubl; James N’Dowbm; Laurent Nicodbn; Denis Noblebo; Maria Manuela Nogueirabp; Anna Norrby-Teglundbq; Laurent Nottalebr; Peter Openshawbs; Mehmet Oztürkbt; Susanna Palkonenbu; Silvio Parodibv; Johann Pelletbw; Ozren Polasekbx; Nathan Priceby; Christian Pristipinobz; Timothy Radstakeca; Martine Raescb; Josep Rocacc; Damjana Rozmancd; Philippe Sabatierce; Shlomo Sassoncf; Bernd Schmeckcg; Ismaïl Serageldinch; Anita Simondsci; Bento Soarescj; Peter Sterkck; Giulio Superti-Furgacl; David Supplecm; Jesper Tegnercn; Mathias Uhlenco; Sylvie van der Werfcp; Pablo Villosladacq; Manlio Vinciguerracr; Vitaly Volpertcs; Steve Webbct; Emiel Wouterscu; Ferran Sanzcv; Francisco Nobregacw

Corrigendum: Trade-offs between driving nodes and time-to-control in complex networks.

Abstract: Scientific Reports 7: Article number: 39978; published online: 05 January 2017; updated: 09 March 2017 This Article contains an error in the order of the Figures. Figures 1, 2, 3 and 4 were published as Figures 2, 4, 1 and 3 respectively. The correct Figures appear below. The Figure legends are correct.

Academia under fire in Hungary

Abstract: On 10 April, Hungarian President Janos Ader signed into law an amendment to the National Higher Education Law that would outlaw the Central European University (CEU). Although portrayed by the government as a purely administrative step, the “Lex-CEU” law is a strident attempt to curtail academic freedom and limit the independence of academic institutions.