Global topological control for synchronized dynamics on networks

A general scheme is proposed and tested to control the symmetry breaking instability of a homogeneous solution of a spatially extended multispecies model, defined on a network. The inherent discreteness of the space makes it possible to act on the topology of the inter-nodes contacts to achieve the desired degree of stabilization, without altering the dynamical parameters of the model. Both symmetric and asymmetric couplings are considered. In this latter setting the web of contacts is assumed to be balanced, for the homogeneous equilibrium to exist. The performance of the proposed method are assessed, assuming the Complex Ginzburg-Landau equation as a reference model. In this case, the implemented control allows one to stabilize the synchronous limit cycle, hence time-dependent, uniform solution. A system of coupled real Ginzburg-Landau equations is also investigated to obtain the topological stabilization of a homogeneous and constant fixed point

Network structure of multivariate time series.

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail

Complex Quantum Networks: a Topical Review

These are exciting times for quantum physics as new quantum technologies are expected to soon transform computing at an unprecedented level. Simultaneously network science is flourishing proving an ideal mathematical and computational framework to capture the complexity of large interacting systems. Here we provide a comprehensive and timely review of the rising field of complex quantum networks. On one side, this subject is key to harness the potential of complex networks in order to provide design principles to boost and enhance quantum algorithms and quantum technologies. On the other side this subject can provide a new generation of quantum algorithms to infer significant complex network properties. The field features fundamental research questions as diverse as designing networks to shape Hamiltonians and their corresponding phase diagram, taming the complexity of many-body quantum systems with network theory, revealing how quantum physics and quantum algorithms can predict novel network properties and phase transitions, and studying the interplay between architecture, topology and performance in quantum communication networks. Our review covers all of these multifaceted aspects in a self-contained presentation aimed both at network-curious quantum physicists and at quantum-curious network theorists. We provide a framework that unifies the field of quantum complex networks along four main research lines: network-generalized, quantum-applied, quantum-generalized and quantum-enhanced. Finally we draw attention to the connections between these research lines, which can lead to new opportunities and new discoveries at the interface between quantum physics and network science.Comment: 103 pages + 29 pages of references, 26 figure

Multilayer Networks

In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such "multilayer" features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks, and many others. We also survey and discuss existing data sets that can be represented as multilayer networks. We review attempts to generalize single-layer-network diagnostics to multilayer networks. We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions, and various types of dynamical processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure

Behavioral and Network Origins of Wealth Inequality: Insights from a Virtual World

Almost universally, wealth is not distributed uniformly within societies or economies. Even though wealth data have been collected in various forms for centuries, the origins for the observed wealth-disparity and social inequality are not yet fully understood. Especially the impact and connections of human behavior on wealth could so far not be inferred from data. Here we study wealth data from the virtual economy of the massive multiplayer online game (MMOG) Pardus. This data not only contains every player's wealth at every point in time, but also all actions of every player over a timespan of almost a decade. We find that wealth distributions in the virtual world are very similar to those in western countries. In particular we find an approximate exponential for low wealth and a power-law tail. The Gini index is found to be $g=0.65$, which is close to the indices of many Western countries. We find that wealth-increase rates depend on the time when players entered the game. Players that entered the game early on tend to have remarkably higher wealth-increase rates than those who joined later. Studying the players' positions within their social networks, we find that the local position in the trade network is most relevant for wealth. Wealthy people have high in- and out-degree in the trade network, relatively low nearest-neighbor degree and a low clustering coefficient. Wealthy players have many mutual friendships and are socially well respected by others, but spend more time on business than on socializing. We find that players that are not organized within social groups with at least three members are significantly poorer on average. We observe that high `political' status and high wealth go hand in hand. Wealthy players have few personal enemies, but show animosity towards players that behave as public enemies.Comment: 22 pages, 8 figures, 8 pages S

Xeno-free 3D Culture of Mesenchymal Stromal Cells For Bone Tissue Engineering

Clinical translation of innovative regenerative approaches using mesenchymal stromal cells (MSCs) is urgently needed for the treatment of challenging bone defects. The overall aim of this thesis, comprising of one systematic review and four original studies, was to optimize a xeno-free three-dimensional (3D) culture system of MSCs, as a clinically relevant strategy for bone tissue engineering (BTE). Secondary aims were to identify a minimally invasive source for MSCs, and to promote angiogenesis within the xeno-free 3D cultures. Human platelet lysate (HPL) represents a favourable supplement for xeno-free expansion of MSCs (Study I). To standardize HPL production, the storage time of platelet concentrates was optimized in terms of HPL cytokine content and biological efficacy on MSCs. Advantages of HPL culture (vs. bovine serum) were observed in relation to all relevant in vitro aspects of MSCs, i.e., growth, immunophenotype and osteogenic differentiation (Studies II and III). Progenitor cells showing a characteristic MSC-like phenotype and multipotency were isolated from human gingiva (GPCs) and periodontal ligament (PDLCs). Both GPCs and PDLCs demonstrated superior growth and osteogenic differentiation in HPL vs. FBS; a subset of GPCs also showed potent neurogenic differentiation (Study III). Given their relative ease of isolation and minimally invasive tissue harvesting, GPCs were prioritized in subsequent experiments. To overcome the limitations of traditional monolayer (2D) culture, 3D spheroid cultures were established in HPL. Both GPCs and BMSCs demonstrated significantly increased expression of stemness- and osteogenesis-related genes in spheroids vs. monolayers, confirmed at the protein level by immunocytochemistry. Moreover, the cytokine release profile of GPC and BMSC spheroids was considerably enhanced compared to monolayers. Under osteogenic conditions, GPC spheroids showed in vitro mineralization comparable to that of BMSCs (Study III). When implanted in vivo, xeno-free GPCs and BMSCs showed ectopic mineralization after 4 and 8 weeks based on micro-CT and histology; implanted human cells were identified at the mineralization sites via in situ hybridization. In the case of BMSCs, significantly greater mineralization was observed in constructs containing spheroids vs. single cells (Study V). To enhance angiogenesis, a coculture strategy was tested using a xeno-free spheroid coculture model of GPCs and human umbilical vein ECs (HUVECs) embedded in an HPL-hydrogel (HPLG). When cultured as spheroids, HUVECs showed characteristic in vitro sprouting angiogenesis in HPLG. A trend for increased in vitro HUVEC-sprouting was observed in co-culture with GPCs. Constructs of coculture and HUVEC spheroids in HPLG comparably supported in vivo neoangiogenesis in a chorioallantoic membrane (CAM) assay (Study IV). Clinically relevant BTE constructs were designed combining BMSCs (as spheroids or single cells) encapsulated in HPLG and 3D printed copolymer scaffolds. Viability and osteogenic differentiation of cells within the constructs was confirmed up to 21 days in vitro; greater mineralization was observed in constructs containing spheroids vs. single cells. When implanted in rats’ calvarial defects, constructs of both spheroids and single cells revealed abundant in vivo bone regeneration for up to 12 weeks (Study V). The results herein suggest clear advantages of xeno-free 3D cultures of MSCs for BTE. GPCs represent a promising alternative to BMSCs with osteogenic and proangiogenic potential, and further work is needed to facilitate clinical translation. In particular, the constructs of xeno-free MSCs, HPLG and 3D printed scaffolds developed herein, represent a clinically relevant strategy for BTE

The future of metabolic engineering and synthetic biology: Towards a systematic practice

Industrial biotechnology promises to revolutionize conventional chemical manufacturing in the years ahead, largely owing to the excellent progress in our ability to re-engineer cellular metabolism. However, most successes of metabolic engineering have been confined to over-producing natively synthesized metabolites in E. coli and S. cerevisiae. A major reason for this development has been the descent of metabolic engineering, particularly secondary metabolic engineering, to a collection of demonstrations rather than a systematic practice with generalizable tools. Synthetic biology, a more recent development, faces similar criticisms. Herein, we attempt to lay down a framework around which bioreaction engineering can systematize itself just like chemical reaction engineering. Central to this undertaking is a new approach to engineering secondary metabolism known as ‘multivariate modular metabolic engineering’ (MMME), whose novelty lies in its assessment and elimination of regulatory and pathway bottlenecks by re-defining the metabolic network as a collection of distinct modules. After introducing the core principles of MMME, we shall then present a number of recent developments in secondary metabolic engineering that could potentially serve as its facilitators. It is hoped that the ever-declining costs of de novo gene synthesis; the improved use of bioinformatic tools to mine, sort and analyze biological data; and the increasing sensitivity and sophistication of investigational tools will make the maturation of microbial metabolic engineering an autocatalytic process. Encouraged by these advances, research groups across the world would take up the challenge of secondary metabolite production in simple hosts with renewed vigor, thereby adding to the range of products synthesized using metabolic engineering.National Institutes of Health (U.S.) (1-R01-GM085323-01A1)Special Research Funds BOF (BOF08/PDO/014)Research Foundation Flanders (FWO-Vlaandern V.4.174.10.N.01