ASU Scholarship Showcase

Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network “mobile” can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed.

Our ability to uncover complex network structure and dynamics from data is fundamental to understanding and controlling collective dynamics in complex systems. Despite recent progress in this area, reconstructing networks with stochastic dynamical processes from limited time series remains to be an outstanding problem. Here we develop a framework based on compressed sensing to reconstruct complex networks on which stochastic spreading dynamics take place. We apply the methodology to a large number of model and real networks, finding that a full reconstruction of inhomogeneous interactions can be achieved from small amounts of polarized (binary) data, a virtue of compressed sensing. Further, we demonstrate that a hidden source that triggers the spreading process but is externally inaccessible can be ascertained and located with high confidence in the absence of direct routes of propagation from it. Our approach thus establishes a paradigm for tracing and controlling epidemic invasion and information diffusion in complex networked systems.

Antibiotic-resistant bacterial infections are difficult to treat, producing a burden on healthcare and the economy. Extraintestinal pathogenic Escherichia coli (ExPEC) strains frequently carry antibiotic resistance genes, cause infections outside of the intestine, and are causative agents of hospital-acquired infections. Developing a prevention strategy against this pathogen is challenging due to its antibiotic resistance and antigenic diversity. E. coli common pilus (ECP) is frequently found in ExPEC strains and may serve as a common antigen to induce protection against several ExPEC serotypes. In addition, live recombinant attenuated Salmonella vaccine (RASV) strains have been used to prevent Salmonella infection and can also be modified to deliver foreign antigens. Thus, the objective of this study was to design a RASV to produce ECP on its surface and assess its ability to provide protection against ExPEC infections. To constitutively display ECP in a RASV strain, we genetically engineered a vector (pYA4428) containing aspartate-β-semialdehyde dehydrogenase and E. coli ecp genes and introduced it into RASV χ9558. RASV χ9558 containing an empty vector (pYA3337) was used as a control to assess protection conferred by the RASV strain without ECP.

We assessed vaccine efficacy in in vitro bacterial inhibition assays and mouse models of ExPEC-associated human infections. We found that RASV χ9558(pYA4428) synthesized the major pilin (EcpA) and tip pilus adhesin (EcpD) on the bacterial surface. Mice orally vaccinated with RASV χ9558(pYA3337) without ECP or χ9558(pYA4428) with ECP, produced anti-Salmonella LPS and anti-E. coli EcpA and EcpD IgG and IgA antibodies. RASV strains showed protective potential against some E. coli and Salmonella strains as assessed using in vitro assays. In mouse sepsis and urinary tract infection challenge models, both vaccines had significant protection in some internal organs. Overall, this work showed that RASVs can elicit an immune response to E. coli and Salmonella antigens in some mice, provide significant protection in some internal organs during ExPEC challenge, and thus this study is a promising initial step toward developing a vaccine for prevention of ExPEC infections. Future studies should optimize the ExPEC antigens displayed by the RASV strain for a more robust immune response and enhanced protection against ExPEC infection.

Evolutionary games model a common type of interactions in a variety of complex, networked, natural systems and social systems. Given such a system, uncovering the interacting structure of the underlying network is key to understanding its collective dynamics. Based on compressive sensing, we develop an efficient approach to reconstructing complex networks under game-based interactions from small amounts of data. The method is validated by using a variety of model networks and by conducting an actual experiment to reconstruct a social network. While most existing methods in this area assume oscillator networks that generate continuous-time data, our work successfully demonstrates that the extremely challenging problem of reverse engineering of complex networks can also be addressed even when the underlying dynamical processes are governed by realistic, evolutionary-game type of interactions in discrete time.

Salmonella enterica serovar Typhimurium genome encodes 13 fimbrial operons. Most of the fimbriae encoded by these operons are not produced under laboratory conditions but are likely to be synthesized in vivo. We used an in vivo expression technology (IVET) strategy to identify four fimbrial operons, agf, saf, sti, and stc that are expressed in the spleen. When any three of these operons were deleted, the strain retained wild-type virulence. However, when all four operons were deleted, the resulting strain was completely attenuated, indicating that these four fimbriae play functionally redundant roles critical for virulence. In mice, oral doses of as low as 1 × 10[superscript 5] CFU of the strain with four fimbrial operons deleted provided 100% protection against challenge with 1 × 10[superscript 9] CFU of wild-type S. Typhimurium. We also examined the possible effect of these fimbriae on the ability of a Salmonella vaccine strain to deliver a guest antigen. We modified one of our established attenuated vaccine strains, χ9088, to delete three fimbrial operons while the fourth operon was constitutively expressed. Each derivative was modified to express the Streptococcus pneumoniae antigen PspA. Strains that constitutively expressed saf or stc elicited a strong Th1 response with significantly greater levels of anti-PspA serum IgG and greater protective efficacy than strains carrying saf or stc deletions. The isogenic strain in which all four operons were deleted generated the lowest anti-PspA levels and did not protect against challenge with virulent S. pneumoniae. Our results indicate that these fimbriae play important roles, as yet not understood, in Salmonella virulence and immunogenicity.

Extraintestinal pathogenic Escherichia coli (ExPEC) strains are important pathogens that cause diverse diseases in humans and poultry. Some E. coli isolates from chicken feces contain ExPEC-associated virulence genes, so appear potentially pathogenic; they conceivably could be transmitted to humans through handling and/or consumption of contaminated meat. However, the actual extraintestinal virulence potential of chicken-source fecal E. coli is poorly understood. Here, we assessed whether fecal E. coli isolates from healthy production chickens could cause diseases in a chicken model of avian colibacillosis and three rodent models of ExPEC-associated human infections. From 304 E. coli isolates from chicken fecal samples, 175 E. coli isolates were screened by PCR for virulence genes associated with human-source ExPEC or avian pathogenic E. coli (APEC), an ExPEC subset that causes extraintestinal infections in poultry. Selected isolates genetically identified as ExPEC and non-ExPEC isolates were assessed in vitro for virulence-associated phenotypes, and in vivo for disease-causing ability in animal models of colibacillosis, sepsis, meningitis, and urinary tract infection. Among the study isolates, 13% (40/304) were identified as ExPEC; the majority of these were classified as APEC and uropathogenic E. coli, but none as neonatal meningitis E. coli. Multiple chicken-source fecal ExPEC isolates resembled avian and human clinical ExPEC isolates in causing one or more ExPEC-associated illnesses in experimental animal infection models. Additionally, some isolates that were classified as non-ExPEC were able to cause ExPEC-associated illnesses in animal models, and thus future studies are needed to elucidate their mechanisms of virulence. These findings show that E. coli isolates from chicken feces contain ExPEC-associated genes, exhibit ExPEC-associated in vitro phenotypes, and can cause ExPEC-associated infections in animal models, and thus may pose a health threat to poultry and consumers.

Locating sources of diffusion and spreading from minimum data is a significant problem in network science with great applied values to the society. However, a general theoretical framework dealing with optimal source localization is lacking. Combining the controllability theory for complex networks and compressive sensing, we develop a framework with high efficiency and robustness for optimal source localization in arbitrary weighted networks with arbitrary distribution of sources. We offer a minimum output analysis to quantify the source locatability through a minimal number of messenger nodes that produce sufficient measurement for fully locating the sources. When the minimum messenger nodes are discerned, the problem of optimal source localization becomes one of sparse signal reconstruction, which can be solved using compressive sensing. Application of our framework to model and empirical networks demonstrates that sources in homogeneous and denser networks are more readily to be located. A surprising finding is that, for a connected undirected network with random link weights and weak noise, a single messenger node is sufficient for locating any number of sources. The framework deepens our understanding of the network source localization problem and offers efficient tools with broad applications.

Evolutionary games of cyclic competitions have been extensively studied to gain insights into one of the most fundamental phenomena in nature: biodiversity that seems to be excluded by the principle of natural selection. The Rock-Paper-Scissors (RPS) game of three species and its extensions [e.g., the Rock-Paper-Scissors-Lizard-Spock (RPSLS) game] are paradigmatic models in this field. In all previous studies, the intrinsic symmetry associated with cyclic competitions imposes a limitation on the resulting coexistence states, leading to only selective types of such states. We investigate the effect of nonuniform intraspecific competitions on coexistence and find that a wider spectrum of coexistence states can emerge and persist. This surprising finding is substantiated using three classes of cyclic game models through stability analysis, Monte Carlo simulations and continuous spatiotemporal dynamical evolution from partial differential equations. Our finding indicates that intraspecific competitions or alternative symmetry-breaking mechanisms can promote biodiversity to a broader extent than previously thought.

Dynamical processes occurring on the edges in complex networks are relevant to a variety of real-world situations. Despite recent advances, a framework for edge controllability is still required for complex networks of arbitrary structure and interaction strength. Generalizing a previously introduced class of processes for edge dynamics, the switchboard dynamics, and exploit- ing the exact controllability theory, we develop a universal framework in which the controllability of any node is exclusively determined by its local weighted structure. This framework enables us to identify a unique set of critical nodes for control, to derive analytic formulas and articulate efficient algorithms to determine the exact upper and lower controllability bounds, and to evaluate strongly structural controllability of any given network. Applying our framework to a large number of model and real-world networks, we find that the interaction strength plays a more significant role in edge controllability than the network structure does, due to a vast range between the bounds determined mainly by the interaction strength. Moreover, transcriptional regulatory networks and electronic circuits are much more strongly structurally controllable (SSC) than other types of real-world networks, directed networks are more SSC than undirected networks, and sparse networks are typically more SSC than dense networks.

Understanding the dynamics of human movements is key to issues of significant current interest such as behavioral prediction, recommendation, and control of epidemic spreading. We collect and analyze big data sets of human movements in both cyberspace (through browsing of websites) and physical space (through mobile towers) and find a superlinear scaling relation between the mean frequency of visit〈f〉and its fluctuation σ : σ ∼〈f⟩^β with β ≈ 1.2. The probability distribution of the visiting frequency is found to be a stretched exponential function. We develop a model incorporating two essential ingredients, preferential return and exploration, and show that these are necessary for generating the scaling relation extracted from real data. A striking finding is that human movements in cyberspace and physical space are strongly correlated, indicating a distinctive behavioral identifying characteristic and implying that the behaviors in one space can be used to predict those in the other.