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.

We develop a completely data-driven approach to reconstructing coupled neuronal networks that contain a small subset of chaotic neurons. Such chaotic elements can be the result of parameter shift in their individual dynamical systems and may lead to abnormal functions of the network. To accurately identify the chaotic neurons may thus be necessary and important, for example, applying appropriate controls to bring the network to a normal state. However, due to couplings among the nodes, the measured time series, even from non-chaotic neurons, would appear random, rendering inapplicable traditional nonlinear time-series analysis, such as the delay-coordinate embedding method, which yields information about the global dynamics of the entire network. Our method is based on compressive sensing. In particular, we demonstrate that identifying chaotic elements can be formulated as a general problem of reconstructing the nodal dynamical systems, network connections and all coupling functions, as well as their weights. The working and efficiency of the method are illustrated by using networks of non-identical FitzHugh–Nagumo neurons with randomly-distributed coupling weights.

We report the results of an ethnographic study of a natural food cooperative in which we found an inherent tension in its mission between idealism and pragmatism, and we explore the dynamics through which that tension was managed and engaged in day-to-day governance and activities. Insights from participant observation, archival data, semi-structured interviews, and surveys provide a detailed and holistic account of the intergroup and intragroup processes through which the co-op negotiated its dualistic nature, as embodied in its hybrid organizational identity. The findings suggest that the value of each side of the duality was recognized at both the individual and organizational levels. Members’ discomfort with the duality, however, led them to split the mission in two and identify with one part, while projecting their less-favored part on others, creating an identity foil (an antithesis). This splitting resulted in ingroups and outgroups and heated intergroup conflict over realizing cooperative ideals vs. running a viable business. Ingroup members favoring one part of the mission nonetheless identified with the outgroup favoring the other because it embodied a side of themselves they continued to value. Individuals who exemplified their ingroup’s most extreme attributes were seen by the outgroup as prototypical, thus serving as “lightning rods” for intergroup conflict; this dynamic paradoxically enabled other ingroup members to work more effectively with moderate members of the outgroup. The idealist–pragmatist duality was kept continually in play over time through oscillating decisions and actions that shifted power from one group to the other, coupled with ongoing rituals to repair and maintain relationships disrupted by the messiness of the process. Thus ostensible dysfunctionality at the group level fostered functionality at the organizational level.

The relation between flux and fluctuation is fundamental to complex physical systems that support and transport flows. A recently obtained law predicts monotonous enhancement of fluctuation as the average flux is increased, which in principle is valid but only for large systems. For realistic complex systems of small sizes, this law breaks down when both the average flux and fluctuation become large. Here we demonstrate the failure of this law in small systems using real data and model complex networked systems, derive analytically a modified flux-fluctuation law, and validate it through computations of a large number of complex networked systems. Our law is more general in that its predictions agree with numerics and it reduces naturally to the previous law in the limit of large system size, leading to new insights into the flow dynamics in small-size complex systems with significant implications for the statistical and scaling behaviors of small systems, a topic of great recent interest.

There are many proteomic applications that require large collections of purified protein, but parallel production of large numbers of different proteins remains a very challenging task. To help meet the needs of the scientific community, we have developed a human protein production pipeline. Using high-throughput (HT) methods, we transferred the genes of 31 full-length proteins into three expression vectors, and expressed the collection as N-terminal HaloTag fusion proteins in Escherichia coli and two commercial cell-free (CF) systems, wheat germ extract (WGE) and HeLa cell extract (HCE). Expression was assessed by labeling the fusion proteins specifically and covalently with a fluorescent HaloTag ligand and detecting its fluorescence on a LabChip[superscript ®] GX microfluidic capillary gel electrophoresis instrument. This automated, HT assay provided both qualitative and quantitative assessment of recombinant protein. E. coli was only capable of expressing 20% of the test collection in the supernatant fraction with ≥20 μg yields, whereas CF systems had ≥83% success rates. We purified expressed proteins using an automated HaloTag purification method. We purified 20, 33, and 42% of the test collection from E. coli, WGE, and HCE, respectively, with yields ≥1 μg and ≥90% purity. Based on these observations, we have developed a triage strategy for producing full-length human proteins in these three expression systems.

We develop a general framework to analyze the controllability of multiplex networks using multiple-relation networks and multiple-layer networks with interlayer couplings as two classes of prototypical systems. In the former, networks associated with different physical variables share the same set of nodes and in the latter, diffusion processes take place. We find that, for a multiple-relation network, a layer exists that dominantly determines the controllability of the whole network and, for a multiple-layer network, a small fraction of the interconnections can enhance the controllability remarkably. Our theory is generally applicable to other types of multiplex networks as well, leading to significant insights into the control of complex network systems with diverse structures and interacting patterns.

Mobile applications markets with app stores have introduced a new approach to define and sell software applications with access to a large body of heterogeneous consumer population. This research examines key seller- and app-level characteristics that impact success in an app store market. We tracked individual apps and their presence in the top-grossing 300 chart in Apple's App Store and examined how factors at different levels affect the apps' survival in the top 300 chart. We used a generalized hierarchical modeling approach to measure sales performance, and confirmed the results with the use of a hazard model and a count regression model. We find that broadening app offerings across multiple categories is a key determinant that contributes to a higher probability of survival in the top charts. App-level attributes such as free app offers, high initial ranks, investment in less-popular (less-competitive) categories, continuous quality updates, and high-volume and high-user review scores have positive effects on apps' sustainability. In general, each diversification decision across a category results in an approximately 15 percent increase in the presence of an app in the top charts. Survival rates for free apps are up to two times more than that for paid apps. Quality (feature) updates to apps can contribute up to a threefold improvement in survival rate as well. A key implication of the results of this study is that sellers must utilize the natural segmentation in consumer tastes offered by the different categories to improve sales performance.

Throughout the long history of virus-host co-evolution, viruses have developed delicate strategies to facilitate their invasion and replication of their genome, while silencing the host immune responses through various mechanisms. The systematic characterization of viral protein-host interactions would yield invaluable information in the understanding of viral invasion/evasion, diagnosis and therapeutic treatment of a viral infection, and mechanisms of host biology. With more than 2,000 viral genomes sequenced, only a small percent of them are well investigated. The access of these viral open reading frames (ORFs) in a flexible cloning format would greatly facilitate both in vitro and in vivo virus-host interaction studies. However, the overall progress of viral ORF cloning has been slow. To facilitate viral studies, we are releasing the initiation of our panviral proteome collection of 2,035 ORF clones from 830 viral genes in the Gateway® recombinational cloning system. Here, we demonstrate several uses of our viral collection including highly efficient production of viral proteins using human cell-free expression system in vitro, global identification of host targets for rubella virus using Nucleic Acid Programmable Protein Arrays (NAPPA) containing 10,000 unique human proteins, and detection of host serological responses using micro-fluidic multiplexed immunoassays. The studies presented here begin to elucidate host-viral protein interactions with our systemic utilization of viral ORFs, high-throughput cloning, and proteomic technologies. These valuable plasmid resources will be available to the research community to enable continued viral functional studies.

Evolutionary dynamical models for cyclic competitions of three species (e.g., rock, paper, and scissors, or RPS) provide a paradigm, at the microscopic level of individual interactions, to address many issues in coexistence and biodiversity. Real ecosystems often involve competitions among more than three species. By extending the RPS game model to five (rock-paper-scissors-lizard-Spock, or RPSLS) mobile species, we uncover a fundamental type of mesoscopic interactions among subgroups of species. In particular, competitions at the microscopic level lead to the emergence of various local groups in different regions of the space, each involving three species. It is the interactions among the groups that fundamentally determine how many species can coexist. In fact, as the mobility is increased from zero, two transitions can occur: one from a five- to a three-species coexistence state and another from the latter to a uniform, single-species state. We develop a mean-field theory to show that, in order to understand the first transition, group interactions at the mesoscopic scale must be taken into account. Our findings suggest, more broadly, the importance of mesoscopic interactions in coexistence of great many species.

The current research examines how the price of a medication influences consumers’ beliefs about their own disease risk—a critical question with new laws mandating greater price transparency for health care goods and services. Four studies reveal that consumers believe that lifesaving health goods are priced according to perceived need (i.e., communal-sharing principles) and that price consequently influences risk perceptions and intentions to consume care. Specifically, consumers believe that lower medication prices signal greater accessibility to anyone in need, and such accessibility thus makes them feel that their own self-risk is elevated, increasing consumption. The reverse is true for higher prices. Importantly, these effects are limited to self-relevant health threats and reveal that consumers make inconsistent assumptions about risk, prevalence, and need with price exposure. These findings suggest that while greater price transparency may indeed reduce consumption of higher-priced goods, it may do so for both necessary and unnecessary care.