AIMC Topic: Synthetic Biology

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Bio-informational futures: The convergence of artificial intelligence and synthetic biology.

EMBO reports
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
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
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
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
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
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
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
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 ...

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

ACS synthetic biology
The Design-Build-Test-Learn (DBTL) cycle, facilitated by exponentially improving capabilities in synthetic biology, is an increasingly adopted metabolic engineering framework that represents a more systematic and efficient approach to strain developm...

Standardizing Automated DNA Assembly: Best Practices, Metrics, and Protocols Using Robots.

SLAS technology
The advancement of synthetic biology requires the ability to create new DNA sequences to produce unique behaviors in biological systems. Automation is increasingly employed to carry out well-established assembly methods of DNA fragments in a multiple...