Machine learning and deep learning in synthetic biology: Key architectures, applications, and challenges
MK Goshisht - ACS omega, 2024 - ACS Publications
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial
progress in synthetic biology in recent years. Biotechnological applications of biosystems …
progress in synthetic biology in recent years. Biotechnological applications of biosystems …
Deep learning concepts and applications for synthetic biology
Synthetic biology has a natural synergy with deep learning. It can be used to generate large
data sets to train models, for example by using DNA synthesis, and deep learning models …
data sets to train models, for example by using DNA synthesis, and deep learning models …
A synthetic protein-level neural network in mammalian cells
Artificial neural networks provide a powerful paradigm for nonbiological information
processing. To understand whether similar principles could enable computation within living …
processing. To understand whether similar principles could enable computation within living …
Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles
Engineered signaling networks can impart cells with new functionalities useful for directing
differentiation and actuating cellular therapies. For such applications, the engineered …
differentiation and actuating cellular therapies. For such applications, the engineered …
DNA input classification by a riboregulator-based cell-free perceptron
AJ van der Linden, PA Pieters, MW Bartelds… - ACS Synthetic …, 2022 - ACS Publications
The ability to recognize molecular patterns is essential for the continued survival of
biological organisms, allowing them to sense and respond to their immediate environment …
biological organisms, allowing them to sense and respond to their immediate environment …
Engineering sequestration-based biomolecular classifiers with shared resources
Constructing molecular classifiers that enable cells to recognize linear and nonlinear input
patterns would expand the biocomputational capabilities of engineered cells, thereby …
patterns would expand the biocomputational capabilities of engineered cells, thereby …
Learning by selective plasmid loss for intracellular synthetic classifiers
We propose a learning mechanism for intracellular synthetic genetic classifiers based on the
selective elimination (curing) of plasmids bearing parts of the classifier circuit. Our focus is …
selective elimination (curing) of plasmids bearing parts of the classifier circuit. Our focus is …
Non-Linear Classifiers for Wet-Neuromorphic Computing using Gene Regulatory Neural Network
Abstract The Gene Regulatory Network (GRN) of biological cells governs a number of key
functionalities that enable them to adapt and survive through different environmental …
functionalities that enable them to adapt and survive through different environmental …
Automatic Implementation of Neural Networks through Reaction Networks--Part I: Circuit Design and Convergence Analysis
Information processing relying on biochemical interactions in the cellular environment is
essential for biological organisms. The implementation of molecular computational systems …
essential for biological organisms. The implementation of molecular computational systems …
Neural networks built from enzymatic reactions can operate as linear and nonlinear classifiers
The engineering of molecular programs capable of processing patterns of multi-input
biomarkers holds great potential in applications ranging from in vitro diagnostics (eg, viral …
biomarkers holds great potential in applications ranging from in vitro diagnostics (eg, viral …