ASU Scholarship Showcase

Two classes of scaling behaviours, namely the super-linear scaling of links or activities, and the sub-linear scaling of area, diversity, or time elapsed with respect to size have been found to prevail in the growth of complex networked systems. Despite some pioneering modelling approaches proposed for specific systems, whether there exists some general mechanisms that account for the origins of such scaling behaviours in different contexts, especially in socioeconomic systems, remains an open question. We address this problem by introducing a geometric network model without free parameter, finding that both super-linear and sub-linear scaling behaviours can be simultaneously reproduced and that the scaling exponents are exclusively determined by the dimension of the Euclidean space in which the network is embedded. We implement some realistic extensions to the basic model to offer more accurate predictions for cities of various scaling behaviours and the Zipf distribution reported in the literature and observed in our empirical studies. All of the empirical results can be precisely recovered by our model with analytical predictions of all major properties. By virtue of these general findings concerning scaling behaviour, our models with simple mechanisms gain new insights into the evolution and development of complex networked systems.

The threat of West Nile virus (WNV) epidemics with increasingly severe neuroinvasive infections demands the development and licensing of effective vaccines. To date, vaccine candidates based on inactivated, live-attenuated, or chimeric virus, and viral DNA and WNV protein subunits have been developed. Some have been approved for veterinary use or are under clinical investigation, yet no vaccine has been licensed for human use. Reaching the milestone of a commercialized human vaccine, however, may largely depend on the economics of vaccine production. Analysis suggests that currently only novel low-cost production technologies would allow vaccination to outcompete the cost of surveillance and clinical treatment. Here, we review progress using plants to address the economic challenges of WNV vaccine production. The advantages of plants as hosts for vaccine production in cost, speed and scalability, especially those of viral vector-based transient expression systems, are discussed. The progress in developing WNV subunit vaccines in plants is reviewed within the context of their expression, characterization, downstream processing, and immunogenicity in animal models. The development of vaccines based on enveloped and non-enveloped virus-like particles is also discussed. These advancements suggest that plants may provide a production platform that offers potent, safe and affordable human vaccines against WNV.

Previously, our group engineered a plant-derived monoclonal antibody (MAb) (pHu-E16) that efficiently treated West Nile virus (WNV) infection in mice. In this study, we developed several pHu-E16 variants to improve its efficacy. These variants included a single-chain variable fragment (scFv) of pHu-E16 fused to the heavy chain (HC) constant domains (CH1-3) of human IgG (pHu-E16scFv-CH1-3) and a tetravalent molecule (Tetra pHu-E16) assembled from pHu-E16scFv-CH1-3 with a second pHu-E16scFv fused to the light chain (LC) constant region. pHu-E16scFv-CH1-3 and Tetra pHu-E16 were efficiently expressed and assembled in plants. To assess the impact of differences in N-linked glycosylation on pHu-E16 variant assembly and function, we expressed additional pHu-E16 variants with various combinations of HC and LC components.

Our study revealed that proper pairing of HC and LC was essential for the complete N-glycan processing of antibodies in both plant and animal cells. Associated with their distinct N-glycoforms, pHu-E16, pHu-E16scFv-CH1-3 and Tetra pHu-E16 exhibited differential binding to C1q and specific Fcγ receptors (FcγR). Notably, none of the plant-derived Hu-E16 variants showed antibody-dependent enhancement (ADE) activity in CD32A+ human cells, suggesting the potential of plant-produced antibodies to minimize the adverse effect of ADE. Importantly, all plant-derived MAb variants exhibited at least equivalent in vitro neutralization and in vivo protection in mice compared to mammalian cell-produced Hu-E16. This study demonstrates the capacity of plants to express and assemble a large, complex and functional IgG-like tetravalent mAb variant and also provides insight into the relationship between MAb N-glycosylation, FcγR and C1q binding, and ADE. These new insights may allow the development of safer and cost effective MAb-based therapeutics for flaviviruses, and possibly other pathogens.

Strategies are needed to improve repopulation of decellularized lung scaffolds with stromal and functional epithelial cells. We demonstrate that decellularized mouse lungs recellularized in a dynamic low fluid shear suspension bioreactor, termed the rotating wall vessel (RWV), contained more cells with decreased apoptosis, increased proliferation and enhanced levels of total RNA compared to static recellularization conditions. These results were observed with two relevant mouse cell types: bone marrow-derived mesenchymal stromal (stem) cells (MSCs) and alveolar type II cells (C10). In addition, MSCs cultured in decellularized lungs under static but not bioreactor conditions formed multilayered aggregates. Gene expression and immunohistochemical analyses suggested differentiation of MSCs into collagen I-producing fibroblast-like cells in the bioreactor, indicating enhanced potential for remodeling of the decellularized scaffold matrix. In conclusion, dynamic suspension culture is promising for enhancing repopulation of decellularized lungs, and could contribute to remodeling the extracellular matrix of the scaffolds with subsequent effects on differentiation and functionality of inoculated cells.

Investigation into the causes underlying the rapid, global amphibian decline provides critical insight into the effects of changing ecosystems. Hypothesized and confirmed links between amphibian declines, disease, and environmental changes are increasingly represented in published literature. However, there are few long-term amphibian studies that include data on population size, abnormality/injury rates, disease, and habitat variables to adequately assess changes through time. We cultured and identified microorganisms isolated from abnormal/injured and repressed tissue regeneration sites of the endangered Ozark Hellbender, Cryptobranchus alleganiensis bishopi, to discover potential causative agents responsible for their significant decline in health and population. This organism and our study site were chosen because the population and habitat of C. a. bishopi have been intensively studied from 1969–2009, and the abnormality/injury rate and apparent lack of regeneration were established.

Although many bacterial and fungal isolates recovered were common environmental organisms, several opportunistic pathogens were identified in association with only the injured tissues of C.a. bishopi. Bacterial isolates included Aeromonas hydrophila, a known amphibian pathogen, Granulicetella adiacens, Gordonai terrae, Stenotrophomonas maltophilia, Aerococcus viridans, Streptococcus pneumoniae and a variety of Pseudomonads, including Pseudomonas aeruginosa, P. stutzeri, and P. alcaligenes. Fungal isolates included species in the genera Penicillium, Acremonium, Cladosporium, Curvularia, Fusarium, Streptomycetes, and the Class Hyphomycetes. Many of the opportunistic pathogens identified are known to form biofilms. Lack of isolation of the same organism from all wounds suggests that the etiological agent responsible for the damage to C. a. bishopi may not be a single organism. To our knowledge, this is the first study to profile the external microbial consortia cultured from a Cryptobranchid salamander. The incidence of abnormalities/injury and retarded regeneration in C. a. bishopi may have many contributing factors including disease and habitat degradation. Results from this study may provide insight into other amphibian population declines.

A distinct pathovar of Salmonella enterica serovar Typhimurium, ST313, has emerged in sub-Saharan Africa as a major cause of fatal bacteremia in young children and HIV-infected adults. D23580, a multidrug resistant clinical isolate of ST313, was previously shown to have undergone genome reduction in a manner that resembles that of the more human-restricted pathogen, Salmonella enterica serovar Typhi. It has since been shown through tissue distribution studies that D23580 is able to establish an invasive infection in chickens. However, it remains unclear whether ST313 can cause lethal disease in a non-human host following a natural course of infection. Herein we report that D23580 causes lethal and invasive disease in a murine model of infection following peroral challenge. The LD⁵⁰ of D23580 in female BALB/c mice was 4.7 x 10⁵ CFU. Tissue distribution studies performed 3 and 5 days post-infection confirmed that D23580 was able to more rapidly colonize the spleen, mesenteric lymph nodes and gall bladder in mice when compared to the well-characterized S. Typhimurium strain SL1344. D23580 exhibited enhanced resistance to acid stress relative to SL1344, which may lend towards increased capability to survive passage through the gastrointestinal tract as well as during its intracellular lifecycle. Interestingly, D23580 also displayed higher swimming motility relative to SL1344, S. Typhi strain Ty2, and the ST313 strain A130. Biochemical tests revealed that D23580 shares many similar metabolic features with SL1344, with several notable differences in the Voges-Proskauer and catalase tests, as well alterations in melibiose, and inositol utilization. These results represent the first full duration infection study using an ST313 strain following the entire natural course of disease progression, and serve as a benchmark for ongoing and future studies into the pathogenesis of D23580.

Given a complex geospatial network with nodes distributed in a two-dimensional region of physical space, can the locations of the nodes be determined and their connection patterns be uncovered based solely on data? We consider the realistic situation where time series/signals can be collected from a single location. A key challenge is that the signals collected are necessarily time delayed, due to the varying physical distances from the nodes to the data collection centre. To meet this challenge, we develop a compressive-sensing-based approach enabling reconstruction of the full topology of the underlying geospatial network and more importantly, accurate estimate of the time delays. A standard triangularization algorithm can then be employed to find the physical locations of the nodes in the network. We further demonstrate successful detection of a hidden node (or a hidden source or threat), from which no signal can be obtained, through accurate detection of all its neighbouring nodes. As a geospatial network has the feature that a node tends to connect with geophysically nearby nodes, the localized region that contains the hidden node can be identified.

Recent works revealed that the energy required to control a complex network depends on the number of driving signals and the energy distribution follows an algebraic scaling law. If one implements control using a small number of drivers, e.g. as determined by the structural controllability theory, there is a high probability that the energy will diverge. We develop a physical theory to explain the scaling behaviour through identification of the fundamental structural elements, the longest control chains (LCCs), that dominate the control energy. Based on the LCCs, we articulate a strategy to drastically reduce the control energy (e.g. in a large number of real-world networks). Owing to their structural nature, the LCCs may shed light on energy issues associated with control of nonlinear dynamical networks.

A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.

Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.