Display Accessibility Tools

Accessibility Tools

Grayscale

Highlight Links

Change Contrast

Increase Text Size

Increase Letter Spacing

Dyslexia Friendly Font

Increase Cursor Size

Core Courses and Forum

image

HRT 841 Foundation in Computational Plant Science 

Semester(s) offered: Fall of every year

Credits: 3   

Description:

The course will synthesize concepts from computational and data science with biological principles.

The goals for this course are for students to:

  1. acquire foundational knowledge and skill sets in biological and data sciences,
  2. formulate hypothesis-driven questions stemming from grand challenges at the interface of biology and data sciences,
  3. synthesize the acquired skills to solve questions in collaborative teams, and
  4. communicate persuasively across disciplines with peers and the public. This is a project-based course taught using a flipped classroom approach.

See course syllabus.

List of major topics:

  • Coding with Python and R
  • Data visualization and statistics
  • Computational and mathematical modeling
  • Biochemistry, molecular, cellular and biology
  • Genetics and genomics
  • Ecology, evolution, phylogenetics
  • Organismal and developmental biology

CSS 844 Frontiers in Computational Plant Science

Semester(s) offered: Spring of every year 

Credits: 3   

Description:

A modular course focusing on interdisciplinary research topics interfacing computational and plant sciences. The topics include: molecular systems biology, phenomics, and understanding mechanisms underlying the connection between genotype and phenotype.

Course objectives:

  • Students will be able to initiate, manage, and complete interdisciplinary group projects in computational plant biology.
  • Students will learn and apply cutting-edge methods of big data analysis.
  • Students will also improve skills in project management, and particularly verbal and written communication across disciplines.

See course syllabus.

Outline of major topics:

  • Big data analysis, including omics and phenomic data
  • Statistical analysis of genotype/phenotype associations
  • Statistical inference and learning
  • Applied computational analysis
  • ​​​​​Supervised and unsupervised machine learning
  • Cross-disciplinary team communication and management

PLB 843 Forum in Computational Plant Science

[trainees are required to enroll twice to fulfill requirements]

Semester(s) offered: Fall and Spring

Credits: 1

Description: 

The forum is open to all students interested in the intersection between computational and plant sciences. All participants will give a short lighting talk to introduce themselves and their research project. Each participant will give a follow up 10-minute research talk on their project followed by 20 minutes of discussion.

The presentations should be broad and accessible, with an emphasis on research directions, interfacing biology and computation, and should highlight a research problem/idea related to the project.

Students will then work in groups to discuss research challenges and ideas related to each project.

The forum will also have three weeks of professional development workshops throughout the semester on diverse topics. Examples include: (1) interdisciplinary communication (2), publishing and peer review (3), and project/time management and organization.

Course objectives:

The broad goals of the IMPACTS forum are to encourage interactions between diverse trainees in the program, discuss challenges at the forefront of computational plant sciences, establish interdisciplinary communication, and professional development. At the end of the forum, the students should be able to:

  • Better verbally communicate with their peers across disciplines.
  • Better communicate their research with their peers across disciplines through presentations.
  • Provide feedback to their peers on alternative research approaches through discussion.
  • Gain professional development skills specified in the syllabus.

Outline of major topics:

Professional development topics include (vary from semester to semester) but not limited to:

  • Verbal communication via presentation
  • Verbal communication through group discussion
  • Written communication through joint proposal development
  • Publishing/peer review
  • Project/time management,
  • How not to write a paper
  • Communicating your science with the public
  • Organizing and planning meetings

[OPTIONal] Introduction to Computational Modeling (CMSE 801) [elective, recommended for Plant Science trainees]

Semester(s) offered: Fall of every year

Credits: 3   

Recommended Background:

One semester of introductory calculus

Description:

Introduction to computational modeling using a wide variety of application examples. Algorithmic thinking and model building, data visualization, numerical methods, all implemented as programs. Command line interfaces. Scientific software development techniques including modular programming, testing, and version control.