Current methods:
- Codon mapping - Mapping the codon values to a lookup table. Simple, quick, and high nucleotide and codon % matches.
- Discrete optimization version 0:
- Iterates through and for each codon finds the best optimized codon.
- Problems with high GC content at beginning and cutting down at end.
- Slow, relatively good results but not state-of-the-art.
- Discrete optimization version 1:
- Iterates through, measures fitness within a specific frame(size 12), and for each codon finds best optimized codon.
- GC content can get extremely close(within 0.1%) to actual vaccine, at cost of major nucleotide and codon differences.
- Fixes GC problems slightly(although sometimes avg GC content within a specific area might dip, this fixes it so it's not indicative of real vaccine)
- Discrete optimization version 2:
- Iterates through, measures fitness for entire sequence and finds best codon to change.
- Very good results - High nucleotide and codon % matches
- Also high GC % and codon frequency %
- Much slower than versions 0 and 1
- Discrete optimization version 3:
- Same as version 2, but optimizes fitness function
- Converges slightly faster
- Fitness function normalized and doesn't require alpha value(which is a constant that isn't guaranteed to be the same across different viruses)
SCHEDULE
Big Dates
Feb. 7: Scienteer finished
Feb. 13: Slides finished
Feb. 15: Hear from judges
Feb. 20: Presentation
Todo
- Find the antigen
- Given antigen name, isolate it within full genome and run program on it
- Create lookup table and identify which to use
- Create GA measuring:
- GC content
- Codon optimization(looking at frequency of codons in human body & use less rare ones)
- Hairpin structures
- CAI Index
- Fix collapsed
Scienteer Info
- Title and category
- Team status
- Project start date
- Survey questions
- Research Plan
- Extra Forms
- Bibliography
- Research Locations
- External Signatures
- Project Approval Method
- Teacher Approval
- IRB Approval
- SRC Approval
- Project end date
- 1C Signature
- SRC Post-approval
- Project Summary
- Abstract
Parts
- Background
- Rationale
- Introduction
- Purpose
- Hypothesis
- Code
- Procedure
- Materials
- Conclusion
- Problems Encountered
- Future Expansions
- Practical Applications
- Bibliography
Day-by-Day
Feb. 1
- Research plan
- Extra forms
- Implement CAI index
- Background
- Rationale
- Create vaccine given specific features(codon_mapping.py + identify_antigen.py)
Feb. 2
- Materials
- Implement CAI index
Feb. 3
- Procedure
- GA - Implement mutation, population selection
Feb. 4
- Problems Encountered
- Create simple shell script to execute
- Implement self-replicating vaccine
- Bibliography
- Introduction
- Purpose
- Background
Feb. 5
- Future Expansions
- Practical Applications
- Implement self-replicating vaccine
- Apply GA to 3 viruses
Feb. 6
- Connect self-replicating vaccine to lookup table, find corresponding structural proteins
- Apply to 3 viruses
- Calculate when to finish
- Rendering:
- Antigen shading
- Run vaccine through AlphaFold + render w/ GFuzz
- Select best 5' and 3' UTRs + cap
- Conclusion
Feb. 7
- Annotate code!
- Major clean-up of files
- Continue rendering
- Conclusion
- Optimize
Feb. 8
- Presentation work
- Continue rendering work
- Slides
- Self-replication work
Feb. 9
- Rendering
- Presentation
- Slides
- Self-replication work
Feb. 10
- Rendering
- Apply GA to more viruses
- Optimize GA if possible
- Creating UI
Feb. 11
- Optimize GA
- Apply GA to more viruses
- Self-replication work
- Slides
- Creating UI
Feb. 11
- Filler - [ ] Add more info to binder
- Slides
- Creating UI
Feb. 12
- Putting down final results
- Finalizing slides