Despite being flanked by the Great Basin and cismontane California, the Sierra Nevada has not played a prominent role in discussions of hunter-gatherer land use in either region. A key reason is the lack of archaeological data from pristine alpine areas, where little archaeological research has occurred. This study investigates high-elevation sites in the southern Sierra Nevada using temperature-adjusted obsidian hydration rates and comparisons to adjoining regions. Two distinct archaeological patterns are identified. The earlier (ca. 3,500 B.P.-1,350 B.P.) limited-use pattern is characterized by dense lithic scatters related to obsidian procurement and logistical hunting forays, most likely by small groups of men. The later (ca. 1,350 B.P.—historic contact) intensive-use pattern is typified by a greater variety of artifact and feature types indicative of a wider range of activities performed by more diverse groups. These broad archaeological patterns are compared to regional cultural developments on either side of the Sierra to investigate how large-scale changes in mobility, subsistence-settlement patterns, and obsidian procurement in core lowland areas influenced prehistoric use of the southern Sierra Nevada alpine zone.

Biopharmaceuticals are emerging as a promising avenue for treating a range of diseases 1, and bringing down the costs of production, as well as monitoring the response to treatment, is of high importance. One technology to assist in these needs is next generation sequencing (NGS). In this dissertation, I use emergent NGS techniques to provide valuable resources in two important cell types, the Chinese hamster ovary (CHO) cell-line in Chapter 2, and CD34+ haematopoietic stem and progenitor cells (HSPCs) in Chapter 3. CHO cells are currently the most used cell line for producing recombinant monoclonal antibodies, and optimization of this cell line including media control and gene engineering has brought down production costs 2. However, costs can still be prohibitive, and the genome resources required for novel gene engineering techniques are limited by the resolution of the genome annotation. In Chapter 2, I revise the TSS genome annotation using two different TSS sequencing techniques across multiple tissues in the Chinese hamster. TSSs were detected in 15308 protein-coding genes, and detected TSSs in 13037 of these genes had TSSs revised by at least 10 base pairs from the nearest NCBI RefSeq TSS. More promoter motif elements were detected at the revised TSSs than at the NCBI TSSs. To demonstrate the accuracy and functionality of our revised TSS annotation we activated the dormant Mgat3 gene in CHO cells by CRISPR activation 3 using a novel identified TSS. CD34+ HSPC cells are the target in cytokine therapies to recruit cells to differentiate into desired immune lineages. Studying the clonal lineage multipotent capacity is important for understanding cellular response to therapy. Recent studies have shown that there is heterogeneity in HSPC clones, which are cells from the same phylogenetic origin, in both their growth and hematopoietic multipotent capacity. However, tracking HPSC clones in humans is limited. In Chapter 3, clonal heterogeneity is tracked across multiple donors in steady-state and in response to ex vivo cytokine cocktail using mitochondrial single-cell ATAC-seq. Cells are grouped into clones using naturally occurring somatic mitochondrial mutations, and their lineages assessed using regulatory regions in the nuclear open-chromatin. Larger clones make up a large fraction of donor HSPC donor populations, with no clone preferentially responding to culture. Most clones show multipotent capacity, differentiating into multiple immune lineage progenitors. Overall, this dissertation provides an improved genome annotation in CHO cells and an analysis on HSPC lineage bias in steady-state and in response to cytokine treatment.

With advances in mass spectrometry, there is increasing demand for more effective and adaptive approaches to systematically extract biological insights from glycomic and lipidomic data. This thesis explores the application of Markov modeling in understanding two key cellular processes, N-glycosylation and lipid biosynthesis.In Chapter 2, we demonstrate that a Markov model of N-glycosylation network successfully captures intricate glycosyltransferase interactions by reproducing a set of glycoprofiles from glycoengineered CHO cells producing erythropoietin (EPO). We further validate the model parsimony and accuracy by predicting the glycoprofiles of other glycoengineered drugs from the trained models and their wildtype glycoprofiles. The results attest to the model’s ability to learn glycosyltranferase activities and substrate specificities. To increase the impact of this approach, we also develop GlycoMME to allow broader access to this modeling pipeline, presenting a promising direction for rational glycoengineering. Chapter 3 extends the modeling approach to lipid biosynthesis, introducing the Lipid Synthesis Investigative Markov Model (LipidSIM). As a low-parameter, biologically interpretable framework, LipidSIM proves powerful in leveraging the interdependency in lipidomic data and extracts and quantifies perturbations to lipid biosynthesis reactions, generating hypotheses directly testable by transcriptomic data. This method is showcased in 5 different scenarios with 3 different lipidomic datasets, and the results substantiate LipidSIM as a valuable tool for extracting insights from high-dimensional lipidomic data of different types. In Chapter 4, a Taguchi design is used to systematically characterize the impact of 15 media supplements on potential cellular phenotypes for CHO cells, especially N-glycosylation. The analytical pipeline allows disentangling the impact of individual supplements at different concentration levels with minimal numbers of experimental configurations. This approach answers the demand to find more flexible strategies for controlling N-glycosylation beyond genetic engineering. When applied in conjunction with the Taguchi design, our modeling framework has the potential to facilitate rapid customization of media for the growing market of glycoprotein drugs. This thesis encapsulates the innovative applications of probabilistic modeling in accounting for the biological dependency underlying omics data, offering insights into intricate cellular processes and motivating further exploration in actionable modeling of biological systems.

Recent research has documented meditation-related experiences in contemporary Buddhist traditions and the appraisal processes involved with either normalizing or pathologizing such experiences. These studies have complicated the frameworks that even clinicians and psychologists use to make such differential diagnoses but to date there has been almost no comparable work examining how such appraisal processes are navigated in other religious and spiritual communities. This thesis makes a contribution in this area by investigating some of the textual sources and themes that contemporary teachers and students in Western Abrahamic contemplative traditions draw upon to interpret distressing and challenging meditation-related experiences. When appraising difficult experiences as ‘part of the path’ in contemporary Christian traditions, teachers and practitioners invoked both Teresa of Avila and John of the Cross, particularly the latter’s discussion of the “dark nights,” and the contemplative path structure utilized by both of purgation-illumination-union. Purgation or purification was often translated into a psychological key and Thomas Keating’s conception of “divine therapy” and “the unloading of the unconscious” in the Centering prayer tradition was found to be influential in this formulation. In Western Sufi traditions, challenging or distressing experiences were understood as normative in relation to Classical Sufi psychological literature describing different states (hal) and stations (maqamat) of the Sufi path (tariqa), particularly those related to a process of purification or “polishing the mirror of the heart.” Other key ideas referenced in normalizing interpretations were the valley of bewilderment (hayrat) in Farid ad-Din Attar’s The Conference of the Birds, descriptions of painful experiences in the poetry of Jallal al-din Rumi, and the dual concept of annihilation (fana) and subsistence (baqa) that is ubiquitous in classical Sufi literature. Jewish teachers and practitioners referenced a constellation of several key texts, figures, and ideas in appraising challenging experiences as ‘part of the path’ as well: mystical interpretations of the Song of Songs, “descent for the sake of ascent” (yeridah tzorech aliyah) and purification, the doctrine of “expanded and constricted consciousness” (mochin d’gadlut, mochin d’katnut), the Hasidic practice and doctrine of elevating sparks and “foreign thoughts” (machshavot zerot), and finally, liminality and “the breaking of the vessels” (shevirat ha-keilim). In introducing the conception of ‘non-linear paths’ to describe these trajectories of contemplative practice invoked by contemporary teachers and practitioners, this thesis seeks to highlight the ways in which certain negatively valenced meditation- related experiences are normalized even as individual variability and unpredictability has been acknowledged for centuries in these traditions.

Protein secretion is one of the most fundamental biological processes in all living organisms. The secretory pathway consists of a complex network of endomembrane systems and associated proteins working together to synthesize, post-translationally modify, transport, and quality control thousands of membrane and secreted proteins vital for cell-cell communication, cell adhesion, modification of the extracellular matrix, and immunity. Not surprisingly, perturbations to the secretory system are seen in various diseases such as cancer1, Alzheimer’s2,3, Parkinson’s4, and non-alcoholic fatty liver disease5. Furthermore, protein secretion is heavily exploited in biotechnology, exemplified by the extensive use of “cell factories” for producing biopharmaceuticals and industrial enzymes. In fact, many of the most important biotherapeutics and monoclonal antibodies are produced via the secretory pathway in mammalian cell culture. Thus, due to its biomedical importance, understanding protein secretion at the molecular level is fundamental to elucidating the underlying pathophysiology of diseases, and is paramount for the efficient production of life-altering biotherapeutics.

In this doctoral dissertation, computational tools and methods are developed to enable systems biology analysis of the secretory pathway. First, transcriptomic and machine learning methods are used to decipher the determinants of recombinant protein yield in the most popular bioproduction host, Chinese hamster ovary (CHO) cells, across the human secretome. Results from this study serve as a resource for future work, building the framework needed to understand protein production in this host system. Second, complex genome scale models are used to generate a user-friendly tool that can predict and quantify metabolic and secretory activity of cells. This tool enables anyone, regardless of their computational background, to monitor and quantify multiple complex biological systems providing phenotypic interpretation from omics data. Finally, using a bottom-up systems biology approach based on collecting, assembling, integrating, and curating information and data by a combination of comprehensive literature surveys and searches in numerous databases, a comprehensive map of the secretory landscape capturing spatial and functional pathway organization is presented. By accounting for all the molecular machinery involved in protein secretion, one can more effectively diagnose the cause of variations in protein secretion through the use of innovative big data analysis techniques and tools from systems biology.

Many data types are used in bioinformatics research, including genomics, transcriptomics, proteomics, pathway data, disease network, and gene ontology (GO) data, which are heavily studied in disease diagnosis or biomarker detection. The use of newer data types, such as glycomics, fMRI, and facial behavior data, is also growing and can provide unique perspectives for disease cell biology. These new data types have unique properties that require newly adapted algorithms for precise and granular characterization, which is essential before machine learning or statistical models can be confidently used to study disease mechanisms or identify biomarkers from large-scale datasets. The newly developed tools can then allow sophisticated evaluations and yield high-quality results. The first part of my thesis introduced GlyCompare, a powerful glycomics analysis pipeline. The pipeline corrects for the sparsity and non-independence in glycomics data by accounting for the shared biosynthetic network in the data. This new approach makes the downstream analyses more interpretable and better powered.Then in the second part, a generalizable machine learning platform was developed with 42,840 models composed of 3570 gene expression feature sets and 12 classification methods. A gene expression ASD diagnostic classifier built with this platform had AUC-ROC ≥ 0.8 on both Training and Test sets. Our classifier is diagnostically predictive and replicable across different toddler ages, races, and ethnicities; outperforms the risk gene mutation classifier; and has potential for clinical translation. In the last section, I developed a pipeline to evaluate facial behavior data from toddlers using state-of-the-art expression analysis software. In certain situations, emotional response is overly intense in ASD compared to other toddlers. Our action unit classifier had a sensitivity of 83.3% and a specificity of 67.5% in the test dataset (90.1% and 75% in the training dataset). We verified that our classifier was unbiased against common confounding factors (age, race, and ethnicity). By combining the action unit classifier and Geo-Pref non-social score, we achieved a specificity of 100% and sensitivity of 50% on the training and test datasets. The ensemble classifier maintained the high specificity while considerably increasing the sensitivity, which provides the potential for screening applications.

In mammalian systems, no cell acts in isolation, but rather coordinates to achieve higher-order function. Such cell behaviors are complex and influenced by context. To comprehensively understand them, we must understand how molecular interactions affect cell phenotypes, and analogously, how cell interactions affect higher-order phenotypes. I begin by examining the role of resource allocation in cellular decision-making processes. I underscore the significance of resource constraints and context in shaping cellular phenotypes and enabling population-level behaviors. My research then pivots to a detailed investigation into a rare systemic inflammatory disorder, Disabling Pansclerotic Morphea (DPM). I report on the discovery of novel variants in the STAT4 gene that are linked to DPM. Leveraging these insights, we propose a successful therapeutic approach using the JAK inhibitor, ruxolitinib, thereby demonstrating the importance of a context-informed genetic understanding in disease management. The JAK-STAT pathway is a key signaling pathway mediating immune cell communication. Thus, I shift the focus of my research to intercellular communication. My novel unsupervised method, Tensor-cell2cell, deciphers complex cell-cell communication patterns across multiple contexts (e.g., time points, disease severities, or spatial contexts). Given the generalizability of this approach to use other communication methods’ outputs as its input, I then introduce a protocol integrating two computational tools, LIANA and Tensor-cell2cell. LIANA is similarly generalizable in that it provides a centralized resource to run many methods, thus providing a natural preceding step to Tensor-cell2cell. The protocol enhances robustness and flexibility in identifying cell-cell communication programs across multiple samples. Finally, I present humanME, a computational tool for generating and analyzing human ME-Models from input metabolic models. This approach refines the prediction accuracy of growth rate and offers unique solutions, highlighting the importance of machinery resources in constraining intracellular activities. Collectively, this body of work leverages omics to provide mechanistic insights to how cellular context impacts interactions and functions in mammalian systems across molecular-, cell-, and tissue-scales. The new methods and tools proposed herein pave the way for more nuanced, context-driven research, underpinning future advancements in human health and disease.