Michaël Moret

Genetic code expansion enables programmable covalent protein design

This paper introduces AminoX, a platform for programmable covalent protein design using non-standard amino acids, enabling efficient screening and functional validation of covalent warheads and multifunctional proteins.

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

This paper introduces RENAISSANCE, a generative machine learning framework that accurately characterizes intracellular metabolic states by parameterizing kinetic models using omics data and experimental observations.

Preventing escape and malfunction of recoded cells due to tRNA base changes

The paper shows recoded E. coli can escape faster due to tRNA base changes and introduces kill switches to prevent this.

Machine Learning and Directed Evolution of Base Editing Enzymes

This work designs precise base editors with reduced bystander editing using guide RNA libraries and protein language models, improving safety and targeting efficiency.

Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks

This paper introduces REKINDLE, a deep learning framework that generates kinetic models of metabolic networks with dynamic properties, demonstrating its efficiency and versatility through three studies.

Genetic design automation for autonomous formation of multicellular shapes from a single cell progenitor

The paper introduces a computational method to design genetic circuits that control the formation of multicellular shapes from a single cell in human cells.

Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties

This paper introduces iSCHRUNK, a method combining Monte Carlo sampling and machine learning to reduce uncertainty in kinetic models, identifying key parameters for desired metabolic behavior.

Automated Gleason grading of prostate cancer tissue microarrays via deep learning

This study develops a deep learning model to automate Gleason grading of prostate cancer tissue microarrays, achieving agreement with pathologists and expert-level patient stratification.