Eric Klavins

The paper uses experimental results from de novo protein designs to identify and resolve failure modes in the Rosetta energy function, improving its accuracy in predicting stable protein structures.

The paper introduces a programming language to formalize biological protocols and handle errors, enabling error analysis through probabilistic inference.

The paper designs modular yeast consortia for synthetic biology, showing that consortia dynamics can be predicted from individual strain models and optimized to achieve target behaviors.

This paper develops a model showing that small populations and modest fitness improvements can accelerate evolutionary hill climbs in parallel turbidostats.

This paper introduces YSA, a method to quantitatively characterize protein-protein interactions in a controlled environment using yeast mating efficiency.

The paper engineers a synthetic developmental gene network in E. coli that couples stochastic differentiation with programmed cell death to enable cooperative cellulose utilization, but also observes cheater phenotypes and models evolutionary escape.