Research Interests:
Research Interests:
This report comprises the complete D5.4.1 deliverable as specified for workpackage WP5.4 in Subproject SP5 of the DELIS (Dynamically Evolving Large-scale Information Systems) Integrated Project. The essential goal of the DELIS project is... more
This report comprises the complete D5.4.1 deliverable as specified for workpackage WP5.4 in Subproject SP5 of the DELIS (Dynamically Evolving Large-scale Information Systems) Integrated Project. The essential goal of the DELIS project is to understand, predict, engineer and control large evolving information systems. In this workpackage we wish to tie together, hitherto, independent research lines on evolving network “form” and “function”. Both in naturally found networks (e.g., biological and software networks) and artificial networks (e.g., dynamic peer-to-peer networks) there appear to be various levels of selection and evolution and interesting relationships between form and function. It is becoming increasingly clear that form does not follow directly from function (or vice versa) in many observed evolving networks. What then is the relationship and how can it be detected and characterised? We summarise two main lines of work, the first section covers detailed analysis of motif...
This report comprises the complete D5.2.1 deliverable as specified for workpackage WP5.2 in Subproject SP5 of the DELIS (Dynamically Evolving Large-scale Information Systems) Integrated Project. The essential goal of the DELIS project is... more
This report comprises the complete D5.2.1 deliverable as specified for workpackage WP5.2 in Subproject SP5 of the DELIS (Dynamically Evolving Large-scale Information Systems) Integrated Project. The essential goal of the DELIS project is to understand, predict, engineer and control large evolving information systems. The main aim of this workpackage is to understand how evolved structures emerge in networks when there is no central design or control. Complex networks emerge under different conditions including design (i.e., top-down decisions) through simple rules of growth and evolution. Such rules are typically local when dealing with biological systems and most social webs. An important deviation from such scenario is provided by groups, collectives of agents engaged in technology development, such as open source (OS) communities. Here we analyze their network structure, showing that it defines a complex weighted network with scaling laws at different levels, as measured by looki...
The emergence of complex patterns of organization close to the Cambrian boundary is known to have happened over a (geologically) short period of time. It involved the rapid diversification of body plans and stands as one of the major... more
The emergence of complex patterns of organization close to the Cambrian boundary is known to have happened over a (geologically) short period of time. It involved the rapid diversification of body plans and stands as one of the major transitions in evolution. How it took place is a controversial issue. Here we explore this problem by considering a simple model of pattern formation in multicellular organisms. By modeling gene network-based morphogenesis and its evolution through adaptive walks, we explore the question of how combinatorial explosions might have been actually involved in the Cambrian event. Here we show that a small amount of genetic complexity including both gene regulation and cell-cell signaling allows one to generate an extraordinary repertoire of stable spatial patterns of gene expression compatible with observed anteroposterior patterns in early development of metazoans. The consequences for the understanding of the tempo and mode of the Cambrian event are outlined.
Research Interests: Evolutionary Biology, Genetics, Algorithms, Developmental Biology, Gene regulation, and 15 moreBiology, Cell Signaling, Gene expression, Biological Sciences, Computer Simulation, Fossils, Animals, Early development, Gene networks, Gene Network, Gene, Evo Devo, Cambrian explosion, Body Plan, and Cell communication
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Liquid neural networks (or “liquid brains”) are a widespread class of cognitive living networks characterised by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent... more
Liquid neural networks (or “liquid brains”) are a widespread class of cognitive living networks characterised by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent connections are maintained, in contrast with standard neural systems. How is this class of systems capable of displaying cognitive abilities, from learning to decision-making? In this paper, the collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics. Using a comparative approach, we review the generic properties of three large classes of systems, namely: standard neural networks (“solid brains”), ant colonies and the immune system. It is shown that, despite their intrinsic physical differences, these systems share key properties with standard neural systems in terms of formal descriptions, but strongly depart in other ways. On one hand, the attractors found in liquid brains are not alwa...
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Mutualistic networks have been shown to involve complex patterns of interactions among animal and plant species, including a widespread presence of nestedness. The nested structure of these webs seems to be positively correlated with... more
Mutualistic networks have been shown to involve complex patterns of interactions among animal and plant species, including a widespread presence of nestedness. The nested structure of these webs seems to be positively correlated with higher diversity and resilience. Moreover, these webs exhibit marked measurable structural patterns, including broad distributions of connectivity, strongly asymmetrical interactions and hierarchical organization. Hierarchical organization is an especially interesting property, since it is positively correlated with biodiversity and network resilience, thus suggesting potential selection processes favouring the observed web organization. However, here we show that all these structural quantitative patterns-and nestedness in particular-can be properly explained by means of a very simple dynamical model of speciation and divergence with no selection-driven coevolution of traits. The agreement between observed and modelled networks suggests that the patter...
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Research Interests: Computer Science, Phonetics, Language Disorder, Biological Sciences, Complex System, and 13 moreHumans, Scale-Free Networks, Mathematical Computing, Biological evolution, Human Brain, Scaling, Graph, Construction Process, Small World, Scale free Networks, Human language, Medical and Health Sciences, and Lexica
Research Interests: Mathematics, Computer Science, Physics, Biology, Ecology, and 15 moreEcological Networks, Biodiversity, Neural Network, Medicine, Food webs, Biological Sciences, Quantitative Biology, Food web, Keystone species, Extinction, Ecosystem, Fragility, Ecological Network, Food Chain, and Medical and Health Sciences
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Research Interests: Evolutionary Biology, Computer Science, Computer Graphics, Computational Biology, Biology, and 15 moreCell Cycle, Complex Networks, Comparative Study, Medicine, Signal Transduction, Saccharomyces cerevisiae, Escherichia coli, Gene Regulatory Networks, Transcription Factor, Hierarchy, Bacillus subtilis, Top Down, Quantitative Method, Metabolic control, and Biochemistry and cell biology
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Research Interests: Mathematics, Catalysis, Molecular Evolution, Statistical Physics, Medicine, and 14 moreTheoretical biology, Phase Transitions, Cell Division, Stochastic processes, Biological Sciences, Mathematical Sciences, Animals, Bifurcation, Metapopulation, Phase transition, Hypercycles, Metapopulations, Saddle, and Bifurcations
SUMMARY Knowledge of the genetic control of segmentation in Drosophila has made insect segmentation a paradigmatic case in the study of the evolution of developmental mechanisms. In Drosophila, the patterns of expression of segmentation... more
SUMMARY Knowledge of the genetic control of segmentation in Drosophila has made insect segmentation a paradigmatic case in the study of the evolution of developmental mechanisms. In Drosophila, the patterns of expression of segmentation genes are established simultaneously in all segments by a complex set of interactions between transcriptional factors that diffuse in a syncytium occupying the whole embryo. Such mechanisms cannot act in short germ‐band insects where segments appear sequentially from a cellularized posterior proliferative zone. Here, we compare mechanisms of segmentation in different organisms and discuss how the transition between the different types of segmentation can be explained by small and progressive changes in the underlying gene networks. The recent discovery of a temporal oscillation in expression during somitogenesis of vertebrate homologs of the pair‐rule gene hairy enhances the plausibility of an earlier proposal that the evolutionary origin of both the...
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Research Interests: Physics, Interstellar Medium, Data Analysis, Astrobiology, Modular Systems (Architecture), and 12 moreMathematical Sciences, Scale-Free Networks, Complex network, Physical sciences, EPL, Scale free network, Chemical Reaction, Large Scale, Metabolic pathway, Small World, Modular Design, and Scale free Networks
Networks predate complexity, from biology to society and technology [1]. In many cases, large-scale, systemlevel properties emerge from local (bottom-up) interactions among network components. This is consistent with the general lack of... more
Networks predate complexity, from biology to society and technology [1]. In many cases, large-scale, systemlevel properties emerge from local (bottom-up) interactions among network components. This is consistent with the general lack of global goals that pervade ...
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Research Interests: Computer Science, Condensed Matter Physics, Urban Planning, Urban Studies, Complex System, and 13 moreMathematical Sciences, Theoretical Condensed Matter Physics, Complex network, Physical sciences, Robustness (evolution), Spatial Networks, Fragility (Networks), Spatial Distribution, Self Organization, Human Settlement, Shortest Path Problem, Inverse, and Shortest Path
Research Interests: Computer Science, Distributed Computing, Condensed Matter Physics, Behavior, Complex Networks, and 15 moreFood webs, Mathematical Sciences, Complex network, Physical sciences, Number, Cycles, Ant Tunneling Networks, Empirical Study, Scale free network, Nest, Size, Minimal Spanning Tree, Brood, Harvester Ant, and Growth mechanism
Owed to their reduced size and low number of proteins encoded, RNA viruses and other subviral pathogens are often considered as being genetically too simple. However, this structural simplicity also creates the necessity for viral RNA... more
Owed to their reduced size and low number of proteins encoded, RNA viruses and other subviral pathogens are often considered as being genetically too simple. However, this structural simplicity also creates the necessity for viral RNA sequences to encode for more than one protein and for proteins to carry out multiple functions, all together resulting in complex patterns of genetic interactions. In this work we will first review the experimental studies revealing that the architecture of viral genomes is dominated by antagonistic interactions among loci. Second, we will also review mathematical models and provide a description of computational tools for the study of RNA virus dynamics and evolution. As an application of these tools, we will finish this review article by analyzing a stochastic bit-string model of in silico virus replication. This model analyzes the interplay between epistasis and the mode of replication on determining the population load of deleterious mutations. The...
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Research Interests: Applied Mathematics, Computer Science, Biology, Complexity, Evolution, and 10 moreComplex Networks, Technological Innovation, Information Processing, Complex network, Biological evolution, Tinkering (Evolution), Biological Network, Life-Form, Community Networks, and Numerical Analysis and Computational Mathematics
When computers started to become a dominant part of technology around the 1950s, fundamental questions about reliable designs and robustness were of great relevance. Their development gave rise to the exploration of new questions, such as... more
When computers started to become a dominant part of technology around the 1950s, fundamental questions about reliable designs and robustness were of great relevance. Their development gave rise to the exploration of new questions, such as what made brains reliable (since neurons can die) and how computers could get inspiration from neural systems. In parallel, the first artificial neural networks came to life. Since then, the comparative view between brains and computers has been developed in new, sometimes unexpected directions. With the rise of deep learning and the development of connectomics, an evolutionary look at how both hardware and neural complexity have evolved or designed is required. In this paper, we argue that important similarities have resulted both from convergent evolution (the inevitable outcome of architectural constraints) and inspiration of hardware and software principles guided by toy pictures of neurobiology. Moreover, dissimilarities and gaps originate fro...
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Human language defines the most complex outcomes of evolution. The emergence of such an elaborated form of communication allowed humans to create extremely structured societies and manage symbols at different levels including, among... more
Human language defines the most complex outcomes of evolution. The emergence of such an elaborated form of communication allowed humans to create extremely structured societies and manage symbols at different levels including, among others, semantics. All linguistic levels have to deal with an astronomic combinatorial potential that stems from the recursive nature of languages. This recursiveness is indeed a key defining trait. However, not all words are equally combined nor frequent. In breaking the symmetry between less and more often used and between less and more meaning-bearing units, universal scaling laws arise. Such laws, common to all human languages, appear on different stages from word inventories to networks of interacting words. Among these seemingly universal traits exhibited by language networks, ambiguity appears to be a specially relevant component. Ambiguity is avoided in most computational approaches to language processing, and yet it seems to be a crucial element...
Viruses have established symbiotic relationships with almost every other living organism on Earth and at all levels of biological organization, from other viruses up to entire ecosystems. In most cases, peacefully coexisting with their... more
Viruses have established symbiotic relationships with almost every other living organism on Earth and at all levels of biological organization, from other viruses up to entire ecosystems. In most cases, peacefully coexisting with their hosts, but in most relevant cases, parasitizing them and inducing diseases. Viruses are playing an essential role in shaping the eco-evolutionary dynamics of their hosts, and also have been involved in some of the major evolutionary innovations either by working as vectors of genetic information or by being themselves coopted by the host into their genomes. Viruses can be studied at different levels of biological organization, from the molecular mechanisms of genome replication, gene expression and encapsidation to global pandemics. All these levels are different and yet connected through the presence of threshold conditions allowing for the formation of a capsid, the loss of genetic information or epidemic spreading. These thresholds, as it occurs wi...
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A common trait of complex systems is that they can be represented by means of a network of interacting parts. It is, in fact, the network organization (more than the parts) that largely conditions most higher-level properties, which are... more
A common trait of complex systems is that they can be represented by means of a network of interacting parts. It is, in fact, the network organization (more than the parts) that largely conditions most higher-level properties, which are not reducible to the properties of the individual parts. Can the topological organization of these webs provide some insight into their evolutionary origins? Both biological and artificial networks share some common architectural traits. They are often heterogeneous and sparse, and most exhibit different types of correlations, such as nestedness, modularity or hierarchical patterns. These properties have often been attributed to the selection of functionally meaningful traits. However, a proper formulation of generative network models suggests a rather different picture. Against the standard selection–optimization argument, some networks reveal the inevitable generation of complex patterns resulting from reuse and can be modelled using duplication–re...
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What is the nature of language? How has it evolved in different species? Are there qualitative, well-defined classes of languages? Most studies of language evolution deal in a way or another with such theoretical contraption and explore... more
What is the nature of language? How has it evolved in different species? Are there qualitative, well-defined classes of languages? Most studies of language evolution deal in a way or another with such theoretical contraption and explore the outcome of diverse forms of selection on the communication matrix that somewhat optimizes communication. This framework naturally introduces networks mediating the communicating agents, but no systematic analysis of the underlying landscape of possible language graphs has been developed. Here we present a detailed analysis of network properties on a generic model of a communication code, which reveals a rather complex and heterogeneous morphospace of language graphs. Additionally, we use curated data of English words to locate and evaluate real languages within this morphospace. Our findings indicate a surprisingly simple structure in human language unless particles with the ability of naming any other concept are introduced in the vocabulary. Th...
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Research Interests: Computer Science, Artificial Intelligence, Communication, Language Acquisition, Scaling (Biology), and 15 moreChild Development, Language Evolution, Syntax, Biology, Language Development, Complex Networks, Language Change, Communication System, Scale-Free Networks, Complex network, Cognitive Process, Small World, Abstract Syntax Tree, Springer Ebooks, and Scale free Networks
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Complex networks in both nature and technology have been shown to display characteristic, small subgraphs (so-called motifs) which appear to be related to their underlying functionality. All these networks share a common trait: they... more
Complex networks in both nature and technology have been shown to display characteristic, small subgraphs (so-called motifs) which appear to be related to their underlying functionality. All these networks share a common trait: they manipulate information at different scales in order to perform some kind of computation. Here we analyze a large set of software class diagrams and show that several highly frequent network motifs appear to be a consequence of network heterogeneity and size, thus suggesting a somewhat less relevant role of functionality. However, by using a simple model of network growth by duplication and rewiring, it is shown the rules of graph evolution seem to be largely responsible for the observed motif distribution.
Research Interests: Engineering, Computer Science, Object Oriented Programming, Open Source Software, Software Evolution, and 15 moreSoftware Architecture, Design Patterns, Complex Networks, Science and Technology, Medicine, Software, Complex System, Mathematical Sciences, Case Study, Complex network, Physical sciences, Software Networks, Graph, Class Diagram, and Scale free Networks
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Research Interests: Neuroscience, Physics, Biophysics, Thermodynamics, Nonlinear dynamics, and 15 moreBiology, Medical Physics, Statistical Physics, Medicine, Stochastic processes, Nonlinear Systems, Brownian Motion, Oscillations, Physical, Neurons, Bistability, Stochastic Resonance, Artificial Neural Network, Física estadística, and multivibrator
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Human language defines the most complex outcomes of evolution. The emergence of such an elaborated form of communication allowed humans to create extremely structured societies and manage symbols at different levels including, among... more
Human language defines the most complex outcomes of evolution. The emergence of such an elaborated form of communication allowed humans to create extremely structured societies and manage symbols at different levels including, among others, semantics. All linguistic levels have to deal with an astronomic combinatorial potential that stems from the recursive nature of languages. This recursiveness is indeed a key defining trait. However, not all words are equally combined nor frequent. In breaking the symmetry between less and more often used and between less and more meaning-bearing units, universal scaling laws arise. Such laws, common to all human languages, appear on different stages from word inventories to networks of interacting words. Among these seemingly universal traits exhibited by language networks, ambiguity appears to be a specially relevant component. Ambiguity is avoided in most computational approaches to language processing, and yet it seems to be a crucial element...
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Patterns of protein interactions are organized around complex heterogeneous networks. Their architecture has been suggested to be of relevance in understanding the interactome and its functional organization, which pervades cellular... more
Patterns of protein interactions are organized around complex heterogeneous networks. Their architecture has been suggested to be of relevance in understanding the interactome and its functional organization, which pervades cellular robustness. Transcription factors are particularly relevant in this context, given their central role in gene regulation. Here we present the first topological study of the human protein–protein interacting transcription factor network built using the TRANSFAC database. We show that the network exhibits scale‐free and small‐world properties with a hierarchical and modular structure, which is built around a small number of key proteins. Most of these proteins are associated with proliferative diseases and are typically not linked to each other, thus reducing the propagation of failures through compartmentalization. Network modularity is consistent with common structural and functional features and the features are generated by two distinct evolutionary st...
Research Interests: Algorithms, Computational Biology, Gene regulation, Molecular Evolution, Biology, and 15 moreComplex Networks, Transcription Factors, Medicine, Protein Structure and Function, Humans, Molecular Phylogenetics and Evolution, Gene Regulatory Networks, Transcription Factor, Interactome, Protein Interaction, Evolutionary Strategy, Small World, Biochemistry and cell biology, Scale free Networks, and Medical biochemistry and metabolomics
Hierarchy seems to pervade complexity in both living and artificial systems. Despite its relevance, no general theory that captures all features of hierarchy and its origins has been proposed yet. Here we present a formal approach... more
Hierarchy seems to pervade complexity in both living and artificial systems. Despite its relevance, no general theory that captures all features of hierarchy and its origins has been proposed yet. Here we present a formal approach resulting from the convergence of theoretical morphology and network theory that allows constructing a 3D morphospace of hierarchies and hence comparing the hierarchical organization of ecological, cellular, technological, and social networks. Embedded within large voids in the morphospace of all possible hierarchies, four major groups are identified. Two of them match the expected from random networks with similar connectivity, thus suggesting that nonadaptive factors are at work. Ecological and gene networks define the other two, indicating that their topological order is the result of functional constraints. These results are consistent with an exploration of the morphospace, using in silico evolved networks.
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Research Interests: Engineering, Computer Science, Physics, Complex Systems Science, Distributed Algorithms, and 15 moreFormal methods, Medicine, Cloud Computing, Fault Tolerance, High Voltage, Complex System, Mathematical Sciences, Complex network, Physical sciences, Grid, Fragility, Dynamic behaviour of materials, Demand Side Resource Management, Electric Power Transmission, and Dynamic Behaviour
Research Interests: Engineering, Mathematics, Computer Science, Optimization (Mathematics), Social insects, and 13 moreMedicine, Percolation, Complex System, Mathematical Sciences, Lattice Beam Model, Physical sciences, Graph Representation, Termite Nests, Spatial Networks, Percolation threshold, Insect, Transportation Networks, and Driving force
Research Interests: Engineering, Mathematics, Computer Science, Open Source Software, Nonlinear dynamics, and 15 moreNetwork Analysis, Complex Networks, Medicine, Open Source, Economic System, Mathematical Sciences, Complex network, Hierarchy of Influences, Centrality, Hierarchical Organization, Hierarchy, Biological systems, Betweenness Centrality, Network structure, and Computational Method
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Research Interests: Genomics, Computational Biology, Biology, Evolution, Complex Networks, and 15 moreMedicine, Biological Sciences, Genome evolution, Mathematical Sciences, Animals, Complex network, Cellular Network, Genome, Gene, Gene Duplication, Evolution Biology, Large Scale, Metabolic pathway, Genomes, and Hybrid Method
Modularity is known to be one of the most relevant characteristics of biological systems and appears to be present at multiple scales. Given its adaptive potential, it is often assumed to be the target of selective pressures. Under such... more
Modularity is known to be one of the most relevant characteristics of biological systems and appears to be present at multiple scales. Given its adaptive potential, it is often assumed to be the target of selective pressures. Under such interpretation, selection would be actively favouring the formation of modular structures, which would specialize in different functions. Here we show that, within the context of cellular networks, no such selection pressure is needed to obtain modularity. Instead, the intrinsic dynamics of network growth by duplication and diversification is able to generate it for free and explain the statistical features exhibited by small subgraphs. The implications for the evolution and evolvability of both biological and technological systems are discussed.
Research Interests: Computer Science, Biology, Complex Networks, Medicine, Multidisciplinary, and 15 moreEmergence, Network Biology, Report, Complex network, Cellular Network, Modularity, Biological evolution, Evolvability, Biological Network, Biological systems, Cell communication, Complex Adaptation, Pleiotropy and Its Evolution, Genetic Interaction, and Modular Design
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We present an analysis of the topological structure and static tolerance to errors and attacks of the September 2003 actualization of the Union for the Coordination of Transport of Electricity (UCTE) power grid, involving thirty-three... more
We present an analysis of the topological structure and static tolerance to errors and attacks of the September 2003 actualization of the Union for the Coordination of Transport of Electricity (UCTE) power grid, involving thirty-three different networks. Though every power grid studied has exponential degree distribution and most of them lack typical small-world topology, they display patterns of reaction to node loss similar to those observed in scale-free networks. We have found that the node removal behavior can be logarithmically related to the power grid size. This logarithmic behavior would suggest that, though size favors fragility, growth can reduce it. We conclude that, with the ever-growing demand for power and reliability, actual planning strategies to increase transmission systems would have to take into account this relative increase in vulnerability with size, in order to facilitate and improve the power grid design and functioning.