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Open Access Publications from the University of California

Scholarly Works (20 results)

  • Thesis
  • Peer Reviewed
UC Santa Barbara Electronic Theses and Dissertations (2021)

Today, a “High-carbon Energy compleX” (HEX) of fossil-fuel enterprises and their allies hold the future captive, attempting to indefinitely preserve global reliance on carbon- emitting energy. Emergent social forces offer alternative futures, from ‘climate capitalism’ to energy democracy and more. Public collective action in the United States of America represents a potential pivotal force in this world-historical crisis, capable of helping initiate a break from catastrophic futures and opening up more democratic and humane alternatives. Studies of public opinion in the United States, framed by a critical analysis of social power that foregrounds the contours of domination and possibilities of resistance, may therefore be essential. This study attempts this through a 2-phase method: Qualitative research on the beliefs of climate-focused scholars across the country (n=22) and quantitative research using a national survey of public perception (n=1065). I demonstrate that the US public is already highly concerned about climate change and supportive of strategically necessary democratizing and redistributive policy solutions. However, both the public and experts show only weak support for social movement action and repertoires of contention1 capable of delivering these solutions. I argue that the most pressing need for intervention by social scientists and activists today is not the production of concern or action divorced from power analysis, but instead the cultivation of power analysis, building public capacities for power-literate strategy to guide collective action toward democratic and ecologically vibrant futures.

    Cover page: Captive Futures
    • Thesis
    • Peer Reviewed
    UC San Diego Electronic Theses and Dissertations (2016)

    Microbial cell factories can have a transformative impact the chemical industry, but, first, we must meet the challenges of designing and optimizing high-yield cell factory strains. The most popular conceptual model for cell factory optimization is the design-build-test-learn cycle. I present methods that use systems biology to improve the optimization process in each of these steps. First, the build step requires a parts list for a host organism and any heterologous pathways. I present BiGG Models, a database of more than 75 high-quality, manually-curated genome-scale metabolic models that comprise a standardized metabolic parts list. BiGG Models has become the most popular resource in the community for gold-standard genome-scale metabolic models. For the test step, contextualization of omics data is an enormous challenge, and I developed a visualization tool to address this challenge. Escher is a web application for visualizing data on biological pathways. With Escher, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP) and metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). For the learn step, genome-scale models can be used to identify general trends in cell factories performance. I introduce a computational method—OptSwap—to predict bioprocessing strain designs by identifying optimal modifications of the cofactor binding specificities of oxidoreductase enzyme and identifying complementary reaction knockouts. I also present an optimization procedure that identifies optimal cofactor- specificity “swaps” for improving theoretical yield in genome-scale metabolic models. Swapping the cofactor specificity of central metabolic enzymes is shown to increase NADPH production and increase theoretical yields for many native and non-native products. Last, the design step requires models that can successfully predict phenotype from genotype. I assess the predictive capabilities of existing models of E. coli through literature mining. I simulate strains from the literature in six historical genome-scale models of E. coli and report that the predictive power of the models has increased as they have expanded in size and scope. Together, these studies provide a path toward successfully applying systems biology methods to optimizing microbial cell factories.

      Cover page: Optimization of microbial cell factories with systems biology
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2018)

      Background

      Flux balance analysis (FBA) is a widely-used method for analyzing metabolic networks. However, most existing tools that implement FBA require downloading software and writing code. Furthermore, FBA generates predictions for metabolic networks with thousands of components, so meaningful changes in FBA solutions can be difficult to identify. These challenges make it difficult for beginners to learn how FBA works.

      Results

      To meet this need, we present Escher-FBA, a web application for interactive FBA simulations within a pathway visualization. Escher-FBA allows users to set flux bounds, knock out reactions, change objective functions, upload metabolic models, and generate high-quality figures without downloading software or writing code. We provide detailed instructions on how to use Escher-FBA to replicate several FBA simulations that generate real scientific hypotheses.

      Conclusions

      We designed Escher-FBA to be as intuitive as possible so that users can quickly and easily understand the core concepts of FBA. The web application can be accessed at https://sbrg.github.io/escher-fba .
        Cover page: Escher-FBA: a web application for interactive flux balance analysis
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2020)

        Background

        Stable isotope tracing has become an invaluable tool for probing the metabolism of biological systems. However, data analysis and visualization from metabolic tracing studies often involve multiple software packages and lack pathway architecture. A deep understanding of the metabolic contexts from such datasets is required for biological interpretation. Currently, there is no single software package that allows researchers to analyze and integrate stable isotope tracing data into annotated or custom-built metabolic networks.

        Results

        We built a standalone web-based software, Escher-Trace, for analyzing tracing data and communicating results. Escher-Trace allows users to upload baseline corrected mass spectrometer (MS) tracing data and correct for natural isotope abundance, generate publication quality graphs of metabolite labeling, and present data in the context of annotated metabolic pathways. Here we provide a detailed walk-through of how to incorporate and visualize 13C metabolic tracing data into the Escher-Trace platform.

        Conclusions

        Escher-Trace is an open-source software for analysis and interpretation of stable isotope tracing data and is available at https://escher-trace.github.io/ .
          Cover page: Escher-Trace: a web application for pathway-based visualization of stable isotope tracing data
          • Article
          • Peer Reviewed
          UC San Diego Previously Published Works (2018)
          As microbes face changing environments, they dynamically allocate macromolecular resources to produce a particular phenotypic state. Broad 'omics' data sets have revealed several interesting phenomena regarding how the proteome is allocated under differing conditions, but the functional consequences of these states and how they are achieved remain open questions. Various types of multi-scale mathematical models have been used to elucidate the genetic basis for systems-level adaptations. In this review, we outline several different strategies by which microbes accomplish resource allocation and detail how mathematical models have aided in our understanding of these processes. Ultimately, such modeling efforts have helped elucidate the principles of proteome allocation and hold promise for further discovery.
            Cover page: Modeling the multi-scale mechanisms of macromolecular resource allocation
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2019)
            Experimental studies of Escherichia coli K-12 MG1655 often implicate poorly annotated genes in cellular phenotypes. However, we lack a systematic understanding of these genes. How many are there? What information is available for them? And what features do they share that could explain the gap in our understanding? Efforts to build predictive, whole-cell models of E. coli inevitably face this knowledge gap. We approached these questions systematically by assembling annotations from the knowledge bases EcoCyc, EcoGene, UniProt and RegulonDB. We identified the genes that lack experimental evidence of function (the 'y-ome') which include 1600 of 4623 unique genes (34.6%), of which 111 have absolutely no evidence of function. An additional 220 genes (4.7%) are pseudogenes or phantom genes. y-ome genes tend to have lower expression levels and are enriched in the termination region of the E. coli chromosome. Where evidence is available for y-ome genes, it most often points to them being membrane proteins and transporters. We resolve the misconception that a gene in E. coli whose primary name starts with 'y' is unannotated, and we discuss the value of the y-ome for systematic improvement of E. coli knowledge bases and its extension to other organisms.
              Cover page: The y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function
              • Article
              • Peer Reviewed
              UC San Diego Previously Published Works (2018)
              Conversion of renewable biomass to useful molecules in microbial cell factories can be approached in a rational and systematic manner using constraint-based reconstruction and analysis. Filtering for high confidence in silico designs is critical because in vivo construction and testing of strains is expensive and time consuming. As such, a workflow was devised to analyze the robustness of growth-coupled production when considering the biosynthetic costs of the proteome and variability in enzyme kinetic parameters using a genome-scale model of metabolism and gene expression (ME-model). A collection of 2632 unfiltered knockout designs in Escherichia coli was evaluated by the workflow. A ME-model was used in the workflow to test the designs' growth-coupled production in addition to a less complex genome-scale metabolic model (M-model). The workflow identified 634 M-model growth-coupled designs which met the filtering criteria and 42 robust designs, which met growth-coupled production criteria using both M and ME-models. Knockouts were found to follow a pattern of controlling intermediate metabolite consumption such as pyruvate consumption and high flux subsystems such as glycolysis. Kinetic parameter sampling using the ME-model revealed how enzyme efficiency and pathway tradeoffs can affect growth-coupled production phenotypes.
                Cover page: Identification of growth-coupled production strains considering protein costs and kinetic variability
                • Article
                • Peer Reviewed
                UC San Diego Previously Published Works (2020)
                The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.
                  Cover page: BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree
                  • Article
                  • Peer Reviewed
                  UC San Diego Previously Published Works (2015)
                  Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)--in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools.
                    Cover page: Escher: A Web Application for Building, Sharing, and Embedding Data-Rich Visualizations of Biological Pathways.
                    • Article
                    • Peer Reviewed
                    UC San Diego Previously Published Works (2016)
                    Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repositories of high-quality models must be established, models must adhere to established standards and model components must be linked to relevant databases. Tools for model visualization further enhance their utility. To meet these needs, we present BiGG Models (http://bigg.ucsd.edu), a completely redesigned Biochemical, Genetic and Genomic knowledge base. BiGG Models contains more than 75 high-quality, manually-curated genome-scale metabolic models. On the website, users can browse, search and visualize models. BiGG Models connects genome-scale models to genome annotations and external databases. Reaction and metabolite identifiers have been standardized across models to conform to community standards and enable rapid comparison across models. Furthermore, BiGG Models provides a comprehensive application programming interface for accessing BiGG Models with modeling and analysis tools. As a resource for highly curated, standardized and accessible models of metabolism, BiGG Models will facilitate diverse systems biology studies and support knowledge-based analysis of diverse experimental data.
                      Cover page: BiGG Models: A platform for integrating, standardizing and sharing genome-scale models
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