Nathan Braniff

NLoed provides a Python package for nonlinear optimal experimental design in systems biology, supporting diverse models and data types with efficient computation.

This paper applies reinforcement learning to optimal experimental design in biology, showing it outperforms existing methods in bacterial growth parameter inference under parametric uncertainty.

The paper shows that ppGpp in E. coli inversely responds to translational elongation rate, enabling growth rate perception and control through a regulatory circuit.