AIMC Topic: Green Fluorescent Proteins

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Machine learning-assisted design of immunomodulatory lipid nanoparticles for delivery of mRNA to repolarize hyperactivated microglia.

Drug delivery Mar 3, 2025
Regulating inflammatory microglia presents a promising strategy for treating neurodegenerative and autoimmune disorders, yet effective therapeutic agents delivery to these cells remains a challenge. This study investigates modified lipid nanoparticle...
Cell Line Lipids Microglia Nanoparticles Liposomes Lipopolysaccharides Immunologic Factors Transfection Interleukin-10 Hyaluronic Acid Green Fluorescent Proteins Machine Learning Drug Delivery Systems Mice Animals RNA, Messenger
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Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences.

ACS synthetic biology Sep 4, 2024
N-terminal coding sequence (NCS) influences gene expression by impacting the translation initiation rate. The NCS optimization problem is to find an NCS that maximizes gene expression. The problem is important in genetic engineering. However, current...
Algorithms Deep Learning Bacillus subtilis Gene Expression Genetic Engineering Green Fluorescent Proteins Synthetic Biology
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Optimization of medium components for protein production by Escherichia coli with a high-throughput pipeline that uses a deep neural network.

Journal of bioscience and bioengineering Feb 1, 2024
To optimize rapidly the medium for green fluorescent protein expression by Escherichia coli with an introduced plasmid, pRSET/emGFP, a single-cycle optimization pipeline was applied. The pipeline included a deep neural network (DNN) and mathematical ...
Neural Networks, Computer Algorithms Escherichia coli Bayes Theorem Green Fluorescent Proteins
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Deep-qGFP: A Generalist Deep Learning Assisted Pipeline for Accurate Quantification of Green Fluorescent Protein Labeled Biological Samples in Microreactors.

Small methods Nov 27, 2023
Absolute quantification of biological samples provides precise numerical expression levels, enhancing accuracy, and performance for rare templates. Current methodologies, however, face challenges-flow cytometers are costly and complex, whereas fluore...
Green Fluorescent Proteins Sepharose Deep Learning Polymerase Chain Reaction Software
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Elucidating the Role of Hydrogen Bonding in the Optical Spectroscopy of the Solvated Green Fluorescent Protein Chromophore: Using Machine Learning to Establish the Importance of High-Level Electronic Structure.

The journal of physical chemistry letters Jul 17, 2023
Hydrogen bonding interactions with chromophores in chemical and biological environments play a key role in determining their electronic absorption and relaxation processes, which are manifested in their linear and multidimensional optical spectra. Fo...
Hydrogen Bonding Water Green Fluorescent Proteins Machine Learning Spectrum Analysis
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High throughput optimization of medium composition for Escherichia coli protein expression using deep learning and Bayesian optimization.

Journal of bioscience and bioengineering Dec 29, 2022
To improve synthetic media for protein expression in Escherichia coli, a strategy using deep neural networks (DNN) and Bayesian optimization was performed in this study. To obtain training data for a deep learning algorithm, E. coli harvesting a plas...
Green Fluorescent Proteins Deep Learning Bayes Theorem Escherichia coli Proteins Escherichia coli
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Lighting up protein design.

eLife May 19, 2022
Using a neural network to predict how green fluorescent proteins respond to genetic mutations illuminates properties that could help design new proteins.
Green Fluorescent Proteins Neural Networks, Computer Mutation
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Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions.

Nature communications Sep 1, 2021
Despite recent advances in high-throughput combinatorial mutagenesis assays, the number of labeled sequences available to predict molecular functions has remained small for the vastness of the sequence space combined with the ruggedness of many fitne...
Neural Networks, Computer Green Fluorescent Proteins Algorithms Bacteria
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Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation.

Nature communications Dec 7, 2020
The invariant development and transparent body of the nematode Caenorhabditis elegans enables complete delineation of cell lineages throughout development. Despite extensive studies of cell division, cell migration and cell fate differentiation, cell...
Green Fluorescent Proteins Cell Lineage Morphogenesis Caenorhabditis elegans Animals, Genetically Modified Software Embryo, Nonmammalian Microscopy, Fluorescence Animals Embryonic Development Deep Learning Computational Biology Cell Movement Imaging, Three-Dimensional Cell Differentiation
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Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks.

Biomolecules Jun 19, 2020
Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer...
Green Fluorescent Proteins Optical Imaging Tumor Cells, Cultured Deep Learning Lung Neoplasms Mice, Nude Neoplastic Stem Cells Female Image Processing, Computer-Assisted Mice Animals Mice, Inbred BALB C
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