Microbiome Science Training Track

  • M5155 Environmental Microbiology (Autumn, 3 credits)
    • This course covers a wide range of microbiology topics ranging from the microbiology of landfills, drinking water, waste water, and pesticides, in addition to tools, monitoring techniques, bioremediation, and applications to bioenergy.
    • Graduates with little exposure to microbial communities will benefit from this lecture-based conceptual overview.
  • M5161 Bioinformatics and Molecular Microbiology (Spring, 3 credits)
    • In this course, you will learn how the genome sequencing technology has revolutionized biology and provided a foundation for new developments in science and medicine. You will become familiar with computational tools that are necessary to analyze genomic data and you will find out what biological questions can be answered by genomic approaches. We will use prokaryotes as the main material for genomic studies, but the core principles that you will learn are also applicable to eukaryotes including humans.
    • This course provides generalizable and foundational informatics tools for gene- and genome-based bioinformatics for undergraduate and graduate students. It is a pre-requisite for the hands-on Microbiome Informatics course.
  • M6155 Topics in Microbiome Science (Spring, 3 credits)
    • This graduate-level reading course prepares students for intensively reading the primary literature, with a focus on understanding ‘microbiomes’ or microbial communities in the oceans, soils and humans. We will explore essential methods and concepts, and ongoing ‘unknowns’ in the field, while also establishing basic experimental design principles and helping set the stage for understanding the kinds of questions and methods that are unique to Microbiome Science and studying microbes in complex communities.
    • This course provides valuable and generalizable graduate-level training in studying microbiomes. It is a pre-requisite for the hands-on Microbiome Informatics courses.
  • M8161 Microbiome Informatics (Autumn, 3 credits)
    • This hands-on course provides the foundational skillsets needed to study microbiomes and microbes in complex communities. This course teaches trainees how to interpret and analyze community genomic datasets with an aim to develop skills in processing and organizing datasets, extracting the function, structure, and evolutionary history of genes in these datasets, and discerning community structure and ecological drivers in metagenomic data. It also introduces modern taxonomic approaches and other ‘omics data types including viral metagenomes,  metatranscriptomes, metaproteomes, metabolomes, and more. As large-scale datasets are increasingly available, graduates of this course will be ideally positioned to utilize such datasets to maximize diverse research endeavors where the ‘microbiome’ might play a key role, and in a way that is currently unique in the world due to the updated microbial content and inclusion of viruses.

Other Courses

  • ANIMSCI 5090 Gut Microbiology (Spring, 2 credits)
    • A study of the major microorganisms of the gastrointestinal tract of animals and humans, their microbial metabolism and functions, interactions with each other and with hosts, and impact on host nutrition and health. Prereq: Micrbio 4000.01 or 4000.02 or 4100, and Biochem 4511, and GPA 2.0 or above in Biochem and Micrbio coursework; or permission of instructor. Class Notes: Connects to Wooster students via Zoom.
  • BMI 8050.01 Applications of Machine Learning and Artificial Intelligence in Biomedical Informatics (Spring, 3 credits)
    • Artificial Intelligence (AI) and Machine Learning (ML) provides an unprecedented opportunity to accelerate and revolutionize human health and the pace of clinical and translational science. The purpose of this course is to train the next generation of the translational medical workforce by teaching them the primary ML and AI algorithms used in bioinformatics and computational biology. We will cover the theoretical underpinnings of the methodology along with an explanation of how to use practical implementations (in R or python) of how to apply the methods to real bioinformatics data sets. An important goal of the course is to introduce students to more advanced algorithms that are not covered in other classes in BMI. Examples include modern regression techniques (including ridge regression, lasso, and elastic nets), deep learning (CNN, RNN, GNN using TensorFlow), non-linear dimension reduction (including t-SNE and ISOMAP), directed and undirected graphical models, and association rules. It is noteworthy that the class will have a special emphasis on the fundamentals and applications of deep learning and provide a conceptual understanding of deep learning with a holistic view and latest developments in the field.  By the end of the course, students will have had practice applying all of these methods to actual data sets.
  • ENVENG 5120 Bioremediation (3 credits)
    • An overview of biotechnology methods for remediation of groundwater and soils. Overview of theory and bio-remediation component design. Includes a study of the role of key microbial groups capable of transforming common contaminants in subsurface media with a particular emphasis on molecular genetic biotechnology methods to identify and document their ecology and metabolic condition. Prereq: A course in Micrbio; or Grad standing; or permission of instructor. Not open to students with credit for CivilEn 818.
  • BSGP 7030 Introduction to Data Science in Biomedical Science Research (Summer, 2 credits)
    • We will meet every Tuesday and Thursday at 9:00-11:00 AM. It is expected that each student will work 3 h per week outside of class in order to pass. Each student enrolled in BSGP 7030 will participate in small group assignments focused on developing the necessary skills to apply data science to biomedical and biological research problems. To demonstrate these skills students will perform and independent project during the last 4 weeks of the class where they will build and test an end-to-end analysis pipeline.  Students are encouraged to build a pipeline related to their own research or they can opt to build an RNASeq analysis pipeline. Students are allowed to work in groups, share ideas and collaborate freely on ways to solve problems but the final graded project must demonstrate the students individual work.
  • M5270 / PHR 5270 Antibiotics and Microbial Natural Products (Spring, 3 credits)
    • Microorganisms represent the largest trove of genetic and metabolic diversity in the world. They are responsible for producing a vast array of chemically diverse natural product small molecules. The unique biological and chemical properties of many of these compounds have afforded many valuable applications throughout medicine, agriculture, and biotechnology. Most critically, microbial natural products represent the largest source of antibiotics in use today. Here, we cover the biology behind the chemistry of these molecules and their role in human medicine.
  • PHR 8194 Introduction to the Structure, Analyses and Interpretation of Genomic Data Studies (Spring, 2 credits)
    • There is a need to train biomedical researchers to undertake cutting-edge cellular experiments that apply genomic-based approaches coupled with computational and statistical analsyes to reveal novel biological understanding. Meeting this training need has several hurdles. Biomedical training frequently focuses on the historical competencies of molecular biology and biochemistry, whereas genomic analyses requires bioinformatic approaches that are often outside of training programs. Bioinformatic approaches leverages biological understanding, statistical insight and computational skills. PHR 8194 was designed to develop bioinformatic skills and abilities in graduate students who have either little or no previous experience in computational science and statistics
  • PLNTPTH 8300 Current Topics in Plant Pathology: Plant-associated microbiomes and their applications (Spring, 2 credits)
    • This course will provide an introduction to the study of associations between plants and microbial communities, from the perspective of beneficial interactions. The course will highlight aspects from both the microbial and plant host perspective, including diversity of plant-associated microbes, examples of beneficial plant microbe interactions, introduction to methods for microbiome research and plant components leading to microbiome establishment and function. These plant-microbial associations will be presented from a perspective of applications in agriculture.
  • PUBHEHS 7375 Quantitative Microbial Risk Analysis Modeling (Spring, 3 credits)
    • This course will outline the fundamental sciences and their application in microbial risk modeling. Students will engage in lectures and project-based learning culminating in a functioning microbial risk model.Prereq: Grad level Stat course, or permission of instructor.

Statistics Courses

  • MOLGEN 5650  Analysis and Interpretation of Biological Data (3 credits)
    • Methods of analyzing biological data including: sampling, descriptive statistics, distributions, analysis of variance, inference, regression, and correlation. Emphasizes practical applications of statistics in the biological sciences. Prereq: Math 1149 or 1150 (150) or equiv, and 10 semester cr hrs at the 3000-level (or 300 level in the quarter system) or above in Agricultural or Biological Sciences. Not open to students with credit for 650.
  • PUBHBIO 6210 Design and Analysis of Studies in Health Sciences I (3 credits, available in person and online)
    • Theory and application of basic statistical concepts for design of studies in health sciences, integrated with statistical software applications. Prereq: Grad standing in PubHlth, or enrollment in MS Pharmacology program, or permission of instructor. Not open to students with credit for 701.
  • PUBHBIO 6211 Design and Analysis of Studies in Health Sciences II (3 credits)
    • A second course in applied biostatistical methods with an emphasis on regression methods commonly used in the health sciences. The focus is on linear regression and ANOVA. Integrated with use of computer statistical packages. Prereq: A grade of B- or above in 6210 (701), or permission of instructor. Not open to students with credit for 702.
  • STAT 8810 Advanced Topics in Statistics I: Statistical inference in Network Data (Spring, 1 credit)
  • The course is intended to introduce the field of statistical inference in network data. The course will have a good mix of theory, methods and applications. The primary audience of the course is PhD students and senior Masters students in Statistics and Biostatistics, there will be elements which are of interest to students from other departments interested in research on network analysis.