Neuronal underpinning of reproductive state dependent olfactory behavior in Drosophila

A general question in neuroscience is how the flow of sensory information is encoded towards a behavioral response. These behavioral responses can be interpreted as decisions the organism needs to make to get a most beneficial outcome. Factors which can influence these decisions can be external or internal. Considering sensory information, external stimuli can elicit "innate" responses to a sensory input, which lead to a certain behavior. Interestingly, these responses can be overwritten given a certain experience or context. The internal state of an organism can be such a context. Internal states, such as age, stress, hunger, or reproductive state can have effects on chemosensory decision making behavior. Such behavior usually manifests itself by attraction or aversion towards a certain odor or taste. Occasionally, transient neuromodulation can affect these behaviors, by focusing an animal's attention to relevant sensory stimuli in its environment. This might facilitate remembering relevant vs. irrelevant stimuli. Here, we are investigating the role of such a sensory neuromodulation and the formation of memory in the female fruit fly, Drosophila melanogaster. Previous work from our lab has shown that mating changes the sensitivity of olfactory and gustatory neurons with the help of specific neuromodulators that act directly on these chemosensory neurons. However, this very transient neuromodulation leads to a long-term behavioral change in females: for instance, while virgin flies usually prefer low concentrations of polyamines, mated flies will prefer higher concentrations after the mating experience and will continue this behavior for up to two weeks until falling back to a virgin-like state. Drosophila's genetic toolset allows us to test the hypothesis that this transient sensory enhancement facilitates the formation of a long-lasting memory. Using a quantitative olfactory choice assay, my collaborators and I silenced and activated neuronal activity in different parts of the fly's associative memory center (i.e. the mushroom body). We revealed a possible neuronal pathway and its modulatory switch between virgin and mated state. These findings suggest that dopaminergic neurons, which are innervating the mushroom body, control virgin vs. mated female behavior by processing sensory input differentially before and after mating, respectively. Furthermore, our data suggests that courtship and pheromones are highly important signals to trigger the reproductive state dependent change in olfactory preference behavior. In addition, my collaborators and I wanted to use state-of-the-art techniques to shed light on the detection of nutrients valuable for the gravid fly by using bioinformatic tools and to promote these methods to the biological fields. As two-photon laser scanning microscopy is an important tool for neuroscientific research in the fly and beyond, I built such a microscope. Harnessing this experience, I have, in collaboration, written a guide for life scientists wishing to build or purchase such a microscope. A joint effort between established behavioral assays and technological advances, such as bioinformatic tools, can support and extend our understanding of neuronal circuits underlying reproductive state dependent behaviors

Networks, Communication, and Computing Vol. 2

Networks, communications, and computing have become ubiquitous and inseparable parts of everyday life. This book is based on a Special Issue of the Algorithms journal, and it is devoted to the exploration of the many-faceted relationship of networks, communications, and computing. The included papers explore the current state-of-the-art research in these areas, with a particular interest in the interactions among the fields

Profiling with Big Data: Identifying Privacy Implication for Individuals, Groups and Society

User profiling using big data raises critical issues regarding personal data and privacy. Until recently, privacy studies were focused on the control of personal data; due to big data analysis, however, new privacy issues have emerged with unidentified implications. This paper identifies and investigates privacy threats that stem from data-driven profiling using a multi-level approach: individual, group and society, to analyze the privacy implications stemming from the generation of new knowledge used for automated predictions and decisions. We also argue that mechanisms are required to protect the privacy interests of groups as entities, independently of the interests of their individual members. Finally, this paper discusses privacy threats resulting from the cumulative effect of big data profiling

Neuronal underpinning of reproductive state dependent olfactory behavior in Drosophila

A general question in neuroscience is how the flow of sensory information is encoded towards a behavioral response. These behavioral responses can be interpreted as decisions the organism needs to make to get a most beneficial outcome. Factors which can influence these decisions can be external or internal. Considering sensory information, external stimuli can elicit "innate" responses to a sensory input, which lead to a certain behavior. Interestingly, these responses can be overwritten given a certain experience or context. The internal state of an organism can be such a context. Internal states, such as age, stress, hunger, or reproductive state can have effects on chemosensory decision making behavior. Such behavior usually manifests itself by attraction or aversion towards a certain odor or taste. Occasionally, transient neuromodulation can affect these behaviors, by focusing an animal's attention to relevant sensory stimuli in its environment. This might facilitate remembering relevant vs. irrelevant stimuli. Here, we are investigating the role of such a sensory neuromodulation and the formation of memory in the female fruit fly, Drosophila melanogaster. Previous work from our lab has shown that mating changes the sensitivity of olfactory and gustatory neurons with the help of specific neuromodulators that act directly on these chemosensory neurons. However, this very transient neuromodulation leads to a long-term behavioral change in females: for instance, while virgin flies usually prefer low concentrations of polyamines, mated flies will prefer higher concentrations after the mating experience and will continue this behavior for up to two weeks until falling back to a virgin-like state. Drosophila's genetic toolset allows us to test the hypothesis that this transient sensory enhancement facilitates the formation of a long-lasting memory. Using a quantitative olfactory choice assay, my collaborators and I silenced and activated neuronal activity in different parts of the fly's associative memory center (i.e. the mushroom body). We revealed a possible neuronal pathway and its modulatory switch between virgin and mated state. These findings suggest that dopaminergic neurons, which are innervating the mushroom body, control virgin vs. mated female behavior by processing sensory input differentially before and after mating, respectively. Furthermore, our data suggests that courtship and pheromones are highly important signals to trigger the reproductive state dependent change in olfactory preference behavior. In addition, my collaborators and I wanted to use state-of-the-art techniques to shed light on the detection of nutrients valuable for the gravid fly by using bioinformatic tools and to promote these methods to the biological fields. As two-photon laser scanning microscopy is an important tool for neuroscientific research in the fly and beyond, I built such a microscope. Harnessing this experience, I have, in collaboration, written a guide for life scientists wishing to build or purchase such a microscope. A joint effort between established behavioral assays and technological advances, such as bioinformatic tools, can support and extend our understanding of neuronal circuits underlying reproductive state dependent behaviors

Big Data Analytics and Information Science for Business and Biomedical Applications

The analysis of Big Data in biomedical as well as business and financial research has drawn much attention from researchers worldwide. This book provides a platform for the deep discussion of state-of-the-art statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions are showcased

MECHANISTIC MODELS OF INTERACTIONS WITHIN AND BETWEEN MAPK PATHWAYS

Cells use signaling pathways to receive and process information about their environment. Understanding signaling pathways is of particular interest because pathway dysregulation of these pathways is implicated in many human diseases including many types of cancer. In this dissertation, I specifically address understanding interactions that govern response complex dynamics and heterogeneity within and between signaling pathways. In particular, I focus on two well-characterized MAPK pathways with homology to human signaling pathways implicated in cancer, the mating response pathway (homologous to ERK) and the high osmolarity glycerol (HOG) response pathway (homologous to p38) of S. cerevisiae (yeast). Although much is known about the molecular components of these pathways, less is known about how these components function as a dynamical system and regulate heterogeneity in the pathway responses. To address this gap in knowledge, we developed experimental techniques that allow for quantification of response dynamics and variability (Chapter 2). These methods were then applied to develop a predictive, mechanistic model of the dynamics of the mating response pathway (Chapter 3) that elucidates how various signaling motifs contribute to the overall dynamics. Additionally, these methods were used to provide insight into the mechanisms that drive heterogeneity in mating response alone (Chapter 4) and increase heterogeneity in the mating response when the HOG pathway is also active (Chapter 5). Together, the work included in this dissertation reveal how quantitative experimental methods and mathematical models can be integrated to understand aspects of signaling pathway response that could not have otherwise been studied.Doctor of Philosoph

Genetic Dissection of the Neural Circuitry Underlying Memory Stability in Drosophila: A Dissertation

Understanding how memory is formed requires looking beyond the genes involved to the neural circuitry and temporal aspects of memory. In this dissertation I have focused my investigation on Dorsal Paired Medial (DPM) neurons, two modulatory neurons essential for memory in Drosophila. DPM neurons highly express the amnesiac (amn) gene, which encodes for a putative pre-pro-neuropeptide. amn function in DPM neurons is required for memory. Here I provide evidence that DPM neurons are cholinergic and that acetylcholine (ACh) and AMN act as co-transmitters essential for DPM function. In order to investigate the temporal requirements of DPM output I blocked transmitter release during discrete intervals in the memory process using shibirets1 and tested flies for shock and sugar-reinforced memory. These experiments demonstrated that stable memory requires persistent transmitter release from DPM neurons. Furthermore these results suggest AMN and DPM neurons act as general stabilizers of mushroom body dependent memory. To further investigate the neural circuitry underlying DPM function I disrupted DPM projections onto the mushroom body lobes by ectopically expressing DScam17-2::GFP in DPM neurons. Flies with DPM neurons that predominantly project to the mushroom body α´/β´ lobes exhibit normal memory, and blocking transmitter release from the mushroom body prime lobes neurons themselves abolishes memory indicating DPM neuron-mushroom body α´/β´ neuron interaction that are critical for memory. Taken together, the experimental evidence presented here are used to provide a rudimentary model of the neural circuitry involved in memory stability, where DPM neurons form a recurrent feedback loop with the mushroom body α´/β´ lobe neurons and act to stabilize odorspecific conditioned memories at Kenyon cell synapses

A systems and molecular analysis of G protein-mediated signalling

The ability of cells to respond correctly to signals from their microenvironment is an essential prerequisite of life. Many external signals are detected through G protein-coupled receptor (GPCR) signalling pathways, which control all aspects of eukaryotic physiology. Ligand-bound GPCRs initiate signalling by promoting exchange of GDP for GTP on the Gα subunit of heterotrimeric G proteins, thereby facilitating activation of downstream effectors. Signalling is terminated by the hydrolysis of GTP to GDP through intrinsic GTPase activity of the Gα subunit, in a reaction catalysed by the regulator of G protein signalling (RGS) proteins. Due to the problem of complexity in higher eukaryotic GPCR signalling, the matingresponse in Schizosaccharomyces pombe has been used to study GPCR signalling in isolation. In vivo data from quantitative assays of reporter strains and live-cell uorescence microscopy informs the development of an ordinary differential equation model of the signalling pathway, first described by Smith et al., 2009. The rate of nucleotide exchange on the Gα (Gpa1) is a key molecular mechanism controlling duration and amplitude of signalling response. The in uence of this is investigated through characterisation of Gpa1 nucleotide exchange mutants and perturbation of reaction rate parameters in the computational model. Further, this thesis also presents data relating to the temporal and spatial regulation of Rgs1 (the sole RGS protein for Gpa1). Using an inter-disciplinary approach, evidence is provided to suggest that an interaction between Rgs1 and the C-terminal tail of the GPCR (Mam2) tethers Rgs1 to the plasma membrane to facilitate its function. Finally, quantification of signalling at the single cell level is described. Time-lapse livecell imaging of uorescent reporter cells is optimised and single cell signalling response quantified using image analysis software. Single cell quantification provides greater insight into temporal dynamics, cell-to-cell variability, and highlights the existence of mechanisms for cellular decision-making

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field