Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (5 results)

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

Computational methods play an irreplaceable role for optimization of biological organisms in the era of high-resolution omics, genetic engineering, and high-performance computing. A general overview of computational methods for optimization of biological organisms is presented in Chapter 1 with a focus on three main challenges relating to data scarcity and heterogeneity, model interpretability, and the large number of factors that can affect an organisms’ phenotype. Recent advances are discussed in Chapter 2 with a forward-looking view on the application of computational methods for microbiome-based diet and health optimization. In Chapter 3, existing computational methods are applied for microbiome-based diet optimization in irritable bowel syndrome (IBS). The integrated data analysis results argue that there are two types of patients distinguishable by their fecal samples, those with high colonic methane and SCFA production, who will respond well on a low-FODMAP diet, and all others, who would benefit a dietary supplementation containing butyrate and propionate, as well as probiotics with SCFA-producing bacteria, such as lactobacillus. In Chapter 4, a novel artificial neural network (ANN) architecture called genetic neural network (GNN) is presented that captures the dependencies and non-linear dynamics that exist in gene networks into the GNN architecture. The results argue for 40% more accuracy of GNNs compared to several common ANNs in predicting genome-wide gene expression given gene knockouts and master regulator perturbations in bacterium E. coli. In Chapter 5, a novel group testing method called algorithmic lifestyle optimization (ALO) is presented for rapid identification of effective lifestyle interventions in individuals. ALO is robust to noise, data size and data heterogeneity, is between 58.9% and 68.4% more efficient compared to standard elimination diet for identification of food items that exacerbate IBS symptoms and allergic reactions, and better than alternative state of the art group testing method for this application. The conclusions and future directions are discussed at the end of each chapter and summarized in the final chapter. Chapters 2, 3 and 4 are published (1–3).

    Cover page: Computational Methods for Optimization of Biological Organisms
    • Article
    • Peer Reviewed
    UC Davis Previously Published Works (2022)
    OBJECTIVE: A hallmark of personalized medicine and nutrition is to identify effective treatment plans at the individual level. Lifestyle interventions (LIs), from diet to exercise, can have a significant effect over time, especially in the case of food intolerances and allergies. The large set of candidate interventions, make it difficult to evaluate which intervention plan would be more favorable for any given individual. In this study, we aimed to develop a method for rapid identification of favorable LIs for a given individual. MATERIALS AND METHODS: We have developed a method, algorithmic lifestyle optimization (ALO), for rapid identification of effective LIs. At its core, a group testing algorithm identifies the effectiveness of each intervention efficiently, within the context of its pertinent group. RESULTS: Evaluations on synthetic and real data show that ALO is robust to noise, data size, and data heterogeneity. Compared to the standard of practice techniques, such as the standard elimination diet (SED), it identifies the effective LIs 58.9%-68.4% faster when used to discover an individuals food intolerances and allergies to 19-56 foods. DISCUSSION: ALO achieves its superior performance by: (1) grouping multiple LIs together optimally from prior statistics, and (2) adapting the groupings of LIs from the individuals subsequent responses. Future extensions to ALO should enable incorporating nutritional constraints. CONCLUSION: ALO provides a new approach for the discovery of effective interventions in nutrition and medicine, leading to better intervention plans faster and with less inconvenience to the patient compared to SED.
      Cover page: Algorithmic lifestyle optimization.
      • Article
      • Peer Reviewed
      UC Davis Previously Published Works (2017)
      Protein inference, the identification of the protein set that is the origin of a given peptide profile, is a fundamental challenge in proteomics. We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile. In its core, DeepPep quantifies the change in probabilistic score of peptide-spectrum matches in the presence or absence of a specific protein, hence selecting as candidate proteins with the largest impact to the peptide profile. Application of the method across datasets argues for its competitive predictive ability (AUC of 0.80±0.18, AUPR of 0.84±0.28) in inferring proteins without need of peptide detectability on which the most competitive methods rely. We find that the convolutional neural network architecture outperforms the traditional artificial neural network architectures without convolution layers in protein inference. We expect that similar deep learning architectures that allow learning nonlinear patterns can be further extended to problems in metagenome profiling and cell type inference. The source code of DeepPep and the benchmark datasets used in this study are available at https://deeppep.github.io/DeepPep/.
        Cover page: DeepPep: Deep proteome inference from peptide profiles
        • Article
        • Peer Reviewed
        UC Davis Previously Published Works (2020)
        Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge.
          Cover page: The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health
          • Article
          • Peer Reviewed
          UC Davis Previously Published Works (2020)
          How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli's populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data. Analysis of the proposed experiments shows that broad exploration of the experimental space followed by fine-tuning emerges as the optimal strategy. Additionally, analysis of the experimental data reveals 29 cases of cross-stress protection and 4 cases of cross-stress vulnerability. Further validation reveals the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to guide omics data collection for training predictive models, making evidence-driven decisions and accelerating knowledge discovery in life sciences.
            Cover page: Accelerated knowledge discovery from omics data by optimal experimental design
            Top