Showing papers by "Albert-László Barabási published in 2007"
TL;DR: This paper found that essential human genes are likely to encode hub proteins and are expressed widely in most tissues, while the vast majority of disease genes are non-essential and show no tendency to encoding hub proteins, and their expression pattern indicates that they are localized in the functional periphery of the network.
Abstract: A network of disorders and disease genes linked by known disorder-gene associations offers a platform to explore in a single graph-theoretic framework all known phenotype and disease gene associations, indicating the common genetic origin of many diseases. Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, supporting the existence of distinct disease-specific functional modules. We find that essential human genes are likely to encode hub proteins and are expressed widely in most tissues. This suggests that disease genes also would play a central role in the human interactome. In contrast, we find that the vast majority of disease genes are nonessential and show no tendency to encode hub proteins, and their expression pattern indicates that they are localized in the functional periphery of the network. A selection-based model explains the observed difference between essential and disease genes and also suggests that diseases caused by somatic mutations should not be peripheral, a prediction we confirm for cancer genes.
2,793 citations
Journal Article•
TL;DR: It is found that essential human genes are likely to encode hub proteins and are expressed widely in most tissues, suggesting that disease genes also would play a central role in the human interactome, and that diseases caused by somatic mutations should not be peripheral.
Abstract: A network of disorders and disease genes linked by known disorder–gene associations offers a platform to explore in a single graph-theoretic framework all known phenotype and disease gene associations, indicating the common genetic origin of many diseases. Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, supporting the existence of distinct disease-specific functional modules. We find that essential human genes are likely to encode hub proteins and are expressed widely in most tissues. This suggests that disease genes also would play a central role in the human interactome. In contrast, we find that the vast majority of disease genes are nonessential and show no tendency to encode hub proteins, and their expression pattern indicates that they are localized in the functional periphery of the network. A selection-based model explains the observed difference between essential and disease genes and also suggests that diseases caused by somatic mutations should not be peripheral, a prediction we confirm for cancer genes.
2,687 citations
TL;DR: This study studies this network of relatedness between products, or “product space,” finding that more-sophisticated products are located in a densely connected core whereas less-sophile products occupy a less-connected periphery.
Abstract: Economies grow by upgrading the products they produce and export. The technology, capital, institutions, and skills needed to make newer products are more easily adapted from some products than from others. Here, we study this network of relatedness between products, or “product space,” finding that more-sophisticated products are located in a densely connected core whereas less-sophisticated products occupy a less-connected periphery. Empirically, countries move through the product space by developing goods close to those they currently produce. Most countries can reach the core only by traversing empirically infrequent distances, which may help explain why poor countries have trouble developing more competitive exports and fail to converge to the income levels of rich countries.
2,152 citations
TL;DR: It is found that, when it comes to information diffusion, weak and strong ties are both simultaneously ineffective, and this coupling significantly slows the diffusion process, resulting in dynamic trapping of information in communities.
Abstract: ncovering the structure and function of communication networks has always been constrained by the practical difficulty of mapping out interactions among a large number of individuals. Indeed, most of our current understanding of com- munication and social networks is based on questionnaire data, reaching typically a few dozen individuals and relying on the individual's opinion to reveal the nature and the strength of the ties. The fact that currently an increasing fraction of human interactions are recorded, from e-mail (1-3) to phone records (4), offers unprecedented opportunities to uncover and explore the large scale characteristics of communication and social networks (5). Here we take a first step in this direction by exploiting the widespread use of mobile phones to construct a map of a society-wide communication network, capturing the mobile interaction patterns of millions of individuals. The data set allows us to explore the relationship between the topology of the network and the tie strengths between individuals, informa- tion that was inaccessible at the societal level before. We demonstrate a local coupling between tie strengths and network topology, and show that this coupling has important conse- quences for the network's global stability if ties are removed, as well as for the spread of news and ideas within the network. A significant portion of a country's communication network wasreconstructedfrom18weeksofallmobilephonecallrecords among 20% of the country's entire population, 90% of whose
1,920 citations
TL;DR: The focus is on networks capturing the collaboration between scientists and the calls between mobile phone users, and it is found that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability.
Abstract: The rich set of interactions between individuals in society results in complex community structure, capturing highly connected circles of friends, families or professional cliques in a social network. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimization of society as a whole. We have developed an algorithm based on clique percolation that allows us to investigate the time dependence of overlapping communities on a large scale, and thus uncover basic relationships characterizing community evolution. Our focus is on networks capturing the collaboration between scientists and the calls between mobile phone users. We find that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability. The behaviour of small groups displays the opposite tendency-the condition for stability is that their composition remains unchanged. We also show that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime. These findings offer insight into the fundamental differences between the dynamics of small groups and large institutions.
1,676 citations
[...]
TL;DR: A bipartite graph composed of US Food and Drug Administration–approved drugs and proteins linked by drug–target binary associations is built, showing an overabundance of 'follow-on' drugs, that is, drugs that target already targeted proteins.
Abstract: The global set of relationships between protein targets of all drugs and all disease-gene products in the human protein-protein interaction or 'interactome' network remains uncharacterized. We built a bipartite graph composed of US Food and Drug Administration-approved drugs and proteins linked by drug-target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types according to Anatomical Therapeutic Chemical classification. Topological analyses of this network quantitatively showed an overabundance of 'follow-on' drugs, that is, drugs that target already targeted proteins. By including drugs currently under investigation, we identified a trend toward more functionally diverse targets improving polypharmacology. To analyze the relationships between drug targets and disease-gene products, we measured the shortest distance between both sets of proteins in current models of the human interactome network. Significant differences in distance were found between etiological and palliative drugs. A recent trend toward more rational drug design was observed.
1,592 citations
TL;DR: The purpose of this perspective is to provide a logical basis for a new approach to classifying human disease that uses conventional reductionism and incorporates the non‐reductionist approach of systems biomedicine.
Abstract: Contemporary classification of human disease derives from observational correlation between pathological analysis and clinical syndromes. Characterizing disease in this way established a nosology that has served clinicians well to the current time, and depends on observational skills and simple laboratory tools to define the syndromic phenotype. Yet, this time-honored diagnostic strategy has significant shortcomings that reflect both a lack of sensitivity in identifying preclinical disease, and a lack of specificity in defining disease unequivocally. In this paper, we focus on the latter limitation, viewing it as a reflection both of the different clinical presentations of many diseases (variable phenotypic expression), and of the excessive reliance on Cartesian reductionism in establishing diagnoses. The purpose of this perspective is to provide a logical basis for a new approach to classifying human disease that uses conventional reductionism and incorporates the non-reductionist approach of systems biomedicine.
555 citations
TL;DR: A recent study reported that among people who carried a single copy of the high-risk allele for the FTO gene, which is associated with fat mass and obesity, the risk of obesity increased by 30%.
Abstract: A recent study reported that among people who carried a single copy of the high-risk allele for the FTO gene, which is associated with fat mass and obesity, the risk of obesity increased by 30%. The risk of obesity increased by 67% among people who carried two alleles, and on average they gained 3.0 kg (6.6 lb) or more.1 Given that approximately one sixth of the population of European descent is homozygous for this allele, this link between the FTO gene and obesity appears to be one of the strongest genotype–phenotype associations detected by modern genome-screening techniques. That obesity has . . .
480 citations
TL;DR: A connected network of 3.9 million nodes from mobile phone call records is constructed, which can be regarded as a proxy for the underlying human communication network at the societal level and a positive correlation between the overlap and weight of a link is reported.
Abstract: We construct a connected network of 3.9 million nodes from mobile phone call records, which can be regarded as a proxy for the underlying human communication network at the societal level. We assign two weights on each edge to reflect the strength of social interaction, which are the aggregate call duration and the cumulative number of calls placed between the individuals over a period of 18 weeks. We present a detailed analysis of this weighted network by examining its degree, strength, and weight distributions, as well as its topological assortativity and weighted assortativity, clustering and weighted clustering, together with correlations between these quantities. We give an account of motif intensity and coherence distributions and compare them to a randomized reference system. We also use the concept of link overlap to measure the number of common neighbours any two adjacent nodes have, which serves as a useful local measure for identifying the interconnectedness of communities. We report a positive correlation between the overlap and weight of a link, thus providing
422 citations
TL;DR: It is shown that the non-Poisson nature of the contact dynamics results in prevalence decay times significantly larger than predicted by the standard Poisson process based models.
Abstract: Halting a computer or biological virus outbreak requires a detailed understanding of the timing of the interactions between susceptible and infected individuals. While current spreading models assume that users interact uniformly in time, following a Poisson process, a series of recent measurements indicates that the intercontact time distribution is heavy tailed, corresponding to a temporally inhomogeneous bursty contact process. Here we show that the non-Poisson nature of the contact dynamics results in prevalence decay times significantly larger than predicted by the standard Poisson process based models. Our predictions are in agreement with the detailed time resolved prevalence data of computer viruses, which, according to virus bulletins, show a decay time close to a year, in contrast with the 1 day decay predicted by the standard Poisson process based models.
365 citations
TL;DR: A flux balance model of Escherichia coli cell metabolism that takes into account a systems-level constraint for the concentration of enzymes catalyzing the various metabolic reactions in the crowded cytoplasm suggests that molecular crowding represents a bound on the achievable functional states of a metabolic network.
Abstract: The influence of the high intracellular concentration of macromolecules on cell physiology is increasingly appreciated, but its impact on system-level cellular functions remains poorly quantified. To assess its potential effect, here we develop a flux balance model of Escherichia coli cell metabolism that takes into account a systems-level constraint for the concentration of enzymes catalyzing the various metabolic reactions in the crowded cytoplasm. We demonstrate that the model's predictions for the relative maximum growth rate of wild-type and mutant E. coli cells in single substrate-limited media, and the sequence and mode of substrate uptake and utilization from a complex medium are in good agreement with subsequent experimental observations. These results suggest that molecular crowding represents a bound on the achievable functional states of a metabolic network, and they indicate that models incorporating this constraint can systematically identify alterations in cellular metabolism activated in response to environmental change.
Posted Content•
TL;DR: This article study the network of relatedness between products, or product space, finding that most upscale products are located in a densely connected core while lower income products occupy a less connected periphery.
Abstract: Economies grow by upgrading the type of products they produce and export. The technology, capital, institutions and skills needed to make such new products are more easily adapted from some products than others. We study the network of relatedness between products, or product space, finding that most upscale products are located in a densely connected core while lower income products occupy a less connected periphery. We show that countries tend to move to goods close to those they are currently specialized in, allowing nations located in more connected parts of the product space to upgrade their exports basket more quickly. Most countries can reach the core only if they jump over empirically infrequent distances in the product space. This may help explain why poor countries have trouble developing more competitive exports, failing to converge to the income levels of rich countries.
TL;DR: It is shown that when nodes in a network belong to two distinct classes, two independent parameters are needed to capture the detailed interplay between the network structure and node properties, and it is found that thenetwork structure significantly limits the values of these parameters.
Abstract: Our enhanced ability to map the structure of various complex networks is increasingly accompanied by the possibility of independently identifying the functional characteristics of each node. Although this led to the observation that nodes with similar characteristics have a tendency to link to each other, in general we lack the tools to quantify the interplay between node properties and the structure of the underlying network. Here we show that when nodes in a network belong to two distinct classes, two independent parameters are needed to capture the detailed interplay between the network structure and node properties. We find that the network structure significantly limits the values of these parameters, requiring a phase diagram to uniquely characterize the configurations available to the system. The phase diagram shows a remarkable independence from the network size, a finding that, together with a proposed heuristic algorithm, allows us to determine its shape even for large networks. To test the usefulness of the developed methods, we apply them to biological and socioeconomic systems, finding that protein functions and mobile phone usage occupy distinct regions of the phase diagram, indicating that the proposed parameters have a strong discriminating power.
TL;DR: It is shown that a core neuronal C. elegans core neuronal protein-DNA interaction network is organized into two TF modules, which contain TFs that bind to a relatively small number of target genes and are more systems specific than the TF hubs that connect the modules.
Abstract: Transcription regulatory networks play a pivotal role in the development, function, and pathology of metazoan organisms Such networks are comprised of protein–DNA interactions between transcription factors (TFs) and their target genes An important question pertains to how the architecture of such networks relates to network functionality Here, we show that a Caenorhabditis elegans core neuronal protein–DNA interaction network is organized into two TF modules These modules contain TFs that bind to a relatively small number of target genes and are more systems specific than the TF hubs that connect the modules Each module relates to different functional aspects of the network One module contains TFs involved in reproduction and target genes that are expressed in neurons as well as in other tissues The second module is enriched for paired homeodomain TFs and connects to target genes that are often exclusively neuronal We find that paired homeodomain TFs are specifically expressed in C elegans and mouse neurons, indicating that the neuronal function of paired homeodomains is evolutionarily conserved Taken together, we show that a core neuronal C elegans protein–DNA interaction network possesses TF modules that relate to different functional aspects of the complete network
27 May 2007
TL;DR: A prototype emergency and disaster information system designed and implemented using DDDAS concepts, WIPER, which is designed to use real-time cell phone calling data from a geographical region to provide enhanced situational awareness for managers in emergency operations centers (EOCs) during disaster events.
Abstract: We describe a prototype emergency and disaster information system designed and implemented using DDDAS concepts. The system is designed to use real-time cell phone calling data from a geographical region, including calling activity --- who calls whom, call duration, services in use, and cell phone location information --- to provide enhanced situational awareness for managers in emergency operations centers (EOCs) during disaster events. Powered-on cell phones maintain contact with one or more within-range cell towers so as to receive incoming calls. Thus, location data about all phones in an area are available, either directly from GPS equipped phones, or by cell tower, cell sector, distance from tower and triangulation methods. This permits the cell phones of a geographical region to serve as an ad hoc mobile sensor net, measuring the movement and calling patterns of the population. A prototype system, WIPER, serves as a test bed to research open DDDAS design issues, including dynamic validation of simulations, algorithms to interpret high volume data streams, ensembles of simulations, runtime execution, middleware services, and experimentation frameworks [1].
[...]
TL;DR: Data on the movement of people becomes ever more detailed, but robust models explaining the observed patterns are still needed and mapping the problem onto a 'network of networks' could be a promising approach.
Abstract: Data on the movement of people becomes ever more detailed, but robust models explaining the observed patterns are still needed. Mapping the problem onto a 'network of networks' could be a promising approach.
Journal Article•
TL;DR: In this article, the authors investigate the mean collective behavior at large scales and focus on the occurrence of anomalous events using standard percolation theory tools, and investigate patterns of calling activity at individual level and show that the interevent time of consecutive calls is heavy-tailed.
Abstract: Novel aspects of human dynamics and social interactions are investigated by means of mobile phone data. Using extensive phone records resolved in both time and space, we study the mean collective behavior at large scales and focus on the occurrence of anomalous events. We discuss how these spatiotemporal anomalies can be described using standard percolation theory tools. We also investigate patterns of calling activity at the individual level and show that the interevent time of consecutive calls is heavy-tailed. This finding, which has implications for dynamics of spreading phenomena in social networks, agrees with results previously reported on other human activities.
TL;DR: In this paper, the authors study the statistical properties of community dynamics in large social networks, where the evolving communities are obtained from subsequent snapshots of the modular structure and find significant difference between the behavior of smaller collaborative or friendship circles and larger communities, eg. institutions.
Abstract: We study the statistical properties of community dynamics in large social networks, where the evolving communities are obtained from subsequent snapshots of the modular structure. Such cohesive groups of people can grow by recruiting new members, or contract by loosing members; two (or more) groups may merge into a single community, while a large enough social group can split into several smaller ones; new communities are born and old ones may disappear. We find significant difference between the behavior of smaller collaborative or friendship circles and larger communities, eg. institutions. Social groups containing only a few members persist longer on average when the fluctuations of the members is small. In contrast, we find that the condition for stability for large communities is continuous changes in their membership, allowing for the possibility that after some time practically all members are exchanged.
07 Jun 2007
TL;DR: In this paper, the authors study the statistical properties of community dynamics in large social networks, where the evolving communities are obtained from subsequent snapshots of the modular structure and find significant difference between the behaviour of smaller collaborative or friendship circles and larger communities, eg. institutions.
Abstract: We study the statistical properties of community dynamics in large social networks, where the evolving communities are obtained from subsequent snapshots of the modular structure. Such cohesive groups of people can grow by recruiting new members, or contract by loosing members; two (or more) groups may merge into a single community, while a large enough social group can split into several smaller ones; new communities are born and old ones may disappear. We find significant difference between the behaviour of smaller collaborative or friendship circles and larger communities, eg. institutions. Social groups containing only a few members persist longer on average when the fluctuations of the members is small. In contrast, we find that the condition for stability for large communities is continuous changes in their membership, allowing for the possibility that after some time practically all members are exchanged.