AIMC Topic: Biophysical Phenomena

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Neural Network for Principle of Least Action.

Journal of chemical information and modeling Jul 5, 2022
The principle of least action is the cornerstone of classical mechanics, theory of relativity, quantum mechanics, and thermodynamics. Here, we describe how a neural network (NN) learns to find the trajectory for a Lennard-Jones (LJ) system that maint...
Neural Networks, Computer Molecular Dynamics Simulation Biophysical Phenomena Thermodynamics
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Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin.

PLoS computational biology Nov 29, 2021
Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painfu...
Biophysical Phenomena Lab-On-A-Chip Devices Laminin Protein Multimerization Humans Anemia, Sickle Cell Neural Networks, Computer High-Throughput Screening Assays Diagnosis, Computer-Assisted Image Interpretation, Computer-Assisted Computational Biology Microfluidics Deep Learning Erythrocytes, Abnormal In Vitro Techniques Erythrocyte Deformability Hemoglobin, Sickle
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Davis Computational Spectroscopy Workflow-From Structure to Spectra.

Journal of chemical information and modeling Aug 27, 2021
We describe an automated workflow that connects a series of atomic simulation tools to investigate the relationship between atomic structure, lattice dynamics, materials properties, and inelastic neutron scattering (INS) spectra. Starting from the at...
Computer Simulation Workflow Spectrum Analysis Biophysical Phenomena Machine Learning
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A strategy for mapping biophysical to abstract neuronal network models applied to primary visual cortex.

PLoS computational biology Aug 16, 2021
A fundamental challenge for the theoretical study of neuronal networks is to make the link between complex biophysical models based directly on experimental data, to progressively simpler mathematical models that allow the derivation of general opera...
Animals Computational Biology Models, Neurological Brain Mapping Humans Neural Networks, Computer Computer Simulation Biophysical Phenomena Nerve Net Visual Cortex Electrophysiological Phenomena Kinetics Neurons Synapses
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Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.

PLoS computational biology Jul 23, 2021
Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that ...
Cell Shape Adaptation, Physiological Cadherins Models, Biological Vimentin Biophysical Phenomena Cell Adhesion Neoplasm Metastasis Extracellular Matrix Antigens, CD Cell Count Epithelial-Mesenchymal Transition Computational Biology Triple Negative Breast Neoplasms Cell Proliferation Humans Machine Learning Biomarkers, Tumor Cell Line, Tumor Female Tumor Microenvironment
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Cartilage structure increases swimming efficiency of underwater robots.

Scientific reports May 28, 2021
Underwater robots are useful for exploring valuable resources and marine life. Traditional underwater robots use screw propellers, which may be harmful to marine life. In contrast, robots that incorporate the swimming principles, morphologies, and so...
Animals Robotics Biomechanical Phenomena Skates, Fish Biophysical Phenomena Mechanical Phenomena Anisotropy Animal Fins Equipment Design Swimming Cartilage
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Learning precise spatiotemporal sequences via biophysically realistic learning rules in a modular, spiking network.

eLife Mar 18, 2021
Multiple brain regions are able to learn and express temporal sequences, and this functionality is an essential component of learning and memory. We propose a substrate for such representations via a network model that learns and recalls discrete seq...
Neural Networks, Computer Models, Neurological Action Potentials Neurons Biophysical Phenomena Learning Spatio-Temporal Analysis
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Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.

Nature methods Jan 6, 2020
In mammalian cells, much of signal transduction is mediated by weak protein-protein interactions between globular peptide-binding domains (PBDs) and unstructured peptidic motifs in partner proteins. The number and diversity of these PBDs (over 1,800 ...
src Homology Domains Signal Transduction Machine Learning Proteins Reproducibility of Results Biophysical Phenomena Humans Peptides Protein Binding
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Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media.

Journal of microscopy Dec 18, 2019
For many nanoparticle applications it is important to understand dispersion in liquids. For nanomedicinal and nanotoxicological research this is complicated by the often complex nature of the biological dispersant and ultimately this leads to severe ...
Workflow Ferric Compounds Automation Microscopy, Electron Image Processing, Computer-Assisted Nanoparticles Culture Media Biophysical Phenomena Dynamic Light Scattering Software Water Machine Learning
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Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.

Scientific reports Nov 15, 2019
Machine learning (ML) is ubiquitous in bioinformatics, due to its versatility. One of the most crucial aspects to consider while training a ML model is to carefully select the optimal feature encoding for the problem at hand. Biophysical propensity s...
Propensity Score Cysteine Oxidation-Reduction Biophysical Phenomena Amino Acids Solvents Sequence Analysis, Protein Machine Learning Computational Biology Proteins
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