CRISPR/CASTE: Functional Genomic Studies of the Major Evolutionary Innovations of Ants

Ants are social organisms that live in groups and depend intimately on their nestmates for growth and survival. Ants have evolved a number of highly sophisticated, social phenotypes that allow them to form coherent colonies. This thesis explores two particularly derived features of ant biology: complex chemical communication and caste plasticity. To study these features, I had a particular focus on generating and characterizing germ-line mutants. I believe that the study of mutants, and applying molecular biology methods more generally, can lead to insights in ant biology that would not be possible with more traditional methods. I first describe my efforts to develop a CRISPR protocol to make the first germ-line mutant ant lines. I conducted this work using a unique, clonal ant species, Ooceraea biroi, that has many properties making it favorable for laboratory genetics studies. Establishing this protocol required a multi-year optimization process to account for many of the particular features of ant biology, such as egg production and incubation, growing and maintaining lines, and optimizing experimental treatments to produce high mutagenesis rates. I next describe the mutants I generated using these methods, null mutants of a highly conserved insect protein called orco. Orco, or olfactory receptor co-receptor, is required for the function of an important class of chemosensory proteins, the odorant receptors, in insects. I created orco mutant ants and found that they have striking deficiencies in their social behavior and fitness, including an inability to nest with other ants or follow chemical pheromone trails and severely reduced life span and fecundity. These results supported the growing consensus that odorant receptors are key chemosensory proteins for pheromone perception in ants, and provided a new window into ant social behavior and collective organization. Unexpectedly, and unlike orco mutants in other types of insects, I also found that orco mutant ants have severe neuro-anatomical deficiencies, including a loss of most antennal lobe glomeruli and sensory neurons in the antenna. This surprising result implies that orco may play an important role in antennal lobe morphology in ants, and could provide insights into the development and evolution of complex olfactory systems. The following chapter is a critical literature review of the development and evolution of morphological castes, such as workers and queens, in ants. I describe a stereotyped and previously overlooked pattern of morphological variation in ants, and illustrate how this pattern may provide some early insights into the molecular mechanisms of caste plasticity. This chapter provides a falsifiable, mechanistic framework for caste development and suggests a route to start looking for the actual molecules that regulate this interesting process. Finally, I start to realize this promise by characterizing a caste mutant in the laboratory. I discovered a spontaneous ‘winged mutant’ that belongs to one of the known clonal lineages of O. biroi and aberrantly expresses queen-like morphology and behavior at worker-like body sizes. These mutants bear a striking resemblance to one class of ant species with derived caste systems, the recurrently evolved workerless social parasites. They could thus provide a window into the mutations that give rise to these unique ants. Overall, this thesis represents the first characterization of mutant lines in ants, and I hope it demonstrates how this approach can be used to generate robust conclusions about ant biology

ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special track at WSTST 2005, Muroran, JAPA

Optimal dynamic operations scheduling for small-scale satellites

A satellite's operations schedule is crafted based on each subsystem/payload operational needs, while taking into account the available resources on-board. A number of operating modes are carefully designed, each one with a different operations plan that can serve emergency cases, reduced functionality cases, the nominal case, the end of mission case and so on. During the mission span, should any operations planning amendments arise, a new schedule needs to be manually developed and uplinked to the satellite during a communications' window. The current operations planning techniques over a reduced number of solutions while approaching operations scheduling in a rigid manner. Given the complexity of a satellite as a system as well as the numerous restrictions and uncertainties imposed by both environmental and technical parameters, optimising the operations scheduling in an automated fashion can over a flexible approach while enhancing the mission robustness. In this paper we present Opt-OS (Optimised Operations Scheduler), a tool loosely based on the Ant Colony System algorithm, which can solve the Dynamic Operations Scheduling Problem (DOSP). The DOSP is treated as a single-objective multiple constraint discrete optimisation problem, where the objective is to maximise the useful operation time per subsystem on-board while respecting a set of constraints such as the feasible operation timeslot per payload or maintaining the power consumption below a specific threshold. Given basic mission inputs such as the Keplerian elements of the satellite's orbit, its launch date as well as the individual subsystems' power consumption and useful operation periods, Opt-OS outputs the optimal ON/OFF state per subsystem per orbital time step, keeping each subsystem's useful operation time to a maximum while ensuring that constraints such as the power availability threshold are never violated. Opt-OS can provide the flexibility needed for designing an optimal operations schedule on the spot throughout any mission phase as well as the ability to automatically schedule operations in case of emergency. Furthermore, Opt-OS can be used in conjunction with multi-objective optimisation tools for performing full system optimisation. Based on the optimal operations schedule, subsystem design parameters are being optimised in order to achieve the maximal usage of the satellite while keeping its mass minimal

Temporal analysis of honey bee interaction networks based on spatial proximity

The BeesBook system provides high-resolution data about bee movements within a single colony by automatically tracking individual honey bees inside a hive over their entire life. This thesis focuses on the process of designing and implementing a network pipeline to extract interaction networks from this data. Spatial proximity is used as an indicator for interactions between bees. Social network analysis methods were applied to investigate the static and dynamic properties of the resulting social networks of honey bees on a global, intermediate and local level. The resulting networks were characterized by a low hierarchical structure and a high density. The global structure of the colony seems to be stable over time. The local structure is highly dynamic, as bees change communities as they age. Communities in the honey bee network are formed by age groups that show a high spatial fidelity. The findings are in line with the established state of research that colonies are organized around age-based task division. The results of the analysis validate the implemented pipeline and the inferred networks. Consequently, this work provides an excellent foundation for future research focusing on temporal network analysis

Could fungal infection make ant societies more open?

Ant colonies are a highly rewarding target for many pathogens and parasites and they also host various species of social parasites that exploit their social system. Myrmica ants seem to be particularly susceptible to exploitation by social parasites and by ecto- and endoparasites. Also many Myrmica colonies frequently adopt unrelated gynes, which can be interpreted as temporary social parasites. Myrmica scabrinodis is a common host of the ectoparasitic fungus Rickia wasmannii and its colonies are used by socially parasitic butterfly larvae of Maculinea genus. In some M. scabrinodis populations both R. wasmannii and Maculinea butterflies occur together using the same host colonies. In our study we used such population to check whether fungal infection change the threshold of acceptance of social parasites and unrelated queens by ants and make colonies more open for strangers. For this purpose we performed experiments during which we tested how infection by R. wasmannii affects frequency and time of Maculinea larva adoption and adoption of unrelated ant queens. We also carried out aggression tests where we used M. scabrinodis workers originating from infected and uninfected colonies to check if fungal infection influences the amount of adverse reactions. Our preliminary results indicate that ants infected by the fungus are more readily adopting social parasites, while being less aggressive towards foreign queens. Thus, infected colonies could be more prone for social parasitism

The neurogenetics of group behavior in Drosophila melanogaster

Organisms rarely act in isolation. Their decisions and movements are often heavily influenced by direct and indirect interactions with conspecifics. For example, we each represent a single node within a social network of family and friends, and an even larger network of strangers. This group membership can affect our opinions and actions. Similarly, when in a crowd, we often coordinate our movements with others like fish in a school, or birds in a flock. Contributions of the group to individual behaviors are observed across a wide variety of taxa but their biological mechanisms remain largely unknown. With the advent of powerful computational tools as well as the unparalleled genetic accessibility and surprisingly rich social life of Drosophila melanogaster, researchers now have a unique opportunity to investigate molecular and neuronal determinants of group behavior. Conserved mechanisms and/or selective pressures in D. melanogaster can likely inform a much wider phylogenetic scale. Here, we highlight two examples to illustrate how quantitative and genetic tools can be combined to uncover mechanisms of two group behaviors in D. melanogaster: social network formation and collective behavior. Lastly, we discuss future challenges towards a full understanding how coordinated brain activity across many individuals gives rise to the behavioral patterns of animal societies

Short-term activity cycles impede information transmission in ant colonies.

Rhythmical activity patterns are ubiquitous in nature. We study an oscillatory biological system: collective activity cycles in ant colonies. Ant colonies have become model systems for research on biological networks because the interactions between the component parts are visible to the naked eye, and because the time-ordered contact network formed by these interactions serves as the substrate for the distribution of information and other resources throughout the colony. To understand how the collective activity cycles influence the contact network transport properties, we used an automated tracking system to record the movement of all the individuals within nine different ant colonies. From these trajectories we extracted over two million ant-to-ant interactions. Time-series analysis of the temporal fluctuations of the overall colony interaction and movement rates revealed that both the period and amplitude of the activity cycles exhibit a diurnal cycle, in which daytime cycles are faster and of greater amplitude than night cycles. Using epidemiology-derived models of transmission over networks, we compared the transmission properties of the observed periodic contact networks with those of synthetic aperiodic networks. These simulations revealed that contrary to some predictions, regularly-oscillating contact networks should impede information transmission. Further, we provide a mechanistic explanation for this effect, and present evidence in support of it

Social networks predict the life and death of honey bees

In complex societies, individuals' roles are reflected by interactions with other conspecifics. Honey bees (Apis mellifera) generally change tasks as they age, but developmental trajectories of individuals can vary drastically due to physiological and environmental factors. We introduce a succinct descriptor of an individual's social network that can be obtained without interfering with the colony. This 'network age' accurately predicts task allocation, survival, activity patterns, and future behavior. We analyze developmental trajectories of multiple cohorts of individuals in a natural setting and identify distinct developmental pathways and critical life changes. Our findings suggest a high stability in task allocation on an individual level. We show that our method is versatile and can extract different properties from social networks, opening up a broad range of future studies. Our approach highlights the relationship of social interactions and individual traits, and provides a scalable technique for understanding how complex social systems function. Honey bee workers take on different tasks for the colony as they age. Here, the authors develop a method to extract a descriptor of the individuals' social networks and show that interaction patterns predict task allocation and distinguish different developmental trajectories