Computational Biology: Dna Sequencing And Inherited Traits
Time: 2-3 class periods (45 minutes each)
Level: Grade 11-12, regular or AP biology
Lesson OverviewThe students learn about computational biology careers and use professional computational biology tools to carry out DNA sequencing experiments.
Students need to be familiar with the basics of DNA: they should know that DNA consists of 4 bases, A, T, C, and G, and that the arrangement of these bases codes for genes. Some understanding of algorithms would be helpful but is not required.
Gene sequencing is all over the news these days. It wasn’t too long ago that scientist sequenced the human genome for the first time. Now, there are many genomes that have been sequenced, and people are both excited and terrified by the scientific advances that this might bring. Many news reports on this subject are somewhat misleading, however, in that they imply that by knowing the gene sequence, we know everything about the organism. However, a genome sequence is just a collection of letters. Suppose you have a gene sequence like ATTACGGGCGCAGCT. What could you say about the organism from which it came? In truth, biologists can’t say any more from that sequence than you can.
In the real world, a sequenced genome isn’t the end or the beginning of the end or even the end of the beginning. We need powerful computational techniques to assemble a genome, find the genes, and determine the functions of those genes.
One way to interpret a genetic sequence is to compare sequences. If we have, for example, a sequence of human DNA and we want to know its function, we might see if there is a similar sequence in an organism we have studied more thoroughly, such as yeast. If the two sequences are similar enough, then it is likely that they have similar function.
Comparing sequences is difficult, however, because DNA mutates. The comparison is done using an important computational biology program, the Basic Local Alignment Search Tool, or BLAST.
- Computational Problem Solving Practices
- Developing Modular Computational Solutions
- Data Practices
- Analyzing Data
- Visualizing Data
- Systems Thinking Practices
- Understanding the Relationships within a System