Modeling and measuring how codon usage modulates the relationship between burden and yield during protein overexpression in bacteria.

Excess utilization of translational resources is a critical source of burden on cells engineered to over-express exogenous proteins. To improve protein yields and genetic stability, researchers often use codon optimization strategies that improve translational efficiency by matching an exogenous gene's codon usage with that of the host organism's highly expressed genes. Despite empirical data that shows the benefits of codon optimization, little is known quantitatively about the relationship between codon usage bias and the burden imposed by protein overexpression.

Scaling Down for Efficiency: Medium-Sized Transformer Models for Protein Sequence Transfer Learning.

Protein language models such as the transformer-based Evolutionary Scale Modeling 2 (ESM2) can offer deep insights into evolutionary and structural properties of proteins. While larger models, such as ESM2 15B, promise to capture more complex patterns in sequence space, they also present practical challenges due to their high dimensionality and high computational cost. We systematically evaluated the performance of all ESM2 models across many biological datasets to determine the impact of model size on transfer learning.

Single-Cell Virology: On-Chip, Quantitative Characterization of the Dynamics of Virus Spread from One Single Cell to Another.

Virus spread at the single-cell level is largely uncharacterized. We have designed and constructed a microfluidic device in which each nanowell contains a single, infected cell (donor) and a single, uninfected cell (recipient). Using a GFP-expressing poliovirus as our model, we observed both lytic and non-lytic spread.