Development of an AI-Coded Size Exclusion Chromatography Modeling Application for Biochemistry Research and Instruction
Type of Presentation
Poster Session
Location
University Library
Start Date
4-10-2026 11:30 AM
End Date
4-10-2026 12:45 PM
Abstract
Size exclusion chromatography (SEC) is a foundational technique in biochemistry and molecular biology, yet there remain limited easy-to-use computer modeling tools for students and early-stage researchers. To address this gap, we developed an interactive, browser-based SEC modeling tool using AI-assisted coding (Claude, Anthropic), enabling both research and classroom instruction without specialized software or hardware requirements and running on any operating system. The simulator was constructed iteratively using a large language model-assisted, naturally speaking prompts, and is freely accessible as a web application through the Henne Lab at Governors State University. This tool allows users to change a comprehensive set of experimentally relevant parameters including: flow rate (mL/min), total column volume (Vt), void volume (V₀), column efficiency expressed as theoretical plates (N), and adjustable time range for chromatogram display. Protein parameters include molecular weight (1,000–500,000 Da), molar extinction coefficient (M⁻¹cm⁻¹), and sample concentration (mM). Separation is based on the partition coefficient (Kav), and peak detection is modeled using the Beer-Lambert Law (A = εlc), producing realistic Gaussian peak profiles. Default proteins include IgG (150 kDa), BSA (66 kDa), and lysozyme (14.3 kDa), with the option to add custom proteins and extinction coefficients. This tool has been successfully integrated into multiple undergraduate and graduate biochemistry courses to reinforce theoretical concepts and demonstrate the utility of AI-assisted development in rapidly producing laboratory scientific software.
Faculty / Staff Sponsor
Walter Henne
Development of an AI-Coded Size Exclusion Chromatography Modeling Application for Biochemistry Research and Instruction
University Library
Size exclusion chromatography (SEC) is a foundational technique in biochemistry and molecular biology, yet there remain limited easy-to-use computer modeling tools for students and early-stage researchers. To address this gap, we developed an interactive, browser-based SEC modeling tool using AI-assisted coding (Claude, Anthropic), enabling both research and classroom instruction without specialized software or hardware requirements and running on any operating system. The simulator was constructed iteratively using a large language model-assisted, naturally speaking prompts, and is freely accessible as a web application through the Henne Lab at Governors State University. This tool allows users to change a comprehensive set of experimentally relevant parameters including: flow rate (mL/min), total column volume (Vt), void volume (V₀), column efficiency expressed as theoretical plates (N), and adjustable time range for chromatogram display. Protein parameters include molecular weight (1,000–500,000 Da), molar extinction coefficient (M⁻¹cm⁻¹), and sample concentration (mM). Separation is based on the partition coefficient (Kav), and peak detection is modeled using the Beer-Lambert Law (A = εlc), producing realistic Gaussian peak profiles. Default proteins include IgG (150 kDa), BSA (66 kDa), and lysozyme (14.3 kDa), with the option to add custom proteins and extinction coefficients. This tool has been successfully integrated into multiple undergraduate and graduate biochemistry courses to reinforce theoretical concepts and demonstrate the utility of AI-assisted development in rapidly producing laboratory scientific software.