ASU Scholarship Showcase

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.

Nasonia, a genus of four closely related parasitoid insect species, is a model system for genetic research. Their haplodiploid genetics (haploid males and diploid females) and interfertile species are advantageous for the genetic analysis of complex traits and the genetic basis of species differences. A fine-scale genomic map is an important tool for advancing genetic studies in this system. We developed and used a hybrid genotyping microarray to generate a high-resolution genetic map that covers 79% of the sequenced genome of Nasonia vitripennis. The microarray is based on differential hybridization of species-specific oligos between N. vitripennis and Nasonia giraulti at more than 20,000 markers spanning the Nasonia genome. The map places 729 scaffolds onto the five linkage groups of Nasonia, including locating many smaller scaffolds that would be difficult to map by other means. The microarray was used to characterize 26 segmental introgression lines containing chromosomal regions from one species in the genetic background of another. These segmental introgression lines have been used for rapid screening and mapping of quantitative trait loci involved in species differences. Finally, the microarray is extended to bulk-segregant analysis and genotyping of other Nasonia species combinations. These resources should further expand the usefulness of Nasonia for studies of the genetic basis and architecture of complex traits and speciation.

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.

Variation in behaviour among group members often impacts collective outcomes. Individuals may vary both in the task that they perform and in the persistence with which they perform each task. Although both the distribution of individuals among tasks and differences among individuals in behavioural persistence can each impact collective behaviour, we do not know if and how they jointly affect collective outcomes. Here, we use a detailed computational model to examine the joint impact of colony-level distribution among tasks and behavioural persistence of individuals, specifically their fidelity to particular resource sites, on the collective trade-off between exploring for new resources and exploiting familiar ones. We developed an agent-based model of foraging honeybees, parametrized by data from five colonies, in which we simulated scouts, who search the environment for new resources, and individuals who are recruited by the scouts to the newly found resources, i.e. recruits. We varied the persistence of returning to a particular food source of both scouts and recruits and found that, for each value of persistence, there is a different optimal ratio of scouts to recruits that maximizes resource collection by the colony. Furthermore, changes to the persistence of scouts induced opposite effects from changes to the persistence of recruits on the collective foraging of the colony. The proportion of scouts that resulted in the most resources collected by the colony decreased as the persistence of recruits increased. However, this optimal proportion of scouts increased as the persistence of scouts increased. Thus, behavioural persistence and task participation can interact to impact a colony's collective behaviour in orthogonal directions. Our work provides new insights and generates new hypotheses into how variations in behaviour at both the individual and colony levels jointly impact the trade-off between exploring for new resources and exploiting familiar ones.

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.

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.

The molecular mechanisms that allow generalist parasitoids to exploit many, often very distinct hosts are practically unknown. The wasp Aphidius ervi, a generalist koinobiont parasitoid of aphids, was introduced from Europe into Chile in the late 1970s to control agriculturally important aphid species. A recent study showed significant differences in host preference and host acceptance (infectivity) depending on the host A. ervi were reared on. In contrast, no genetic differentiation between A. ervi populations parasitizing different aphid species and aphids of the same species reared on different host plants was found in Chile. Additionally, the same study did not find any fitness effects in A. ervi if offspring were reared on a different host as their mothers. Here, we determined the effect of aphid host species (Sitobion avenae versus Acyrthosiphon pisum reared on two different host plants alfalfa and pea) on the transcriptome of adult A. ervi females.

We found a large number of differentially expressed genes (between host species: head: 2,765; body: 1,216; within the same aphid host species reared on different host plants: alfalfa versus pea: head 593; body 222). As expected, the transcriptomes from parasitoids reared on the same host species (pea aphid) but originating from different host plants (pea versus alfalfa) were more similar to each other than the transcriptomes of parasitoids reared on a different aphid host and host plant (head: 648 and 1,524 transcripts; body: 566 and 428 transcripts). We found several differentially expressed odorant binding proteins and olfactory receptor proteins in particular, when we compared parasitoids from different host species. Additionally, we found differentially expressed genes involved in neuronal growth and development as well as signaling pathways.

These results point towards a significant rewiring of the transcriptome of A. ervi depending on aphid-plant complex where parasitoids develop, even if different biotypes of a certain aphid host species (A. pisum) are reared on the same host plant. This difference seems to persist even after the different wasp populations were reared on the same aphid host in the laboratory for more than 50 generations. This indicates that either the imprinting process is very persistent or there is enough genetic/allelic variation between A. ervi populations. The role of distinct molecular mechanisms is discussed in terms of the formation of host fidelity.

Adaptation requires genetic variation, but founder populations are generally genetically depleted. Here we sequence two populations of an inbred ant that diverge in phenotype to determine how variability is generated. Cardiocondyla obscurior has the smallest of the sequenced ant genomes and its structure suggests a fundamental role of transposable elements (TEs) in adaptive evolution. Accumulations of TEs (TE islands) comprising 7.18% of the genome evolve faster than other regions with regard to single-nucleotide variants, gene/exon duplications and deletions and gene homology. A non-random distribution of gene families, larvae/adult specific gene expression and signs of differential methylation in TE islands indicate intragenomic differences in regulation, evolutionary rates and coalescent effective population size. Our study reveals a tripartite interplay between TEs, life history and adaptation in an invasive species.

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.

Desaturase genes are essential for biological processes, including lipid metabolism, cell signaling, and membrane fluidity regulation. Insect desaturases are particularly interesting for their role in chemical communication, and potential contribution to speciation, symbioses, and sociality. Here, we describe the acyl-CoA desaturase gene families of 15 insects, with a focus on social Hymenoptera. Phylogenetic reconstruction revealed that the insect desaturases represent an ancient gene family characterized by eight subfamilies that differ strongly in their degree of conservation and frequency of gene gain and loss. Analyses of genomic organization showed that five of these subfamilies are represented in a highly microsyntenic region conserved across holometabolous insect taxa, indicating an ancestral expansion during early insect evolution. In three subfamilies, ants exhibit particularly large expansions of genes. Despite these expansions, however, selection analyses showed that desaturase genes in all insect lineages are predominantly undergoing strong purifying selection. Finally, for three expanded subfamilies, we show that ants exhibit variation in gene expression between species, and more importantly, between sexes and castes within species. This suggests functional differentiation of these genes and a role in the regulation of reproductive division of labor in ants. The dynamic pattern of gene gain and loss of acyl-CoA desaturases in ants may reflect changes in response to ecological diversification and an increased demand for chemical signal variability. This may provide an example of how gene family expansions can contribute to lineage-specific adaptations through structural and regulatory changes acting in concert to produce new adaptive phenotypes.