Homework

Weekly homework submissions:

• Week 1 HW: Principles and Practices

  Class assignment 1. First, describe a biological engineering application or tool you want to develop and why. I’m heavily inspired by Professor Jacobson’s call for a “bio-FPGA” tool, as well as his lecture about cellular automata. I’d like to develop a bio-FPGA that can be programmed to grow into arbitrary 2D patterns on a petri dish, using the machine learning technique mentioned in the lecture to reverse learn the cellular automata rules for growing a specific pattern. The learned CA rules can be encoded by genetically programming the bio-FPGA then using bacteria with the genes to grow an actual cell culture into the pattern, like the butterfly wing letter patterns in the lecture. If this is feasible, 3D patterns would be the next step, and one might even imagine a wild future of programmable plants that grow into the shapes of houses and furniture.

• Week 2 HW: DNA Read, Write, and Edit

  Part 0: Basics of Gel Electrophoresis I watched the lecture, recitation, and read the lab. Essentially, we use the negative charge of DNA to pull DNA fragments towards a positive anode in a porous agarose gel. Larger DNA fragments move slower in the agarose gel. Part 1: Benchling & In-silico Gel Art I spent some time playing around with Ronan’s gel art site to make a pattern (below on the left). I noticed that some of the restriction enzymes in the gel art tool weren’t on the HTGAA enzyme list, so I didn’t use them.

• Week 3 HW: Lab Automation

  Opentrons Artwork My artwork is here: https://rcdonovan.com/?id=vmns94wqt45wpqc I used Ronan’s tool to make this. I uploaded an image of tomatoes but it didn’t render well, so I modified it significantly by hand with the editor. Then, I attended the Saturday session on Zoom with Ronan, Michelle, and Ice at Ginkgo Bioworks. Here’s the end result:

• Week 4 HW: Protein Design, Part I

  Part A: Conceptual questions Answer any NINE of the following questions from Shuguang Zhang: (i.e. you can select two to skip) 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) A dalton is 1.66053906892(52)*10−23 g, so 500g = 500 / 1.66053906892(52)*10−23 = 3.0110704e+25 daltons.

• Week 5 HW: Protein Design, Part II

  Part A: SOD1 Binder Peptide Design (From Pranam) Superoxide dismutase 1 (SOD1) is a cytosolic antioxidant enzyme that converts superoxide radicals into hydrogen peroxide and oxygen. In its native state, it forms a stable homodimer and binds copper and zinc. Mutations in SOD1 cause familial Amyotrophic Lateral Sclerosis (ALS). Among them, the A4V mutation (Alanine → Valine at residue 4) leads to one of the most aggressive forms of the disease. The mutation subtly destabilizes the N-terminus, perturbs folding energetics, and promotes toxic aggregation.

• Week 6 HW: Genetic Circuits I

  What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose? The mix includes: Phusion DNA Polymerase, deoxynucleotides and reaction buffer that has been optimized and includes MgCl2. DNA Polymerase builds the DNA sequence from deoxynucleotides starting with the template strands and primer. It’s high-fidelity, so it won’t make as many errors as Taq. Reaction buffer allows the reaction to take place. What are some factors that determine primer annealing temperature during PCR? For primer annealing, we want the template to be high enough for the template DNA to be denatured and but low enough that some of the primer is annealed. So the lab protocol suggests to use temperatures near the Tm of the primer (temp where the DNA is 50/50 double/single stranded).

• Week 7 HW: Genetic Circuits II

  Assignment Part 1: Intracellular Artificial Neural Networks (IANNs) What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? IANNs are analog, instead of digital (e.g. boolean/binary logic), which means they work on numerical values. This means that their behavior can be finely adjusted: they can output specific quantities, and they can take into account the specific/relative magnitudes of inputs. This is well suited for biological applications because biological systems exhibit homeostatic behaviors.