Homework
Weekly homework submissions:
Week 1 HW: Principles and Practices
Describe a biological engineering application or tool you want to develop and why. I am interested in technologies that can enable an outside the body gestation and perhaps in the future, a pregnancy-free gestation and birth. Two such technologies are crucial to this future: artificial womb technology (AWT) and stem cell-based embryo models (SCBEMs). Stem cell-based embryo models (SCBEMs) are stem cells that can form viable human embryos without the need for sperm or egg cells. SCBEMs are most useful in research identifying causes of miscarriage and are not legally allowed to be grown post 14 days. A code has been developed in the UK from Cambridge University that does not allow for the sperm and egg cell-free embryos to be implanted into carriers. Without such enforcements, we might see a future whereby we can build new species without physical nor emotional attachment to ourselves.
Week 2 HW: DNA Read, Write, & Edit
PART 1: Benchling & In-Silico Gel Art Lecture Prep Homework questions from Professor Jacobson The human genome is ~3.1 billion base pairs long. DNA polymerase makes roughly one error per 10 to the five nucleotides. Without correction, this is fairly error prone as it would make roughly 30,000 errors per genome replication. Luckily, human biology has interesting error correction measures including the mismatch repair system. The mismatch repair system recognises base-base mismatches and insertion/deletion mispairs through the complexes of MutS-related proteins that recognise nicks in the DNA strands. There are over a million ways to code an average human protein. Not all the possible codes can work for a protein of interest due to codon rarity and context-dependent protein production. Homework questions from Dr. LeProust
Python Script found here. Find my three project ideas here, but here is a draft outline Post Lab Questions Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications. I read the paper “High-Throughtput Microbial Culturomics Using Automation and Machine Learning” This paper describes a process utilising lab automation and machine learning to enable faster strain isolation and characterisation. Isolating individual bacterial from the collected microbiome is an arduous process.