AIMC Topic: Synthetic Biology

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Abstraction hierarchy to define biofoundry workflows and operations for interoperable synthetic biology research and applications.

Nature communications
Lack of standardization in biofoundries limits the scalability and efficiency of synthetic biology research. Here, we propose an abstraction hierarchy that organizes biofoundry activities into four interoperable levels: Project, Service/Capability, W...

Optimizing phage therapy with artificial intelligence: a perspective.

Frontiers in cellular and infection microbiology
Phage therapy is emerging as a promising strategy against the growing threat of antimicrobial resistance, yet phage and bacteria are incredibly diverse and idiosyncratic in their interactions with one another. Clinical applications of phage therapy o...

Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida.

Communications biology
Although synthetic biology can produce valuable chemicals in a renewable manner, its progress is still hindered by a lack of predictive capabilities. Media optimization is a critical, and often overlooked, process which is essential to obtain the tit...

Scaling Up Synthetic Cell Production Using Robotics and Machine Learning Toward Therapeutic Applications.

Advanced biology
Synthetic cells (SCs), developed through bottom-up synthetic biology, hold great potential for biomedical applications, with the promise of replacing malfunctioning natural cells and treating diseases with spatiotemporal control. Currently, most SC s...

Combining diffusion and transformer models for enhanced promoter synthesis and strength prediction in deep learning.

mSystems
UNLABELLED: In the field of synthetic biology, the engineering of synthetic promoters that outperform their natural counterparts is of paramount importance, which can optimize the expression of exogenous genes, enhance the efficiency of metabolic pat...

A novel interpretability framework for enzyme turnover number prediction boosted by pre-trained enzyme embeddings and adaptive gate network.

Methods (San Diego, Calif.)
It is a vital step to identify the enzyme turnover number (kcat) in synthetic biology and early-stage drug discovery. Recently, deep learning methods have achieved inspiring process to predict kcat with the development of multi-species enzyme-substra...

Engineering a New Generation of Gene Editors: Integrating Synthetic Biology and AI Innovations.

ACS synthetic biology
CRISPR-Cas technology has revolutionized biology by enabling precise DNA and RNA edits with ease. However, significant challenges remain for translating this technology into clinical applications. Traditional protein engineering methods, such as rati...

Machine learning for synthetic gene circuit engineering.

Current opinion in biotechnology
Synthetic biology leverages engineering principles to program biology with new functions for applications in medicine, energy, food, and the environment. A central aspect of synthetic biology is the creation of synthetic gene circuits - engineered bi...

AI-driven automated discovery tools reveal diverse behavioral competencies of biological networks.

eLife
Many applications in biomedicine and synthetic bioengineering rely on understanding, mapping, predicting, and controlling the complex behavior of chemical and genetic networks. The emerging field of diverse intelligence investigates the problem-solvi...

Synthetic biology and artificial intelligence in crop improvement.

Plant communications
Synthetic biology plays a pivotal role in improving crop traits and increasing bioproduction through the use of engineering principles that purposefully modify plants through "design, build, test, and learn" cycles, ultimately resulting in improved b...