AIMC Topic: Synthetic Biology
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A machine learning Automated Recommendation Tool for synthetic biology.
Nature communications
Sep 25, 2020
Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long develop...
Sequencing enabling design and learning in synthetic biology.
Current opinion in chemical biology
Aug 6, 2020
The ability to read and quantify nucleic acids such as DNA and RNA using sequencing technologies has revolutionized our understanding of life. With the emergence of synthetic biology, these tools are now being put to work in new ways - enabling de no...
Bio-informational futures: The convergence of artificial intelligence and synthetic biology.
EMBO reports
Feb 11, 2020
Synthetic biology and artificial intelligence naturally converge in the biofoundry. Navigating the ethical and societal issues of the biofoundry's potential remains a major challenge.
Reinforcement Learning for Bioretrosynthesis.
ACS synthetic biology
Dec 30, 2019
Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are requi...
Function2Form Bridge-Toward synthetic protein holistic performance prediction.
Proteins
Oct 29, 2019
Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on indiv...
A Comprehensive Network Atlas Reveals That Turing Patterns Are Common but Not Robust.
Cell systems
Sep 18, 2019
Turing patterns (TPs) underlie many fundamental developmental processes, but they operate over narrow parameter ranges, raising the conundrum of how evolution can ever discover them. Here we explore TP design space to address this question and to dis...
Dynamic Metabolomics for Engineering Biology: Accelerating Learning Cycles for Bioproduction.
Trends in biotechnology
Aug 28, 2019
Metabolomics is a powerful tool to rationally guide the metabolic engineering of synthetic bioproduction pathways. Current reports indicate great potential to further develop metabolomics-directed synthetic bioproduction. Advanced mass metabolomics m...
Metabolic perceptrons for neural computing in biological systems.
Nature communications
Aug 28, 2019
Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing usi...
Designing Eukaryotic Gene Expression Regulation Using Machine Learning.
Trends in biotechnology
Aug 17, 2019
Controlling the expression of genes is one of the key challenges of synthetic biology. Until recently fine-tuned control has been out of reach, particularly in eukaryotes owing to their complexity of gene regulation. With advances in machine learning...
A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.
Cell
Jun 6, 2019
Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over ...
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