Homework
Weekly homework submissions:
Week 1 HW: Principles and Practices
First, describe a biological engineering application or tool you want to develop and why. This could be inspired by an idea for your HTGAA class project and/or something for which you are already doing in your research, or something you are just curious about. As a Biologist, I have a macro-scale perspective on life, from organisms to ecosystems to planetary systems, and have always been drawn to technological innovations. However, I am now curious about the fundamental question of what constitutes life at a micro-scale, and what does engineering its core principles entail. Still interested in biocomputational methods, I want to learn more about the intersection of bio-artificial intelligence and synthetic biology.
Week 2 HW: DNA Read, Write, & Edit
Part 1: Benchling & In-silico Gel Art My original idea was to create two sister chromatids, since most of the patterns from the Enzymes were scattered vertical lines, and they kind of looked like alleles inside a chromosome. I had some trouble creating the centromere of the chromosome because none of the enzymes alone created just one line in the middle of the ladder (so around 800 bp), so I picked SacI and SalI and ignored the top line at 12.0 kb.
Opentrons Artwork For this activity, I decided to do Majora’s Mask from The Legend of Zelda: To view the design in the Opentrons page, follow this link: https://opentrons-art.rcdonovan.com/?id=7f6p4e5817h6ar0 To view the full code in Google Colab, follow this link: https://colab.research.google.com/drive/1FpWo3znYjL6Z7uWbc_opzQbmgbDo33rL?usp=sharing Post Lab Questions 1. Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications.
Week 4 HW: Protein Design Part I
Part A. Conceptual Questions 1. How many molecules of amino acids do you take with a piece of 500 grams of meat? (on average an amino acid is ~100 Daltons)
- Why do humans eat beef but do not become a cow, eat fish but do not become fish?
Week 5 HW: Protein Design Part II
Part A: SOD1 Binder Peptide Design Part 1: Generate Binders with PepMLM To get the human SOD1 sequence, I went to UniProt. The ID for this protein is P00441 and the sequence is the following: MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ Now, if the mutation is A4V, that means that in position 4 there’s a change from alanine to valine. The mutated sequence is then the following:
Week 6 HW: Genetic Circuits Part I: Assembly Technologies
Assignment: DNA Assembly 1. What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose? The Phusion High-Fidelity PCR Master Mix contains all the core reagents necessary for accurate and efficient DNA amplification: Phusion DNA Polymerase: A high-fidelity enzyme with 3′→5′ exonuclease proofreading activity that minimizes errors during DNA synthesis, especially important for mutation-based cloning (NEB, 2023). dNTPs (deoxynucleotide triphosphates): Provide the nucleotide building blocks (A, T, G, C) for DNA strand elongation. Reaction Buffer (with Mg²⁺): Maintains the ionic strength and conditions needed for optimal enzyme activity and DNA strand stability. Stabilizers & enhancers: Help maintain enzyme performance across temperature ranges and buffer pH changes during thermocycling. (New England Biolabs (NEB), 2023).
Week 7 HW: Genetic Circuits Part II: Neuromorphic Circuits
Assignment Part 1: Intracellular Artificial Neural Networks (IANNs) 1. What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? Characteristic Intracellular Artificial Neural Networks Traditional Genetic Circuits (that use Boolean functions) Input-output mapping Continuous logic that can sum multiple inputs with determined importance or “weights”. This allows for classification of complex patterns. Discrete simple logic (AND, OR, NAND) with ON/OFF behaviors. Vulnerability to noise Since they rely on graded responses, they can average across inputs. This makes them less vulnerable to change output when exposed to noise. Sensitive to noise around thresholds. If there are small fluctuations the ON/OFF gate can be flipped. Decision-making They classify inputs into categories at once and produce signals to different “effector modules” (also called “winner-take-all decisions” in mammalian cells, as mentioned in Chen et al., 2024). This also allows for higher adaptive behavior. They often produce a single binary output per circuit. This makes them less adaptable. Table created using information taken from:
Homework Part A: General and Lecturer-Specific Questions General homework questions 1. Explain the main advantages of cell-free protein synthesis over traditional in vivo methods, specifically in terms of flexibility and control over experimental variables. Name at least two cases where cell-free expression is more beneficial than cell production.
Week 10 HW: Advanced Imaging & Measurement Technology
Final Project Please identify at least one (ideally many) aspect(s) of your project that you will measure. It could be the mass or sequence of a protein, the presence, absence, or quantity of a biomarker, etc. Please describe all of the elements you would like to measure, and furthermore describe how you will perform these measurements. What are the technologies you will use (e.g., gel electrophoresis, DNA sequencing, mass spectrometry, etc.)? Describe in detail.
Week 10 HW: Advanced Imaging & Measurement Technology
Final Project