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For each of the following exercises, determine the range (possible values) of the random variable. The number of mutations in a nucleotide sequence of length 40,000 in a DNA strand after exposure to radiation is measured. Each nucleotide may be mutated.

Short Answer

Expert verified
The range is 0 to 40,000.

Step by step solution

01

Understand the Context

We are given a DNA strand with a nucleotide sequence length of 40,000. After exposure to radiation, each nucleotide has the potential to mutate. We need to determine the range of the number of mutations that can occur in this sequence.
02

Define the Random Variable

The random variable in this context is the number of mutations in the nucleotide sequence of length 40,000. Let's denote this random variable as \( X \). The task is to find the possible range of values that \( X \) can take.
03

Identify Possible Values of the Random Variable

Since there are 40,000 nucleotides in the sequence, \( X \) could be 0 if no mutations occur, up to 40,000 if every nucleotide undergoes a mutation. Thus, the possible values \( X \) can take range from 0 to 40,000.
04

Determine the Range of the Random Variable

The range of a random variable is the set of all possible values it can take. Based on our analysis, the range of \( X \), the number of mutations, is all integers from 0 to 40,000, inclusive.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Range of Random Variables
Random variables are variables whose values are determined by the outcomes of a random process or experiment. The range of a random variable represents all the possible values it can assume. In this context, we are dealing with mutations that can occur in a nucleotide sequence due to radiation exposure.

For the given DNA strand with 40,000 nucleotides, the random variable \( X \) captures the number of mutations. The range is inclusive of all integer values between the lowest possible number of mutations (0) and the maximum possible mutations (40,000). Hence, \( X \) can take any integer value from 0 to 40,000, inclusive.
  • A range defines the boundaries for your random variable's outcomes.
  • In this exercise, it encapsulates all scenarios, from no mutations to complete mutation of the sequence.
Understanding the range helps in statistical analyses, providing a basis for calculating probabilities of different mutation scenarios.
Mutation Probability
Mutation probability refers to the likelihood that a mutation will occur in a given nucleotide due to exposure to certain conditions like radiation. Each nucleotide has a chance or risk of mutating, which is expressed as a probability between 0 and 1.

For a sequence of 40,000 nucleotides, the probability of mutation for each nucleotide affects the overall outcome. If each nucleotide has the same mutation probability \( p \), the expected number of mutations can be calculated by multiplying \( p \) by the total number of nucleotides.
  • Mutation probability must be assessed based on specific environmental factors and genetic makeup.
  • Higher mutation probabilities lead to a greater number of expected mutations within the sequence.
Calculating mutation probabilities is crucial in genetic studies, where it's important to predict how many nucleotides might undergo changes.
Nucleotide Sequence
A nucleotide sequence refers to the linear sequence of nucleotides in a DNA or RNA molecule. Each nucleotide consists of a sugar, a phosphate group, and a nitrogenous base, and the sequence of these bases encodes genetic information.

In the given exercise, we are looking at a sequence of 40,000 nucleotides, which can be thought of as a very long string of genetic code. Mutations in this sequence alter the base pairings, potentially leading to changes in the organism's traits or functions.
  • Each position in a nucleotide sequence is a potential site for mutation.
  • The sequence is fundamental in biotechnology and genetics for understanding organismal traits.
Recognizing the significance of nucleotide sequences helps in various applications, such as genetic engineering, forensic science, and evolutionary biology.

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