Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Biot-Savart Law: Problem-Solving00:59

Biot-Savart Law: Problem-Solving

2.8K
The magnitude and direction of a magnetic field created by a steady current can be calculated using the Biot-Savart law.
Consider a mobile phone battery bank as a source of steady current, which flows through the wire connected between the two. What is the magnitude of the magnetic field created by this current at a field point P?
To estimate the magnitude of the total magnetic field, we first consider a small current element of length dl, at a distance r from the field point. Now the following...
2.8K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

729
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
729
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

98
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
98
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

11.4K
In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
11.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Structure-Guided Prioritization and Synthesis of New Ligands for GPR17 Receptor.

ACS omega·2026
Same author

Extracellular vesicles (EVs) in neurodevelopment: The emerging role of lipids.

Biochemical pharmacology·2026
Same author

Systems biology-based drug repurposing for neuroinflammation treatment in activated human microglia.

Scientific reports·2026
Same author

Mild Cognitive Impairment Associated with Obstructive Sleep Apnoea: A Pilot Study on Oxygen-Related Plasma Biomarkers and Network Analysis.

Molecular neurobiology·2026
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

Glucoerucin, Glucosinolate From Brassicaceae Vegetables, Improves the Metabolic Profile in a Murine Model of Diet-Induced Obesity.

Phytotherapy research : PTR·2026
Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026
Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Aug 26, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

637

Quantum computing algorithms: getting closer to critical problems in computational biology.

Laura Marchetti1, Riccardo Nifosì2, Pier Luigi Martelli3

  • 1University of Pisa, Department of Pharmacy, via Bonanno 6, 56126 Pisa Italy.

Briefings in Bioinformatics
|October 11, 2022
PubMed
Summary
This summary is machine-generated.

Quantum computing offers powerful new methods for analyzing massive biological datasets. This review explores quantum algorithms for multi-scale modeling and genomic analysis, highlighting potential advantages over classical approaches.

Keywords:
biomoleculesgenomicsmolecular modellingquantum algorithmsquantum machine learning

More Related Videos

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation
09:26

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

Published on: December 29, 2021

4.3K
A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.6K

Related Experiment Videos

Last Updated: Aug 26, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

637
DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation
09:26

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

Published on: December 29, 2021

4.3K
A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.6K

Area of Science:

  • Biocomputing and Computational Biology
  • Quantum Computing Applications in Life Sciences

Background:

  • Biotechnological advancements provide vast biological data, necessitating advanced computational tools.
  • Classical computational methods are used for biomolecule simulation and omics data analysis.
  • Machine learning is increasingly applied to biological data modeling and handling.

Conclusions:

  • Quantum computing holds significant potential to advance biocomputation, particularly in areas like multi-scale modeling and genomics.
  • Hybrid classical/quantum approaches represent a realistic pathway for near-term implementation.
  • Overcoming hardware limitations (qubit count, decoherence) is crucial for widespread QC adoption in life sciences.