A Computational Framework for Learning from Complex Data: Formulations, Algorithms, and Applications

Many real-world processes are dynamically changing over time. As a consequence, the observed complex data generated by these processes also evolve smoothly. For example, in computational biology, the expression data matrices are evolving, since gene expression controls are deployed sequentially during development in many biological processes. Investigations into the spatial and temporal gene expression dynamics are essential for understanding the regulatory biology governing development. In this dissertation, I mainly focus on two types of complex data: genome-wide spatial gene expression patterns in the model organism fruit fly and Allen Brain Atlas mouse brain data. I provide a framework to explore spatiotemporal regulation of gene expression during development. I develop evolutionary co-clustering formulation to identify co-expressed domains and the associated genes simultaneously over different temporal stages using a mesh-generation pipeline. I also propose to employ the deep convolutional neural networks as a multi-layer feature extractor to generate generic representations for gene expression pattern in situ hybridization (ISH) images. Furthermore, I employ the multi-task learning method to fine-tune the pre-trained models with labeled ISH images. My proposed computational methods are evaluated using synthetic data sets and real biological data sets including the gene expression data from the fruit fly BDGP data sets and Allen Developing Mouse Brain Atlas in comparison with baseline existing methods. Experimental results indicate that the proposed representations, formulations, and methods are efficient and effective in annotating and analyzing the large-scale biological data sets

Neuronal cell types in the fly: single-cell anatomy meets single-cell genomics.

At around 150 000 neurons, the adult Drosophila melanogaster central nervous system is one of the largest species, for which a complete cellular catalogue is imminent. While numerically much simpler than mammalian brains, its complexity is still difficult to parse without grouping neurons into consistent types, which can number 1-1000 cells per hemisphere. We review how neuroanatomical and gene expression data are being used to discover neuronal types at scale. The correlation among multiple co-varying neuronal properties, including lineage, gene expression, morphology, connectivity, response properties and shared behavioral significance is essential to the definition of neuronal cell type. Initial studies comparing morphological and transcriptomic definitions of neuronal type suggest that these are highly consistent, but there is much to do to match these approaches brain-wide. Matched single-cell transcriptomic and morphological data provide an effective reference point to integrate other data types, including connectomics data. This will significantly enhance our ability to make functional predictions from brain wiring diagrams as well facilitating molecular genetic manipulation of neuronal types

Statistical lower bounds on protein copy number from fluorescence expression images

Motivation: Fluorescence imaging has become a commonplace for quantitatively measuring mRNA or protein expression in cells and tissues. However, such expression data are usually relative—absolute concentrations or molecular copy numbers are typically not known. While this is satisfactory for many applications, for certain kinds of quantitative network modeling and analysis of expression noise, absolute measures of expression are necessary

Gene Circuit Analysis of the Terminal Gap Gene huckebein

The early embryo of Drosophila melanogaster provides a powerful model system to study the role of genes in pattern formation. The gap gene network constitutes the first zygotic regulatory tier in the hierarchy of the segmentation genes involved in specifying the position of body segments. Here, we use an integrative, systems-level approach to investigate the regulatory effect of the terminal gap gene huckebein (hkb) on gap gene expression. We present quantitative expression data for the Hkb protein, which enable us to include hkb in gap gene circuit models. Gap gene circuits are mathematical models of gene networks used as computational tools to extract regulatory information from spatial expression data. This is achieved by fitting the model to gap gene expression patterns, in order to obtain estimates for regulatory parameters which predict a specific network topology. We show how considering variability in the data combined with analysis of parameter determinability significantly improves the biological relevance and consistency of the approach. Our models are in agreement with earlier results, which they extend in two important respects: First, we show that Hkb is involved in the regulation of the posterior hunchback (hb) domain, but does not have any other essential function. Specifically, Hkb is required for the anterior shift in the posterior border of this domain, which is now reproduced correctly in our models. Second, gap gene circuits presented here are able to reproduce mutants of terminal gap genes, while previously published models were unable to reproduce any null mutants correctly. As a consequence, our models now capture the expression dynamics of all posterior gap genes and some variational properties of the system correctly. This is an important step towards a better, quantitative understanding of the developmental and evolutionary dynamics of the gap gene network

Gene circuit analysis of the terminal gap gene <i>huckebein</i>

The early embryo of Drosophila melanogaster provides a powerful model system to study the role of genes in pattern formation. The gap gene network constitutes the first zygotic regulatory tier in the hierarchy of the segmentation genes involved in specifying the position of body segments. Here, we use an integrative, systems-level approach to investigate the regulatory effect of the terminal gap gene huckebein (hkb) on gap gene expression. We present quantitative expression data for the Hkb protein, which enable us to include hkb in gap gene circuit models. Gap gene circuits are mathematical models of gene networks used as computational tools to extract regulatory information from spatial expression data. This is achieved by fitting the model to gap gene expression patterns, in order to obtain estimates for regulatory parameters which predict a specific network topology. We show how considering variability in the data combined with analysis of parameter determinability significantly improves the biological relevance and consistency of the approach. Our models are in agreement with earlier results, which they extend in two important respects: First, we show that Hkb is involved in the regulation of the posterior hunchback (hb) domain, but does not have any other essential function. Specifically, Hkb is required for the anterior shift in the posterior border of this domain, which is now reproduced correctly in our models. Second, gap gene circuits presented here are able to reproduce mutants of terminal gap genes, while previously published models were unable to reproduce any null mutants correctly. As a consequence, our models now capture the expression dynamics of all posterior gap genes and some variational properties of the system correctly. This is an important step towards a better, quantitative understanding of the developmental and evolutionary dynamics of the gap gene network

Evolution of Insect Olfaction

Neuroethology utilizes a wide range of multidisciplinary approaches to decipher neural correlates of natural behaviors associated with an animal's ecological niche. By placing emphasis on comparative analyses of adaptive and evolutionary trends across species, a neuroethological perspective is uniquely suited to uncovering general organizational and biological principles that shape the function and anatomy of the nervous system. In this review, we focus on the application of neuroethological principles in the study of insect olfaction and discuss how ecological environment and other selective pressures influence the development of insect olfactory neurobiology, not only informing our understanding of olfactory evolution but also providing broader insights into sensory processing

Circuit Neuroscience in Zebrafish

A central goal of modern neuroscience is to obtain a mechanistic understanding of higher brain functions under healthy and diseased conditions. Addressing this challenge requires rigorous experimental and theoretical analysis of neuronal circuits. Recent advances in optogenetics, high-resolution in vivo imaging, and reconstructions of synaptic wiring diagrams have created new opportunities to achieve this goal. To fully harness these methods, model organisms should allow for a combination of genetic and neurophysiological approaches in vivo. Moreover, the brain should be small in terms of neuron numbers and physical size. A promising vertebrate organism is the zebrafish because it is small, it is transparent at larval stages and it offers a wide range of genetic tools and advantages for neurophysiological approaches. Recent studies have highlighted the potential of zebrafish for exhaustive measurements of neuronal activity patterns, for manipulations of defined cell types in vivo and for studies of causal relationships between circuit function and behavior. In this article, we summarize background information on the zebrafish as a model in modern systems neuroscience and discuss recent results

Non-Canonical Odor Coding Ensures Robust Mosquito Attraction to Humans

Aedes aegypti mosquitoes spread deadly diseases, including dengue, Zika, yellow fever, and chikungunya. Only female mosquitoes bite, and they do so because they require a blood-meal for reproduction. Aedes aegypti prefer to bite human hosts, which contributes to their effectiveness as a deadly disease vector. Mosquitoes rely heavily on chemosensory cues, including carbon dioxide (CO2) emitted from breath and human body odor, which is a mixture of more than 200 different individual odorants. Although the exact odor profile of people varies considerably, Aedes aegypti are incredibly reliable in finding humans to bite, despite widespread efforts to by humans to mask our odor. Even mosquitoes with genetic mutations that eliminate entire families of chemosensory receptors are still able to find and bite humans. It remains unknown how the mosquito olfactory system is seemingly infallible in its ability to detect humans for taking a blood meal. In the well-studied olfactory systems of Drosophila melanogaster and Mus musculus, individual olfactory sensory neurons express a single type of olfactory receptor and project their axons to discrete regions, called glomeruli, in the antennal lobe or olfactory bulb, respectively. This organization is believed to be a widespread motif in olfactory systems and has been established dogma since the mid-2000s and is hypothesized to permit the brain to parse which subpopulation of olfactory neurons is activated by a given odor. To understand how human odor is encoded in the mosquito olfactory system, we developed a CRISPR-Cas9-based genetic knock-in strategy in Aedes aegypti and generated a suite of transgenic mosquito strains that label populations of olfactory sensory neurons. Surprisingly, we find that the olfactory system of Aedes aegypti does not have the expected “one-receptor-to-one-neuron-to-oneglomerulus” organization seen in other insects. Rather, there are many more receptors than glomeruli. We frequently observe co-expression of multiple chemosensory receptors within individual olfactory sensory neurons and individual glomeruli are commonly innervated by olfactory sensory neurons expressing different receptors. What is the functional consequence of this unconventional organization? To understand how co-expression of multiple chemosensory families affects human odor detection by mosquitoes, we examined a minimal mixture that drives host seeking behavior. Mosquitoes are attracted to the combination of the two human-derived, cues CO2 and lactic acid. We found that the same neurons that sense CO2 also sense volatile amines, including triethyl amine. These amines are detected by separate chemosensory receptor genes and we discovered that these cues can be interchanged to drive attraction in the presence of lactic acid. This sensory organization, in which multiple receptors that respond to very different types of chemicals are co-expressed, suggests a redundancy in the odor code at the level of the olfactory sensory neurons for cues that signal the presence of a human to bite. We speculate that this design supports the robust human host-seeking seen in this olfactory specialist

HENA, heterogeneous network-based data set for Alzheimer's disease.

Alzheimer's disease and other types of dementia are the top cause for disabilities in later life and various types of experiments have been performed to understand the underlying mechanisms of the disease with the aim of coming up with potential drug targets. These experiments have been carried out by scientists working in different domains such as proteomics, molecular biology, clinical diagnostics and genomics. The results of such experiments are stored in the databases designed for collecting data of similar types. However, in order to get a systematic view of the disease from these independent but complementary data sets, it is necessary to combine them. In this study we describe a heterogeneous network-based data set for Alzheimer's disease (HENA). Additionally, we demonstrate the application of state-of-the-art graph convolutional networks, i.e. deep learning methods for the analysis of such large heterogeneous biological data sets. We expect HENA to allow scientists to explore and analyze their own results in the broader context of Alzheimer's disease research