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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Genomic Imprinting and Inheritance02:30

Genomic Imprinting and Inheritance

Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
The expression of some genes depends on which parent passed the gene to the offspring, through a phenomenon known as...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
DNA as a Genetic Template02:05

DNA as a Genetic Template

Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...
DNA as a Genetic Template02:05

DNA as a Genetic Template

Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...
Genomic DNA in Prokaryotes00:46

Genomic DNA in Prokaryotes

The genome of most prokaryotic organisms consists of double-stranded DNA organized into one circular chromosome in a region of cytoplasm called the nucleoid. The chromosome is tightly wound, or supercoiled, for efficient storage. Prokaryotes also contain other circular pieces of DNA called plasmids. These plasmids are smaller than the chromosome and often carry genes that confer adaptive functions, such as antibiotic resistance.
Genomic Diversity in Bacteria
Although bacterial genomes are much...

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Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight
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An Overview and Perspectives on the Materials Genome Initiative-Based Asphalt Mix Design Framework.

Jian Liu1, Zhen Wang1, Fanijo Ebenezer2

  • 1School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia, Athens, GA 30602, USA.

Materials (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

The Materials Genome Initiative (MGI) offers a new framework for optimizing asphalt mix design. Integrating experiments, modeling, and big data analytics can overcome limitations of traditional methods for durable highways.

Keywords:
Materials Genome Initiativeaggregateartificial intelligenceasphalt binderasphalt mix designdata-drivenlaboratory testingnumerical simulation

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Area of Science:

  • Materials Science
  • Civil Engineering
  • Computational Materials Science

Background:

  • Traditional asphalt mix design relies on trial-and-error, limiting the development of high-performance mixtures for durable highways.
  • The Materials Genome Initiative (MGI) framework accelerates materials development through integrated experiments, computational modeling, and big data analytics.
  • MGI has shown success in other material domains, indicating strong potential for asphalt mixture optimization.

Purpose of the Study:

  • To systematically review the application prospects of MGI methodologies in asphalt mixture design.
  • To identify limitations of current asphalt mix design approaches, including laboratory testing, numerical simulations, and artificial intelligence.
  • To propose an integrated MGI-based framework for optimizing asphalt mix design.

Main Methods:

  • Review of MGI progress in materials design.
  • Summary of current research in asphalt mix optimization using laboratory testing, numerical simulations (FEM, DEM), and AI.
  • Exploration of advanced experimental techniques for multiscale characterization of asphalt binders and aggregates.
  • Definition of the asphalt mixture genome (binder, aggregate, compaction genes).

Main Results:

  • Current asphalt mix design methods have limitations that can be overcome by integrating MGI principles.
  • An integrated MGI framework requires combining experimental, computational, and data analytics approaches.
  • The concept of an asphalt mixture genome is defined, encompassing key material and process factors.

Conclusions:

  • Applying the MGI framework to asphalt mix design holds significant potential for improving highway durability.
  • Integration of diverse methodologies is crucial for developing a comprehensive MGI-based asphalt mix design system.
  • Defining the asphalt mixture genome provides a foundation for data-driven material discovery and optimization.