Showing papers by "Albert-László Barabási published in 2011"
TL;DR: Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
Abstract: Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
3,978 citations
TL;DR: In this article, the authors developed analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system's entire dynamics.
Abstract: The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system's entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network's degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.
2,889 citations
23 Oct 2011
TL;DR: The degree distribution, twopoint correlations, and clustering are the studied topological properties and an evolution of networks is studied to shed light on the influence the dynamics has on the network topology.
Abstract: Networks have become a general concept to model the structure of arbitrary relationships among entities. The framework of a network introduces a fundamentally new approach apart from ‘classical’ structures found in physics to model the topology of a system. In the context of networks fundamentally new topological effects can emerge and lead to a class of topologies which are termed ‘complex networks’. The concept of a network successfully models the static topology of an empirical system, an arbitrary model, and a physical system. Generally networks serve as a host for some dynamics running on it in order to fulfill a function. The question of the reciprocal relationship among a dynamical process on a network and its topology is the context of this Thesis. This context is studied in both directions. The network topology constrains or enhances the dynamics running on it, while the reciprocal interaction is of the same importance. Networks are commonly the result of an evolutionary process, e.g. protein interaction networks from biology. Within such an evolution the dynamics shapes the underlying network topology with respect to an optimal achievement of the function to perform. Answering the question what the influence on a dynamics of a particular topological property has requires the accurate control over the topological properties in question. In this Thesis the degree distribution, twopoint correlations, and clustering are the studied topological properties. These are motivated by the ubiquitous presence and importance within almost all empirical networks. An analytical framework to measure and to control such quantities of networks along with numerical algorithms to generate them is developed in a first step. Networks with the examined topological properties are then used to reveal their impact on two rather general dynamics on networks. Finally, an evolution of networks is studied to shed light on the influence the dynamics has on the network topology.
2,720 citations
TL;DR: In this paper, the authors discuss different types of interactome networks and the insights that can come from analyzing them, including how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.
1,398 citations
TL;DR: A proteome-wide binary protein-protein interaction map for the interactome network of the plant Arabidopsis thaliana containing about 6200 highly reliable interactions between about 2700 proteins is described.
Abstract: Plants have unique features that evolved in response to their environments and ecosystems. A full account of the complex cellular networks that underlie plant-specific functions is still missing. W...
850 citations
21 Aug 2011
TL;DR: It is shown that mobility measures alone yield surprising predictive power, comparable to traditional network-based measures, and the prediction accuracy can be significantly improved by learning a supervised classifier based on combined mobility and network measures.
Abstract: Our understanding of how individual mobility patterns shape and impact the social network is limited, but is essential for a deeper understanding of network dynamics and evolution. This question is largely unexplored, partly due to the difficulty in obtaining large-scale society-wide data that simultaneously capture the dynamical information on individual movements and social interactions. Here we address this challenge for the first time by tracking the trajectories and communication records of 6 Million mobile phone users. We find that the similarity between two individuals' movements strongly correlates with their proximity in the social network. We further investigate how the predictive power hidden in such correlations can be exploited to address a challenging problem: which new links will develop in a social network. We show that mobility measures alone yield surprising predictive power, comparable to traditional network-based measures. Furthermore, the prediction accuracy can be significantly improved by learning a supervised classifier based on combined mobility and network measures. We believe our findings on the interplay of mobility patterns and social ties offer new perspectives on not only link prediction but also network dynamics.
725 citations
TL;DR: The time evolution of information propagation is followed through communication networks by using empirical data on contact sequences and the susceptible-infected model and introducing null models where event sequences are appropriately shuffled to distinguish between the contributions of different impeding effects.
Abstract: While communication networks show the small-world property of short paths, the spreading dynamics in them turns out slow. Here, the time evolution of information propagation is followed through communication networks by using empirical data on contact sequences and the susceptible-infected model. Introducing null models where event sequences are appropriately shuffled, we are able to distinguish between the contributions of different impeding effects. The slowing down of spreading is found to be caused mainly by weight-topology correlations and the bursty activity patterns of individuals.
700 citations
TL;DR: It is found that small social groups are geographically very tight, but become much more clumped when the group size exceeds about 30 members, and the results suggest that spreading processes face distinct structural and spatial constraints.
Abstract: Social groups are fundamental building blocks of human societies. While our social interactions have always been constrained by geography, it has been impossible, due to practical difficulties, to evaluate the nature of this restriction on social group structure. We construct a social network of individuals whose most frequent geographical locations are also known. We also classify the individuals into groups according to a community detection algorithm. We study the variation of geographical span for social groups of varying sizes, and explore the relationship between topological positions and geographic positions of their members. We find that small social groups are geographically very tight, but become much more clumped when the group size exceeds about 30 members. Also, we find no correlation between the topological positions and geographic positions of individuals within network communities. These results suggest that spreading processes face distinct structural and spatial constraints.
369 citations
TL;DR: In this article, the authors explore societal response to external perturbations and identify real-time changes in communication and mobility patterns in the vicinity of eight emergencies, such as bomb attacks and earthquakes, comparing these with eight non-emergencies, like concerts and sporting events.
Abstract: Despite recent advances in uncovering the quantitative features of stationary human activity patterns, many applications, from pandemic prediction to emergency response, require an understanding of how these patterns change when the population encounters unfamiliar conditions. To explore societal response to external perturbations we identified real-time changes in communication and mobility patterns in the vicinity of eight emergencies, such as bomb attacks and earthquakes, comparing these with eight non-emergencies, like concerts and sporting events. We find that communication spikes accompanying emergencies are both spatially and temporally localized, but information about emergencies spreads globally, resulting in communication avalanches that engage in a significant manner the social network of eyewitnesses. These results offer a quantitative view of behavioral changes in human activity under extreme conditions, with potential long-term impact on emergency detection and response.
340 citations
TL;DR: This paper introduced a flavor network that captures the flavor compounds shared by culinary ingredients and found that Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis.
Abstract: The cultural diversity of culinary practice, as illustrated by the variety of regional cuisines, raises the question of whether there are any general patterns that determine the ingredient combinations used in food today or principles that transcend individual tastes and recipes. We introduce a flavor network that captures the flavor compounds shared by culinary ingredients. Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis. By contrast, East Asian cuisines tend to avoid compound sharing ingredients. Given the increasing availability of information on food preparation, our data-driven investigation opens new avenues towards a systematic understanding of culinary practice.
338 citations
Journal Article•
TL;DR: A flavor network is introduced that captures the flavor compounds shared by culinary ingredients, showing Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis.
Abstract: The cultural diversity of culinary practice, as illustrated by the variety of regional cuisines, raises the question of whether there are any general patterns that determine the ingredient combinations used in food today or principles that transcend individual tastes and recipes. We introduce a flavor network that captures the flavor compounds shared by culinary ingredients. Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis. By contrast, East Asian cuisines tend to avoid compound sharing ingredients. Given the increasing availability of information on food preparation, our data-driven investigation opens new avenues towards a systematic understanding of culinary practice.
TL;DR: Quantitative, holistic systems biology applied to human disease offers a unique approach for diagnosing established disease, defining disease predilection, and developing individualized (personalized) treatment strategies that can take full advantage of modern molecular pathobiology and the comprehensive data sets that are rapidly becoming available for populations and individuals.
Abstract: Contemporary views of human disease are based on simple correlation between clinical syndromes and pathological analysis dating from the late 19th century. Although this approach to disease diagnosis, prognosis, and treatment has served the medical establishment and society well for many years, it has serious shortcomings for the modern era of the genomic medicine that stem from its reliance on reductionist principles of experimentation and analysis. Quantitative, holistic systems biology applied to human disease offers a unique approach for diagnosing established disease, defining disease predilection, and developing individualized (personalized) treatment strategies that can take full advantage of modern molecular pathobiology and the comprehensive data sets that are rapidly becoming available for populations and individuals. In this way, systems pathobiology offers the promise of redefining our approach to disease and the field of medicine. 2011 John Wiley & Sons, Inc. WIREs Syst Biol Med2011 3 619‐627 DOI: 10.1002/wsbm.144
TL;DR: It is shown that for random networks the ranking provided by pagerank is sensitive to perturbations in the network topology, making it unreliable for incomplete or noisy systems, and in scale-free networks this is predicted analytically the emergence of super-stable nodes whose ranking is exceptionally stable toperturbations.
Abstract: Pagerank, a network-based diffusion algorithm, has emerged as the leading method to rank web content, ecological species and even scientists. Despite its wide use, it remains unknown how the structure of the network on which it operates affects its performance. Here we show that for random networks the ranking provided by pagerank is sensitive to perturbations in the network topology, making it unreliable for incomplete or noisy systems. In contrast, in scale-free networks we predict analytically the emergence of super-stable nodes whose ranking is exceptionally stable to perturbations. We calculate the dependence of the number of super-stable nodes on network characteristics and demonstrate their presence in real networks, in agreement with the analytical predictions. These results not only deepen our understanding of the interplay between network topology and dynamical processes but also have implications in all areas where ranking has a role, from science to marketing.
28 Mar 2011
TL;DR: In this article, the authors combine two related but distinct datasets, collected from a large scale privacy-preserving distributed social sensor system, and find that the social and organizational context significantly impacts to whom and how fast people forward information.
Abstract: Information spreading processes are central to human interactions. Despite recent studies in online domains, little is known about factors that could affect the dissemination of a single piece of information. In this paper, we address this challenge by combining two related but distinct datasets, collected from a large scale privacy-preserving distributed social sensor system. We find that the social and organizational context significantly impacts to whom and how fast people forward information. Yet the structures within spreading processes can be well captured by a simple stochastic branching model, indicating surprising independence of context. Our results build the foundation of future predictive models of information flow and provide significant insights towards design of communication platforms.
Book•
31 May 2011
TL;DR: Novel protein interactions are identified for CACNA1A and ATXN7 linking them to other ataxia-causing proteins and theAtaxia network, and potential pathways that can contribute to the pathophysiology of ataxIA, MD, and diseases comorbid with ataxian diseases are suggested.
Abstract: Spinocerebellar ataxias 6 and 7 (SCA6 and SCA7) are neurodegenerative disorders caused by expansion of CAG repeats encoding polyglutamine (polyQ) tracts in CACNA1A, the alpha1A subunit of the P/Q-type calcium channel, and ataxin-7 (ATXN7), a component of a chromatin-remodeling complex, respectively We hypothesized that finding new protein partners for ATXN7 and CACNA1A would provide insight into the biology of their respective diseases and their relationship to other ataxia-causing proteins We identified 118 protein interactions for CACNA1A and ATXN7 linking them to other ataxia-causing proteins and the ataxia network To begin to understand the biological relevance of these protein interactions within the ataxia network, we used OMIM to identify diseases associated with the expanded ataxia network We then used Medicare patient records to determine if any of these diseases co-occur with hereditary ataxia We found that patients with ataxia are at 303-fold greater risk of these diseases than Medicare patients overall One of the diseases comorbid with ataxia is macular degeneration (MD) The ataxia network is significantly (P 5 737 3 10 25 ) enriched for proteins that interact with known MD-causing proteins, forming a MD subnetwork We found that at least two of the proteins in the MD subnetwork have altered expression in the retina of Ataxin-7 266Q/1 mice suggesting an in vivo functional relationship with ATXN7 Together these data reveal novel protein interactions and suggest potential pathways that can contribute to the pathophysiology of ataxia, MD, and diseases comorbid with ataxia
Posted Content•
TL;DR: This article showed that the distribution of the number of events in a bursty period serves as a good indicator of the dependencies, leading to the universal observation of power-law distribution in a broad class of phenomena.
Abstract: Inhomogeneous temporal processes, like those appearing in human communications, neuron spike trains, and seismic signals, consist of high-activity bursty intervals alternating with long low-activity periods. In recent studies such bursty behavior has been characterized by a fat-tailed inter-event time distribution, while temporal correlations were measured by the autocorrelation function. However, these characteristic functions are not capable to fully characterize temporally correlated heterogenous behavior. Here we show that the distribution of the number of events in a bursty period serves as a good indicator of the dependencies, leading to the universal observation of power-law distribution in a broad class of phenomena. We find that the correlations in these quite different systems can be commonly interpreted by memory effects and described by a simple phenomenological model, which displays temporal behavior qualitatively similar to that in real systems.
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TL;DR: Muller and Schuppert as discussed by the authors showed that roughly 80% of the nodes must be controlled to gain full control over gene regulatory networks, but their result hides subtleties that reveal as much about controllability as about the limits of our current understanding of biological networks.
Abstract: Replying to F.-J. Muller & A. Schuppert , 10.1038/nature10543 (2011)
Muller and Schuppert1 describe an exception to our finding2 that roughly 80% of the nodes must be controlled to gain full control over gene regulatory networks. Yet our result hides subtleties that reveal as much about controllability as about the limits of our current understanding of biological networks.
Posted Content•
TL;DR: This article introduced a flavor network that captures the flavor compounds shared by culinary ingredients and found that Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis.
Abstract: The cultural diversity of culinary practice, as illustrated by the variety of regional cuisines, raises the question of whether there are any general patterns that determine the ingredient combinations used in food today or principles that transcend individual tastes and recipes. We introduce a flavor network that captures the flavor compounds shared by culinary ingredients. Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis. By contrast, East Asian cuisines tend to avoid compound sharing ingredients. Given the increasing availability of information on food preparation, our data-driven investigation opens new avenues towards a systematic understanding of culinary practice.
05 Sep 2011
TL;DR: It is shown that by measuring the entropy of each individuals trajectory, can explore the underlying predictability of human mobility, raising fundamental questions on how predictable the authors really are.
Abstract: A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to understand human activity patterns. I will discuss recent effort to explore human activity patterns, using the mobility of individuals as a proxy. As an application, I will show that by measuring the entropy of each individuals trajectory, we find can explore the underlying predictability of human mobility, raising fundamental questions on how predictable we really are. I will also discuss the interplay between human mobilty, social links, and the predictive power of data mining.