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ComputationalThinkingCourseComputingDNAdegeneracy
1. Computational Thinking Course:Computing DNA
degeneracy
Project plan at a glance...
Core subject(s) Science
Subject area(s) A level Biology, DNA structure and Genomics.
Suggested age 16 to 18 years old
Prior knowledge Understanding of genetic transcription and translation. N.b No
prior understanding of Python programming required.
Time Curriculum: 90 minutes
Extracurricular: 4 x 90 minutes.
Standards Core Subject: GCE Biology (UK)
Computer Science: UK
Project Overview
The project will be titled: "Degenerate DNA code: The search for genetic resilience." The
purpose of the project is for students to discover which nucleotide sequences are the most
resilient to the effects of mutations. The students will apply the computational thinking skills
(CTS) of decomposition, pattern recognition, abstraction and algorithm design in order
to further define the problem, create Python based solutions and formulate a conclusion.
Learning outcomes
Students will be expected to be able to:
● Communicate the ideas of the triplet code, degeneracy and genetic mutations.
○ State the central dogma of biology.
○ Describe how the triplet code has 64 possibilities, but encodes for only 20
amino acids.
○ Explain the significance of different mutations.
● Create, edit and test Python code that can solve the project question.
○ Create functions and variables.
○ Use loops and conditional logic.
○ Create and search data structures such as arrays.
2. Project lesson plan
Lesson content Potential structure/ suggested activities
1: Decomposition
Intro to DNA
mutations.
Introduction to the idea of DNA degeneracy and the project aims/objectives by
the teacher (presentation).
Students (self) organise into groups and each student researches a particular
mutation. CTS decomposition.
Students are tasked with breaking down the problem into subproblems. CTS
decomposition.
Potential project decomposition given and groups create an analysis and
suggest improvements.
2: Pattern
recognition
DNA degeneracy
Basic combinatorics worksheet/’practical’ activity using different coloured
counters and students, linking to the idea of codon degeneracy.
Students are given a variety of nucleotide sequences and using dice and or a
spreadsheet application create some nucleotide sequences that ‘appear’
resilient to random mutations. Students are guided to identify potential
patterns in the different nucleotide sequences. CTS Pattern recognition.
Discussion surrounding which nucleotide sequences are the most resistant to
random mutations.
3: Abstraction
DNA degeneracy
and interspecies
genetic
conservation.
Students asked to input their most resistant nucleotide sequences into a
random mutation Python program.
Students mind map generalisations. Students use a template Python program
that manipulates text strings in order to test generalisations.
Peer assess solutions and suggest improvements.
4: Algorithmic
design Genomic
databases and
BLAST
Students are given different snippets of pseudo code and are asked to arrange
them into a logical sequence (card sort type activity). Students create pseudo
code for their Python programs.
Students start writing their program. CTS algorithm design.
Students present preliminary findings and teacher comments on code design
(via an online feedback system).
5: Algorithmic
design
Self directed problem solving and coding.
Poster presentation of findings.
