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Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
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Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Effects of feedback

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Dosage Compensation

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Reliably Engineering and Controlling Stable Optogenetic Gene Circuits in Mammalian Cells
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Adjusting phenotypes by noise control.

Kyung H Kim1, Herbert M Sauro

  • 1Department of Bioengineering, University of Washington, Seattle, Washington, United States of America. kkim@uw.edu

Plos Computational Biology
|January 19, 2012
PubMed
Summary

Scientists developed a new numerical method to control noise in biological systems. This approach allows for precise manipulation of gene expression and system dynamics, enhancing synthetic biology designs.

Area of Science:

  • Systems Biology
  • Synthetic Biology
  • Biophysics

Background:

  • Phenotypic variability in genetically identical cells arises from stochasticity in biochemical processes.
  • Controlling noise in biological networks is challenging due to complex mathematical structures.
  • Existing engineering approaches lack systematic methods for noise control.

Purpose of the Study:

  • To provide a numerical analysis method for quantifying parametric sensitivity of noise characteristics.
  • To enable systematic control of noise levels in biological systems.
  • To offer a tool for designing and controlling synthetic gene networks.

Main Methods:

  • Utilized the linear noise approximation for numerical analysis.
  • Quantified parametric sensitivity of noise characteristics.

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  • Applied the method to various biological systems including gene expression and metabolic networks.

    • Main Results:

    • Developed a method for orthogonal control of mean and noise levels.
    • Demonstrated control over system dynamics, such as noisy oscillations.
    • Showed that extrinsic noise and feedback can enhance control efficiency.
    • Successfully applied the method to HIV and yeast gene expression, and metabolic networks.

    Conclusions:

    • The proposed numerical analysis method offers a systematic approach to noise control in stochastic systems.
    • This method is applicable to a wide range of continuous time Markovian systems, including biological, chemical, and even social networks.
    • The findings are expected to advance the design and control of synthetic gene networks.