AIMC Topic: Cricetulus

Clear Filters Showing 11 to 20 of 32 articles

Rapid total sialic acid monitoring during cell culture process using a machine learning model based soft sensor.

Biotechnology progress
Total sialic acid content (TSA) in biotherapeutic proteins is often a critical quality attribute as it impacts the drug efficacy. Traditional wet chemical assays to quantify TSA in biotherapeutic proteins during cell culture typically takes several h...

Optimizing recombinant antibody fragment production: A comparison of artificial intelligence and statistical modeling.

Biotechnology and applied biochemistry
Maximizing the recombinant protein yield necessitates optimizing the production medium. This can be done using a variety of methods, including the conventional "one-factor-at-a-time" approach and more recent statistical and mathematical methods such ...

Insights into the transformation of natural organic matter during UV/peroxydisulfate treatment by FT-ICR MS and machine learning: Non-negligible formation of organosulfates.

Water research
Natural organic matter (NOM) is a major sink of radicals in advanced oxidation processes (AOPs) and understanding the transformation of NOM is important in water treatment. By using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR ...

Leveraging machine learning to dissect role of combinations of amino acids in modulating the effect of zinc on mammalian cell growth.

Biotechnology progress
Although the contributions of individual components of cell culture media are largely known, their combinatorial effects are far less understood. Experiments varying one component at a time cannot identify combinatorial effects, and analysis of the l...

Data-driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins.

Biotechnology and bioengineering
Recently, the advancement in process analytical technology and artificial intelligence (AI) has enabled the generation of enormous culture data sets from biomanufacturing processes that produce various recombinant therapeutic proteins (RTPs), such as...

Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations.

Scientific reports
Treatment of COVID-19 with a soluble version of ACE2 that binds to SARS-CoV-2 virions before they enter host cells is a promising approach, however it needs to be optimized and adapted to emerging viral variants. The computational workflow presented ...

Machine learning-enabled nanosafety assessment of multi-metallic alloy nanoparticles modified TiO system.

NanoImpact
Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials is crucial for rapid environmental and health risk assessment. However, existing structure-toxicity correlation models for such nanomaterials are only based on ...

Machine Learning Models Identify New Inhibitors for Human OATP1B1.

Molecular pharmaceutics
The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors o...

Codon Optimization Using a Recurrent Neural Network.

Journal of computational biology : a journal of computational molecular cell biology
Codon optimization of a DNA sequence can significantly increase efficiency of protein expression, reducing the cost to manufacture biologic pharmaceuticals. Although directed methods based on such factors as codon usage bias and GC nucleotide content...

Traceable impedance-based single-cell pipetting, from a research set-up to a robust and fast automated robot: DispenCell-S1.

SLAS technology
Single-cell isolation is a truly transformative tool for the understanding of biological systems. It allows single-cell molecular analyses and considers the heterogeneity of cell populations, which is of particular relevance for the diagnosis and tre...