AIMC Topic: Kinetics

Clear Filters Showing 41 to 50 of 311 articles

Machine learning-based biological process optimization for low molecular weight welan gum production.

International journal of biological macromolecules
This study focuses on optimizing the fermentation process for the production of low molecular weight welan gum (LMW-WG) using Sphingomonas sp. ATCC 31555 with glycerol as the sole carbon source. A series of single-factor experiments were conducted to...

Validity of recurrent neural networks to predict pedal forces and lower limb kinetics in cycling.

Journal of biomechanics
Dynamic variables contribute to understand the mechanics of pedalling and can assist with injury prevention. Measuring pedal forces and joint moments and powers has a high cost, which can be mitigated by using trained artificial neural networks (ANN)...

Physics-informed machine learning for automatic model reduction in chemical reaction networks.

Scientific reports
Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this synergy proves valuable, addressing the high computati...

CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters.

Nature communications
Estimation of enzymatic activities still heavily relies on experimental assays, which can be cost and time-intensive. We present CatPred, a deep learning framework for predicting in vitro enzyme kinetic parameters, including turnover numbers (k), Mic...

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...

Modeling and optimization of docosahexaenoic acid production by Schizochytrium sp. based on kinetic modeling and genetic algorithm optimized artificial neural network.

Bioresource technology
Docosahexaenoic acid (DHA), an essential ω-3 polyunsaturated fatty acid, is efficiently biosynthesized by Schizochytrium sp., yet its bioprocess optimization remains constrained by dynamic interdependencies between cultivation parameters and metaboli...

Deep learning-driven semi-rational design in phenylalanine ammonia-lyase for enhanced catalytic efficiency.

International journal of biological macromolecules
Phenylalanine ammonia-lyase (PAL) possesses significant potential in agriculture, industry, and the treatment of various diseases, including cancer. In particular, PAL derived from Anabaena variabilis (AvPAL) has been successfully utilized in clinica...

Co-pyrolysis kinetics and enhanced synergy for furfural residues and polyethylene using artificial neural network and fast heating.

Waste management (New York, N.Y.)
The efficient co-utilization of biomass and waste plastics is a key method to address the widely concerned environmental problem and replace traditional energy. Co-pyrolysis behaviors and synergistic effects of furfural residues (FR) and polyethylene...

Excited state kinetics of tryptophan and NAD(P)H in blood plasma of normal and abnormal liver conditions: A tool to understand the metabolic changes and classification.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Early diagnosis at the metabolomic level is crucial for the treatment of liver cirrhosis and hepatocellular carcinoma (HCC). In this study, attempts were made to investigate the excited-state kinetics of intrinsic fluorophores, tryptophan and nicotin...

Deep Learning and Single-Molecule Localization Microscopy Reveal Nanoscopic Dynamics of DNA Entanglement Loci.

ACS nano
Understanding molecular dynamics at the nanoscale remains challenging due to limitations in the temporal resolution of current imaging techniques. Deep learning integrated with Single-Molecule Localization Microscopy (SMLM) offers opportunities to pr...