UNMBiophysicsFall2025

UNM Physics 480/581: Biological Physics

Course Day and Time

Monday and Wednesday at 15:30-16:45 in PAIS 1140

Class on August 18 and 20 will be held by Zoom only.

Instructor: Prof. Keith Lidke

Room 2218 PAIS
e-mail: klidke@unm.edu

Office Hours

TBD

Overview

This course covers topics related to simulation, analysis and interpretation of biophysical processes and related microscopy imaging techniques.

This course will use an unconventional format that involves collaboratively developing a next-generation LLM-based image/data analysis platform that combines the Julia programming language, Large Language Models (LLMs), and Model Context Protocol (MCP) servers. It will require students to gain proficiency with Git/GitHub and state-of-the-art agentic coding tools such as claude-code.

Learning Objectives

By the end of this course, students will be able to:

Implement and apply computational methods for analyzing biophysical data and microscopy images
Design and execute simulations of biophysical processes including diffusion, molecular interactions, and fluorophore photophysics.
Apply estimation theory and statistical methods to extract quantitative information from noisy microscopy data
Develop scientific software collaboratively using modern version control and development practices
Integrate Large Language Models and AI tools effectively into scientific computing workflows
Evaluate and apply appropriate image processing techniques for biological microscopy data
Critically assess the limitations and assumptions of different computational approaches in biophysics

The concepts will include:

Topics

Simulation of Biophysical Processes and Microscopy Systems

Monte Carlo Markov Chains and fluorophore photophysics
Diffusion and interaction of membrane proteins
Polymer models including worm-like chains
Microscope Point Spread Function calculations and engineering
Single molecule localization microscopy (SMLM) data
Detectors and noise models

Image and Data Analysis Techniques

Estimation theory, Fisher Information and Cramer-Rao Lower Bounds
Bayesian Methods
Basic image and data processing
Analysis of single molecule localization microscopy data
Analysis of single particle tracking data
Deconvolution techniques including Wiener filtering, Richardson-Lucy and Neural Networks
Image stitching and registration
Basic concepts of training and using Neural Networks for image analysis

Other topics may be covered based on student interest.

Format

The lectures will introduce the concepts and theory behind each topic.

The class will then collectively generate and test implementations of the analysis and simulations tools. These will be plugged into a next-generation LLM-based data/image analysis concept that is in development by the Lidke lab and will be available for use by the UNM community.

To build these tools, students will learn:

Working knowledge of the Julia programming language
Git/GitHub for software versioning and collaborative development
How, when and when not to use state-of-the-art agentic coding tools such as claude-code
How to build and use Model Context Protocol servers
Context engineering for LLMs

Prerequisites

There are no formal prerequisites, but knowldedge of the following concepts will be assumed and used throughout the class:

Programming fundamentals (language agnostic): functions, loops, conditionals, inheritance.
Calculus and differential equations
Linear Algebra
Basic statistics (mean, variance, probability distributions)
Basic optics
Fourier Transforms

Grading

The course can be taken for a letter grade or C/NC.

Students will be evaluated based on their contributions to our collaborative development effort. Evaluation will be based on a combination of instructor review, peer review, and an LLM review of contributions to the code base.

Evaluations and grade assessments will be conducted at 1/3, 2/3, and the end of the course, with the final evaluation determining course grades.

Requirements for each grade:

A - Has led the successful development of at least two larger analysis or simulation tools (scope to be negotiated with instructor). Has provided useful feedback to other projects continuously throughout the course.

B - Has led the successful development of at least one large and one small analysis or simulation tool (scope to be negotiated with instructor). Has provided useful feedback to other projects continuously throughout the course.

C/Credit - Has led the successful development of at least one small analysis or simulation tool (scope to be negotiated with instructor). Has provided useful feedback to other projects continuously throughout the course.