SDS Seminar Series – Sarah Coleman, University of Texas at Austin

The Fall 2025 SDS Seminar Series kicks off on September 5th from 2:00 p.m. to 3:00 p.m. with Dr. Sarah Coleman (Postdoctoral Fellow, Department of Statistics and Data Sciences, University of Texas at Austin). This event is in-person in POB 6.304.

Title: A Linear Mixed Effects Model for Evaluating Synthetic Gene Circuits

Abstract: A significant advancement in synthetic biology is the development of synthetic gene circuits with predictive Boolean logic. However, there is no universally accepted statistical method to analyze the performance of these circuits. Basic tests fail to capture the predicted logic (OR, AND, etc.) and most studies neglect statistical analysis entirely. As synthetic gene circuits shift toward advanced applications, primarily in computing, biosensing, and human health, it is critical to standardize the statistical methods used to evaluate gate success. Here, we propose the application of a linear mixed effects model to analyze and quantify genetic Boolean logic gate performance. First, we analyze 144 published gates to identify “typical” behavior. Next, we propose a linear mixed effects model for gate evaluation, where the fixed effect describes the switch between the OFF and ON state and the random effect is the input group identity. We evaluate this model framework for "typical” gates and suggest best practices for researchers. Finally, we experimentally validate this model framework using a series of nested repressor OR gates with intentionally degraded performance.