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Related Concept Videos

Restarting Stalled Replication Forks02:37

Restarting Stalled Replication Forks

DNA replication is initiated at sites containing predefined DNA sequences known as origins of replication. DNA is unwound at these sites by the minichromosome maintenance (MCM) helicase and other factors such as Cdc45 and the associated GINS complex.The unwound single strands are protected by replication protein A (RPA) until DNA polymerase starts synthesizing DNA at the 5’ end of the strand in the same direction as the replication fork. To prevent the replication fork from falling apart, a...
Restarting Stalled Replication Forks02:37

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Replication in Eukaryotes02:31

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Replication in Eukaryotes01:29

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In eukaryotic cells, DNA replication is highly conserved and tightly regulated. Multiple linear chromosomes must be duplicated with high fidelity before cell division, so there are many proteins that fulfill specialized roles in the replication process. Replication occurs in three phases: initiation, elongation, and termination, and ends with two complete sets of chromosomes in the nucleus.
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Updated: Jun 23, 2026

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

Adaptive distributed replica-exchange simulations.

Andre Luckow1, Shantenu Jha, Joohyun Kim

  • 1Institute of Computer Science, Potsdam University, 14482 Potsdam, Germany.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|May 20, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces BigJob, a framework for efficiently using distributed computing resources for replica-exchange (RE) algorithms. It addresses challenges in parallel computing, enabling faster simulations by leveraging available infrastructure.

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A Cost Effective and Adaptable Scratch Migration Assay
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Last Updated: Jun 23, 2026

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

A Cost Effective and Adaptable Scratch Migration Assay
08:59

A Cost Effective and Adaptable Scratch Migration Assay

Published on: June 30, 2020

Area of Science:

  • Computational chemistry
  • High-performance computing
  • Parallel algorithms

Background:

  • Replica-exchange (RE) algorithms can significantly benefit from distributed computing resources due to their loosely coupled nature.
  • Current implementations face challenges in effectively utilizing multiple distributed resources and lack infrastructure independence.

Purpose of the Study:

  • To propose an extensible and scalable framework for opportunistic resource utilization in RE simulations.
  • To develop an infrastructure-independent mechanism for parallelizing RE algorithms.

Main Methods:

  • Developed a framework based on the Simple API for Grid Applications.
  • Introduced a new abstraction, BigJob, to manage replica scheduling and redistribution.
  • Analyzed RE algorithm requirements and production system challenges.

Main Results:

  • The BigJob framework provides a general-purpose mechanism for opportunistic resource use.
  • It enables adaptive redistribution and effective scheduling of replicas.
  • The approach is designed to be infrastructure-independent.

Conclusions:

  • The proposed BigJob framework offers a scalable and extensible solution for enhancing RE simulations.
  • It effectively addresses the challenges of distributed resource utilization in parallel computing.
  • This work facilitates more efficient and accessible large-scale simulations.