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COMP_SCI 396, 496: Data-driven Plant Science


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Prerequisites

No prerequisite

Description

Plants generate signals across many scales—from molecular activity to whole-plant physiology—that can now be measured, analyzed, and modeled with computational tools. This interdisciplinary course introduces students to data-driven approaches in plant science, bridging experimental biology and computational analysis.  

Students will integrate Arduino microcontroller-based IoT sensors, computational photography, chromatography, and novel bioelectronic sensors with computational pipelines for comprehensive data analysis. The curriculum covers RNA sequencing and Linux-based bioinformatics alongside machine learning methods for interpreting complex biological datasets. Through weekly lectures and hands-on laboratories, students learn to design experiments, collect and visualize sensor data, extract and process RNA, and apply computational methods to analyze results. 

The course emphasizes interdisciplinary collaboration between engineering and biology students, with lab reports developing technical and scientific communication skills. Instead of a final exam, students complete a team project integrating hardware, biology, and computation, presenting their work during the final week.

Advanced undergraduates and graduate students from all colleges and disciplines are welcome to enroll. The skills learned in the class can be applied to careers in agriculture, plant science, biomedicine, Machine Learning or Systems engineering, and beyond.

  • This course fulfills the Technical Elective area.
  • This course cross-list with COMP_ENG 395_495.

Prerequisites: No prerequisite. Basic courses like CS 110, CE 203 or basic biology (BIO 103, 201) courses would be helpful but not needed.

REFERENCE TEXTBOOKS: N/A
REQUIRED TEXTBOOK: N/A
RECOMMENDED TEXTBOOK: N/A

Course Coordinator: Prof. Arora 
Course Instructor:
Prof. Nivedita Arora, Assistant Professor, ECE and CS department, McCormick School of Engineering
Prof. Susan Strickler, Susan Strickler, Adjunct Associate Professor, Plant Biology and Conservation Program

Course Objectives:
Technical Skills:

  • Design and deploy Arduino-based IoT sensors and bioelectronic tools for plant data collection
  • Learn about different types of environments (temp, moisture), electrochemical and remote hyperspectral vision sensing techniques
  • Basic Signal Processing
  • Extract and process RNA samples for sequencing analysis
  • Execute Linux-based bioinformatics workflows and apply machine learning to biological datasets

Analytical Skills:

  • Analyze RNA sequencing data using computational pipelines
  • Visualize and interpret multi-scale plant data (molecular to physiological)
  • Implement best practices for data and code management Communication and Collaboration:
  • Design experiments integrating hardware, biology, and computation
  • Communicate technical findings through lab reports and presentations
  • Collaborate effectively in interdisciplinary engineering-biology teams Applied Integration:
  • Synthesize experimental, biological, and computational approaches in team research projects
  • Present and demonstrate integrated plant science solutions

Course Topics:

  • Arduino-based IoT and bioelectronic sensors for plant monitoring
  • Chromatography techniques
  • RNA extraction, processing, and sequencing workflows
  • Linux command line and bioinformatics pipelines
  • Data visualization and machine learning applications
  • Code and data management best practices
  • Multi-scale plant signal analysis (molecular to physiological)
  • Hardware-biology-computation integration
  • Data-driven experimental design
Grades:
Class participation: 10%
Individual Writing Assignment: 10%
Group Labs/Activities: 40%
Projects (Proposal, video presentation, report, demo): 40%