Focus Area Courses

There are four required focus areas. One course must be taken to satisfy each area. The core is designed to provide breadth to otherwise very specialized training; therefore, the same course may not be counted as satisfying multiple areas.

Scientific Ethics

One of the following courses must be taken at any time during PULSe graduate training.

  • GRAD 61200 - Responsible Conduct in Research (1 credit) Lecture once per week for 50 minutes per meeting for 8 weeks. Offered: Fall, Spring. Overview of values, professional standards, and regulations that define responsible conduct in research. Students learn the values and standards of responsible research through readings and lecture/discussion and practice application of these values and standards to research situations through class discussion of case studies from life sciences research.
  • HORT 60100 - Planning and Presenting Horticulture Research (1 credit) Lecture once per week for 50 minutes per meeting for 16 weeks. Typically Offered Fall. Students will be familiarized with planning and presenting plant science and horticulture research. Written and oral presentations based on the students' proposed thesis topic will be evaluated. Class trips required. Students will pay individual lodging or meal expenses where necessary. Instructor permission required.

Scientific Communication

  • GRAD 60100 - PULSe Scientific Communications (1 credit/taken twice). Designed to develop the skills needed for effective scientific presentations. Students register for this course in the Fall and Spring semesters of Year 1 of study.

Proposal Writing

One of the following courses must be completed before the end of Year 2 of PULSe graduate training; however, the proposal writing class should not be taken during the first year.

  • HORT 60300 - Grants and Grantsmanship (1 credit). Lecture once per week for 50 minutes per meeting for 16 weeks. Offered Spring. Focuses on funding opportunities in agricultural research and techniques of writing successful scientific grant proposals. Students will write a proposal on a research topic of their choice during the course, and they will gain experience in the peer review process by preparing written reviews of proposals and participating in a panel meeting in which proposals are discussed and ranked.
  • MCMP 62500 - Grant Writing (1 credit). Offered Fall. Instructions for the preparation and submission of an NIH-style RO1 grant proposal will be covered. Each student will write and submit a complete proposal. The proposals will be student reviewed in a mock study section at the end of the course.

Analysis of Data

This requirement is designed to train students from a variety of backgrounds in methods of acquiring and/or analyzing data in any of the various disciplines within PULSe. As such, there is a menu of courses from which students (and TGs) can choose depending on the individual student or TG needs. These courses and their descriptions are listed below. Students must satisfy this requirement by the end of Year 2.  Students in the Biotechnology TG take an additional 9-12 Math/Statistics credit hour requirements.

  • BIOL 59500 - Methods and Measurements in Physical Biochemistry (3 credits) Lecture 3 times per week for 50 minutes per meeting for 16 weeks. Offered: Fall. Introduction to physical methods in biochemistry and physical measurements of biological systems, such as UV/Visspectroscopy, circular dichroism, IR and Raman spectroscopy, fluorescence, neutron diffraction, light scattering, scattering from ordered materials, x-ray crystallography, NMR and ESR spectroscopy, electron microscopy, mass spectroscopy. Application of these techniques to studies of structure and dynamic behavior of biological macromolecules, composition, and orientation of structural elements and cofactors, ligand binding and conformational change in biological interactions and detailed probes of local changes in structure, solvent accessibility and specific bonds formed in biological reactions. Interpretation of the resulting data and analysis of strengths and limitations of each technique. Examples from research articles are discussed that illustrate how these methods are used in modern biochemistry. Prerequisite: Introductory Calculus and Physics or permission of the instructor.
  • MCMP 51400 - Biomolecular Interactions: Theory and Practice (Four 1-credit modules) In order to fulfill this course requirement, PULSe students are required to take Module 1 and at least two other modules for a total of three credit hours. Offered: Spring. Theory and applications of biophysical and bioanalytical methods for the identification and quantification of biological and pharmaceutical samples. Methods to be discussed include chromatography, electrophoresis, optical spectroscopy, mass spectrometry, electrochemical methods, radiochemical analysis, ultracentrifugation, calorimetry and surface phasmon resonance. Physical measurements, such as binding equilibrium, kinetics and macromolecular structure will be discussed. Fundamentals of each technique will be discussed, with a major focus on the application and integration of presented methods for the analysis of biological problems. Prerequisite: MCMP 31000 or authorized equivalent courses or consent of instructor.
  • STAT 50300 - Statistical Methods for Biology (3 credits) Lecture 3 times per week for 50 minutes per meeting for 16 weeks. Offered: Fall, Spring. Introductory statistical methods, with emphasis on applications in biology. Topics include descriptive statistics, binomial and normal distributions, confidence interval estimation, hypothesis testing, analysis of variance, introduction to nonparametric testing, linear regression and correlation, goodness-of-fit tests, and contingency tables. Credit allowed in either 50300 or 51100 but not both. Prerequisite: Calculus.
  • STAT 51100 - Statistical Methods (3 credits) Lecture 3 times per week for 50 minutes per meeting for 16 weeks. Offered: Fall, Spring. Descriptive statistics; elementary probability; sampling distributions; inference, testing hypotheses, and estimation; normal, binomial, Poisson, hypergeometric distributions; one-way analysis of variance; contingency tables; regression. Credit allowed in either 503 or 511 but not both. Prerequisite: MA 16200 or authorized equivalent courses or consent of instructor.
  • STAT 51200 - Applied Regression Analysis (3 credits) Lecture 3 times per week for 50 minutes per meeting for 16 weeks. Offered: Fall, Spring. Inference in simple and multiple linear regression, residual analysis, transformations, polynomial regression, model building with real data, nonlinear regression. One-way and two-way analysis of variance, multiple comparisons, fixed and random factors, analysis of covariance. Use of existing statistical computer programs. Prerequisite: STAT 50300, STAT 51100, STAT 51700, or consent of instructor.
  • CS 59000 - Computing for Life Sciences (3 credits) Lecture 3 times per week for 50 minutes per meeting for 16 weeks.  Offered:  Fall.  Basic bioinformatics algorithms and Python programming.  Course topics include biological databases, algorithms for biological sequence (DNA, protein), sequence alignment and database search, sequence motif search, protein tertiary (3D) structure comparison, protein-protein interaction and comparative genomics.  This course is targeted at non-CS majors who are working or interested in the bioinformatics field.  No programming experience is required.
  • CS 66200 - Pattern Recognition and Decision-Making Processes (3 credits) Lecture 3 times per week for 50 minutes per meeting for 16 weeks. Offered: Spring. Introduction to the basic concepts and various approaches of pattern recognition and decision-making processes. Topics include various classifier designs, evaluation of classifiability, learning machines, feature extraction, and modeling. Prerequisite: ECE 30200 or authorized equivalent courses or consent of instructor.
  • CS 53000 -Introduction to Scientific Visualization (3 credits) Lecture 3 times per week for 50 minutes per meeting for 16 weeks. Offered: Spring. Teaches the fundamentals of scientific visualization and prepares students to apply these techniques in fields such as astronomy, biology, chemistry, engineering, and physics. Emphasis is on the presentation of scalar, vector, and tensor fields; data sampling and resampling; and reconstruction using multivariate finite elements (surfaces, volumes, and surfaces on surfaces). Prerequisite: CS 25100 or authorized equivalent courses or consent of instructor.
  • BIOL 59500/CS 59000 - Protein Bioinformatics (3 credits) Lecture 2 times per week for 75 minutes per meeting for 16 weeks. Offered: Spring. Algorithmic challenges in analyzing sequences (what genes encode an organism, and how are genes related across organisms), structures (what do the protein constructed for these genes look like, and what does that imply about their functions), interactions (how are proteins helping and hindering each other in complex networks), and the underlying experimental data. The computational techniques applied include dynamic programming, graph search, hidden Markov models, clustering, optimization, and simulation. Computer Science Department registration approval is required.
  • BIOL 59500 - Practical Biocomputing (3 credits) Offered: Spring. Computational skills are necessary for a career in modern science. Electronic resources and high-throughput technologies are transforming biology; becoming a “power user” of these resources is essential. Unfortunately, such resources are often incomplete (requiring various sources to be combined), massive (making it difficult to find the specific information one is seeking), or in the wrong format (making them incompatible with other software). While not every scientist needs to develop their own applications, dealing with big data and modern approaches to data analysis frequently requires the use of scripts to manipulate large datasets. Practical Biocomputing is not a "bioinformatics" course per se, although many of the examples are drawn from this field – The course is intended to give you the skill and experience needed to acquire and manipulate essential data, including: How to automate analyses, how to transform data between formats needed for software packages, how to use scripts to find and retrieve data from electronic sources, how to create internet robots to automatically retrieve and integrate data, basic principles of relational databases and how to integrate database analyses with calculations, and how to create and manage pipelines and workflow for running complex series of software.

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