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Lagging Strand Synthesis

Lagging Strand Synthesis

During replication, the complementary strands in double-stranded DNA are synthesized at different rates. Replication first begins on the leading strand. Replication starts later, occurs more slowly, and proceeds discontinuously on the lagging strand.
There are several major differences between synthesis of the leading strand and synthesis of the lagging strand. 1) Leading strand synthesis happens in the direction of replication fork opening, whereas lagging strand synthesis happens in the...

Lagging Strand Synthesis

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During replication, the complementary strands in double-stranded DNA are synthesized at different rates. Replication first begins on the leading strand. Replication starts later, occurs more slowly, and proceeds discontinuously on the lagging strand.
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The Spindle Assembly Checkpoint

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The spindle assembly checkpoint is a molecular surveillance mechanism ensuring the fidelity of chromosome segregation during anaphase. The checkpoint monitors the completion of all the prerequisite steps before chromosome segregation to determine whether the segregation process should proceed or be delayed.
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Conservative Site-specific Recombination and Phase Variation

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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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Long-term Potentiation

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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.

Long-term Potentiation

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Related Experiment Video

Updated: May 9, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Spike-timing-dependent construction.

Toby Lightheart¹, Steven Grainger, Tien-Fu Lu

¹School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia. toby.lightheart@adelaide.edu.au

Neural Computation

|July 31, 2013

Summary

Spike-timing-dependent construction (STDC) creates new neurons and connections in neural networks, overcoming limitations of traditional plasticity models. This framework enables adaptive structural changes for complex, real-world applications.

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Area of Science:

Computational Neuroscience
Artificial Intelligence

Background:

Spike-timing-dependent plasticity (STDP) has driven research in spiking neural networks (SNNs).
Network topology limits learning solely through connection weight modification.

Purpose of the Study:

To propose a framework for novel constructive SNNs.
To model synaptic plasticity as network construction.
To address limitations in computational power for SNN adaptation.

Main Methods:

Developed a conceptual analogy for synaptic plasticity as network construction.
Generalized existing constructive algorithms.
Created a framework for designing constructive SNNs.

Main Results:

Demonstrated the framework's application in developing simulations.
Validated the developed theoretical conditions.
Showcased automatic structural responses for complex applications.

Conclusions:

STDC offers a powerful approach for adaptive SNNs.
The proposed framework facilitates the design of novel constructive SNNs.
STDC has potential applications in biological modeling and machine learning.