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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Evolutionary Processes in Microbes

Microbial evolution occurs rapidly due to short generation times and a variety of genetic processes, including horizontal gene transfer, mutation, recombination, and genetic drift. These mechanisms collectively enable microbes to adapt swiftly to changing environments.Horizontal gene transfer (HGT) allows genes to move between different species and occurs through three main mechanisms: conjugation, transformation, and transduction. Conjugation involves direct cell-to-cell contact for DNA...
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Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Microbial genome evolution is a highly dynamic process shaped by continual gene gain and loss across species and strains. This genomic flexibility allows microorganisms to adapt rapidly to environmental pressures and interactions with other organisms. Central to understanding this diversity is the distinction between the core and pan genomes.The core genome comprises the genes shared by all sampled strains of a species, representing essential functions needed for fundamental cellular processes.
Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

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Published on: July 25, 2013

EvoMD: an algorithm for evolutionary molecular design.

Samuel S Y Wong1, Weimin Luo, Keith C C Chan

  • 1Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. cssywong@comp.polyu.edu.hk

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|September 30, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces the Evolutionary Algorithm for Molecular Design (EvoMD), a novel approach for automated molecular design. EvoMD overcomes limitations of traditional methods by using graph-based evolutionary algorithms without requiring predefined chemical rules.

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Area of Science:

  • Computational Chemistry
  • Cheminformatics
  • Artificial Intelligence

Background:

  • Traditional Computer-Aided Molecular Design (CAMD) methods struggle with large, nonlinear search spaces.
  • Existing graph-based evolutionary algorithms (EAs) require predefined molecular components and chemical rules.

Purpose of the Study:

  • To develop an automated molecular design approach that overcomes the constraints of traditional methods.
  • To introduce a novel evolutionary algorithm (EA) for molecular design that does not require prior knowledge of chemical rules.

Main Methods:

  • Proposes the Evolutionary Algorithm for Molecular Design (EvoMD), which encodes molecular designs in graphs.
  • Utilizes a novel crossover operator independent of known chemistry rules and employs novel mutation operators.
  • Incorporates atomic-based and fragment-based approaches for handling molecules of varying sizes.

Main Results:

  • EvoMD demonstrates a promising approach to automated molecular design.
  • The fitness function is based on property descriptors of the molecular graph design.
  • Successful testing across diverse datasets indicates EvoMD's potential.

Conclusions:

  • EvoMD offers a flexible and automated solution for molecular design.
  • The algorithm's ability to operate without predefined chemical rules is a significant advancement.
  • EvoMD shows potential for broader applications in drug discovery and materials science.