The use of multilayer network analysis in animal behaviour

Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as \emph{multilayer network analysis}, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour as connected 'layers' of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population, and evolutionary levels of organisation. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.Comment: Thoroughly revised; title changed slightl

The influence of topology and information diffusion on networked game dynamics

This thesis studies the influence of topology and information diffusion on the strategic interactions of agents in a population. It shows that there exists a reciprocal relationship between the topology, information diffusion and the strategic interactions of a population of players. In order to evaluate the influence of topology and information flow on networked game dynamics, strategic games are simulated on populations of players where the players are distributed in a non-homogeneous spatial arrangement. The initial component of this research consists of a study of evolution of the coordination of strategic players, where the topology or the structure of the population is shown to be critical in defining the coordination among the players. Next, the effect of network topology on the evolutionary stability of strategies is studied in detail. Based on the results obtained, it is shown that network topology plays a key role in determining the evolutionary stability of a particular strategy in a population of players. Then, the effect of network topology on the optimum placement of strategies is studied. Using genetic optimisation, it is shown that the placement of strategies in a spatially distributed population of players is crucial in maximising the collective payoff of the population. Exploring further the effect of network topology and information diffusion on networked games, the non-optimal or bounded rationality of players is modelled using topological and directed information flow of the network. Based on the topologically distributed bounded rationality model, it is shown that the scale-free and small-world networks emerge in randomly connected populations of sub-optimal players. Thus, the topological and information theoretic interpretations of bounded rationality suggest the topology, information diffusion and the strategic interactions of socio-economical structures are cyclically interdependent

The influence of topology and information diffusion on networked game dynamics

This thesis studies the influence of topology and information diffusion on the strategic interactions of agents in a population. It shows that there exists a reciprocal relationship between the topology, information diffusion and the strategic interactions of a population of players. In order to evaluate the influence of topology and information flow on networked game dynamics, strategic games are simulated on populations of players where the players are distributed in a non-homogeneous spatial arrangement. The initial component of this research consists of a study of evolution of the coordination of strategic players, where the topology or the structure of the population is shown to be critical in defining the coordination among the players. Next, the effect of network topology on the evolutionary stability of strategies is studied in detail. Based on the results obtained, it is shown that network topology plays a key role in determining the evolutionary stability of a particular strategy in a population of players. Then, the effect of network topology on the optimum placement of strategies is studied. Using genetic optimisation, it is shown that the placement of strategies in a spatially distributed population of players is crucial in maximising the collective payoff of the population. Exploring further the effect of network topology and information diffusion on networked games, the non-optimal or bounded rationality of players is modelled using topological and directed information flow of the network. Based on the topologically distributed bounded rationality model, it is shown that the scale-free and small-world networks emerge in randomly connected populations of sub-optimal players. Thus, the topological and information theoretic interpretations of bounded rationality suggest the topology, information diffusion and the strategic interactions of socio-economical structures are cyclically interdependent

The use of multilayer network analysis in animal behaviour

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.We gratefully acknowledge the 806 supporters of MX16: the UC Davis Institute for Social Sciences, the U.S. Army Research Office 807 under Multidisciplinary University Research Initiative Award No. W911NF-13-1-0340, the UC 808 Davis Complexity Sciences Center, the UC Davis Anthropology Department, the UC Davis 809 Graduate Student Association, the UC Davis Department of Engineering, and the UC Davis 810 Office of Research.Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as multilayer network analysis, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour through connected ‘layers’ of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population and evolutionary levels of organization. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.Natural Environment Research Council (NERC)National Science Foundation (NSF) Graduate Research FellowshipNFS IOS grantNIH R01NERC standard gran

Developing microRNA screening as a functional genomics tool for disease research

Originally discovered as regulators of developmental timing in C. elegans, microRNAs (miRNAs) have emerged as modulators of nearly every cellular process, from normal development to pathogenesis. With the advent of whole genome libraries of miRNA mimics suitable for high throughput screening, it is possible to comprehensively evaluate the function of each member of the miRNAome in cell-based assays. Since the relatively few microRNAs in the genome are thought to directly regulate a large portion of the proteome, miRNAome screening, coupled with the identification of the regulated proteins, might be a powerful new approach to gaining insight into complex biological processes

Non-IID recommender systems : a machine learning approach

University of Technology Sydney. Faculty of Engineering and Information Technology.A recommender system (RS) comprises the core software, tools, and techniques that effectively and efficiently cope with information overload as well as locate information that is genuinely required. As one of the most widely used artificial intelligence (AI) systems, RSs have been integrated into daily life over the past two decades. In recent decade, the machine learning approach has dominated AI research in almost all areas. Therefore, modeling advanced RSs using the machine learning approach forms the basic methodology of this thesis. Current RSs suffer from many problems, such as data sparsity and cold start, because they fail to consider the non-IIDness in data, which includes the heterogeneities and coupled relations within and between users and items, as well as their interactions. Thus, we propose non-IID recommender systems by modeling the non-IIDness in recommendation data with the machine learning approach. Specifically, we study non-IID RS modeling techniques from three perspectives: users, items, and interactions. This research not only promotes the design of new machine learning models and algorithms in theory, but also extensively influences the evolution of technology and society. To construct the non-IID RS from a user perspective, we jointly model two aspects: (1) the heterogeneities of users and (2) the coupling between users. Specifically, we study the non-IID user modeling in two representative RSs: (1) a group-based RS (GBRS) and (2) a social network-based RS (SNRS). First, we perform an in-depth analysis of existing GBRSs and demonstrate their deficiencies in modeling the heterogeneity and coupling between group members for making group decisions. A deep neural network is designed to learn a group preference representation, which jointly considers all members’ heterogeneous preferences. Second, we model an SNRS by modeling the influential contexts that embed the influence of relevant users and items, because a user’s selection is largely influenced by other users with social relationships. To construct the non-IID RS from an item perspective, we target two modeling aspects: (1) the heterogeneities of items and (2) the coupling between items. Specifically, we study the non-IID item modeling in two representative RSs: (1) a cross-domain RS (CDRS) and (2) a session-based RS (SBRS). First, existing CDRSs may fail to conduct cross-domain transfer because of domain heterogeneity; thus, we propose an irregular tensor factorization model, which can more effectively capture the coupling between heterogeneous domains with learning the domain factors for each domain. Second, we construct an effective and efficient personalized SBRS to more effectively capture the couplings between items by modeling intra- and inter-session contexts. To construct the non-IID RS from an interaction perspective, we target two modeling aspects: (1) the heterogeneities of interactions and (2) the coupling between interactions. Specifically, we study the non-IID interaction modeling in two representative RSs: (1) a multi-objective RS (MORS) and (2) an attraction-based RS (ABRS). First, we study an MORS to tackle the challenges of recommendation for users and items in the long tail. Subsequently, a coupled regularization model is proposed to jointly optimize two objectives: the credibility and specialty. Existing content-based RSs can recommend new content according to similarity; however, they are not capable of interpreting the attraction points in user-item interactions. Therefore, to construct an interpretable content-based RS, we propose attraction modeling to learn and track user attractiveness. In the last section, we summarize the contributions of our work and present the future directions that can improve and extend the non-IID RS

Disordered proteins and network disorder in network descriptions of protein structure, dynamics and function. Hypotheses and a comprehensive review

During the last decade, network approaches became a powerful tool to describe protein structure and dynamics. Here we review the links between disordered proteins and the associated networks, and describe the consequences of local, mesoscopic and global network disorder on changes in protein structure and dynamics. We introduce a new classification of protein networks into ‘cumulus-type’, i.e., those similar to puffy (white) clouds, and ‘stratus-type’, i.e., those similar to flat, dense (dark) low-lying clouds, and relate these network types to protein disorder dynamics and to differences in energy transmission processes. In the first class, there is limited overlap between the modules, which implies higher rigidity of the individual units; there the conformational changes can be described by an ‘energy transfer’ mechanism. In the second class, the topology presents a compact structure with significant overlap between the modules; there the conformational changes can be described by ‘multi-trajectories’; that is, multiple highly populated pathways. We further propose that disordered protein regions evolved to help other protein segments reach ‘rarely visited’ but functionally-related states. We also show the role of disorder in ‘spatial games’ of amino acids; highlight the effects of intrinsically disordered proteins (IDPs) on cellular networks and list some possible studies linking protein disorder and protein structure networks

Evolution from the ground up with Amee – From basic concepts to explorative modeling

Evolutionary theory has been the foundation of biological research for about a century now, yet over the past few decades, new discoveries and theoretical advances have rapidly transformed our understanding of the evolutionary process. Foremost among them are evolutionary developmental biology, epigenetic inheritance, and various forms of evolu- tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led to the conceptualization of an extended evolutionary synthesis. Starting from abstract principles rooted in complexity theory, this thesis aims to provide a unified conceptual understanding of any kind of evolution, biological or otherwise. This is used in the second part to develop Amee, an agent-based model that unifies development, niche construction, and phenotypic plasticity with natural selection based on a simulated ecology. Amee is implemented in Utopia, which allows performant, integrated implementation and simulation of arbitrary agent-based models. A phenomenological overview over Amee’s capabilities is provided, ranging from the evolution of ecospecies down to the evolution of metabolic networks and up to beyond-species-level biological organization, all of which emerges autonomously from the basic dynamics. The interaction of development, plasticity, and niche construction has been investigated, and it has been shown that while expected natural phenomena can, in principle, arise, the accessible simulation time and system size are too small to produce natural evo-devo phenomena and –structures. Amee thus can be used to simulate the evolution of a wide variety of processes