Jackie Valeri (COLLINS LAB)

Sequence-to-function Machine Learning for Biological Sequences

Toehold switches, which are programmable nucleic acid sensors, face a design bottleneck; our limited understanding of how sequence impacts function often necessitates time-consuming screens to identify effective sensors. We employ machine learning approaches such as convolutional neural networks and language models to characterize and optimize toehold sequences in silico. We then broaden our scope to other biological sequences and develop an automated machine learning platform that mitigates technical challenges such as model design choices.

Amy Xiao (ALM LAB)

Metrics to Relate SARS-CoV-2 Wastewater Data to Clinical Testing Dynamics

Wastewater surveillance has emerged as a useful tool to combat the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. We developed three new metrics to provide insight into the balance between disease spread and the public health response. These metrics could help further integrate wastewater surveillance into the pandemic response toolkit for COVID-19 and future pandemics.