In silico, in vitro, and in vivo machine learning in synthetic biology and metabolic engineering

Improved protein structure prediction using potentials from deep learning

(2020) Andrew W. Senior et al.   NATURE

Large scale active-learning-guided exploration for in vitro protein production optimization

(2020) Olivier Borkowski et al.   Nature Communications

A deep learning approach to programmable RNA switches

(2020) Nicolaas M. Angenent-Mari et al.   Nature Communications

Sequence-to-function deep learning frameworks for engineered riboregulators

(2020) Jacqueline A. Valeri et al.   Nature Communications

Lessons from Two Design–Build–Test–Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning

(2019) Paul Opgenorth et al.   ACS Synthetic Biology

Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers

(2019) Jae Yong Ryu et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Plug-and-play metabolic transducers expand the chemical detection space of cell-free biosensors

(2019) Peter L. Voyvodic et al.   Nature Communications

DEEPred: Automated Protein Function Prediction with Multi-task Feed-forward Deep Neural Networks

(2019) Ahmet Sureyya Rifaioglu et al.   Scientific Reports

Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation

(2019) Pablo Carbonell et al.   ACS Synthetic Biology

A robotic platform for flow synthesis of organic compounds informed by AI planning

(2019) Connor W. Coley et al.   SCIENCE

Metabolic perceptrons for neural computing in biological systems

(2019) Amir Pandi et al.   Nature Communications

Towards a fully automated algorithm driven platform for biosystems design

(2019) Mohammad HamediRad et al.   Nature Communications

Reinforcement learning for batch bioprocess optimization

(2019) P. Petsagkourakis et al.   COMPUTERS & CHEMICAL ENGINEERING

Reinforcement Learning for Bioretrosynthesis

(2019) Mathilde Koch et al.   ACS Synthetic Biology

RetroPath2.0: A retrosynthesis workflow for metabolic engineers

(2018) Baudoin Delépine et al.   METABOLIC ENGINEERING

MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae

(2018) Yikang Zhou et al.   METABOLIC ENGINEERING

Computational Protein Design with Deep Learning Neural Networks

(2018) Jingxue Wang et al.   Scientific Reports

Control of a bioreactor using a new partially supervised reinforcement learning algorithm

(2018) B. Jaganatha Pandian et al.   JOURNAL OF PROCESS CONTROL

Cellular checkpoint control using programmable sequential logic

(2018) Lauren B. Andrews et al.   SCIENCE

Deep learning to predict the lab-of-origin of engineered DNA

(2018) Alec A. K. Nielsen et al.   Nature Communications

Machine Learning of Designed Translational Control Allows Predictive Pathway Optimization in Escherichia coli

(2018) Adrian J. Jervis et al.   ACS Synthetic Biology

Predicting novel substrates for enzymes with minimal experimental effort with active learning

(2017) Dante A. Pertusi et al.   METABOLIC ENGINEERING

Engineering Cellular Metabolism

(2016) Jens Nielsen et al.   CELL

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

Genetic circuit design automation

(2016) A. A. K. Nielsen et al.   SCIENCE

Semisupervised Gaussian Process for Automated Enzyme Search

(2016) Joseph Mellor et al.   ACS Synthetic Biology

LigandRFs: random forest ensemble to identify ligand-binding residues from sequence information alone

(2014) Peng Chen et al.   BMC BIOINFORMATICS

Toehold Switches: De-Novo-Designed Regulators of Gene Expression

(2014) Alexander A. Green et al.   CELL

The artificial neural network approach based on uniform design to optimize the fed-batch fermentation condition: application to the production of iturin A

(2014) Wenjing Peng et al.   Microbial Cell Factories

Genomically encoded analog memory with precise in vivo DNA writing in living cell populations

(2014) F. Farzadfard et al.   SCIENCE

Amplifying Genetic Logic Gates

(2013) J. Bonnet et al.   SCIENCE

Analysis of multiple compound-protein interactions reveals novel bioactive molecules

(2011) H. Yabuuchi et al.   Molecular Systems Biology

Neural network computation with DNA strand displacement cascades

(2011) Lulu Qian et al.   NATURE

Molecular signatures-based prediction of enzyme promiscuity

(2010) Pablo Carbonell et al.   BIOINFORMATICS

Predictive Behavior Within Microbial Genetic Networks

(2008) I. Tagkopoulos et al.   SCIENCE