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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Sequence Networks of Rotating Machines01:24

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Multi-species Conserved Sequences02:51

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Protein Complex Assembly

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Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq
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A genetic regulatory network based method for multi-objective sequencing problem in mixed-model assembly lines.

You Long Lv1, Jie Zhang1

  • 1College of Mechanical Engineering, Donghua University, 2999 North Renmin Road, Shanghai, China.

Mathematical Biosciences and Engineering : MBE
|April 6, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel genetic regulatory network method to optimize assembly line sequencing, minimizing costs and production variations. The approach effectively balances multiple objectives for improved efficiency in mixed-model manufacturing.

Keywords:
differential equationgene regulationgenetic regulatory networkmixed-model assembly linemultiple objectivessequencing problem

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Area of Science:

  • Operations Research
  • Industrial Engineering
  • Computational Biology

Background:

  • Mixed-model assembly lines face complex sequencing challenges with multiple, often conflicting, objectives.
  • Minimizing utility work, production rate variation, and setup costs simultaneously is crucial for operational efficiency.

Purpose of the Study:

  • To develop and validate a novel genetic regulatory network (GRN) based sequencing method for mixed-model assembly lines.
  • To address multi-objective optimization problems inherent in assembly line balancing and scheduling.

Main Methods:

  • Constructed a mathematical model for the multi-objective sequencing problem.
  • Developed a GRN approach where genes represent decision variables and gene regulation equations model interactions.
  • Designed a gene expression procedure to generate solutions and optimize objective functions via regulatory parameter tuning.

Main Results:

  • The proposed GRN method effectively generates a series of solutions for the sequencing problem.
  • Comparative experiments demonstrated superior solution quality compared to existing methods.
  • The method showed particular effectiveness on industrial instances from a diesel engine assembly line.

Conclusions:

  • The genetic regulatory network based sequencing method offers a powerful new approach for mixed-model assembly line optimization.
  • This biologically inspired method provides a robust framework for minimizing weighted sums of multiple objectives.
  • The validated effectiveness highlights its potential for real-world industrial applications.