Homework

Weekly homework submissions:

  1. 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. For HTGAA 2026, I’d like to propose the design and development of a synthetic biology based microbial system for the improvement of agricultural productivity in saline soils of the Bolivian Altiplano. This is because oil salinization is continuing to progress in the high-altitude areas of Bolivia as a consequence of climate change, water shortage and historical land use (Andrade, 2025). According to the Food and Agriculture Organization (n.d.), already a considerable fraction of irrigated and arid agricultural lands worldwide face the challenge of soil salinity. Scientific studies have shown that soil salinity significantly reduces crop yields, alters soil biological functions, and directly threatens food security, particularly in smallholder farming systems (Farooq et al., 2021). In the same way, the majority of smallholder farmers in the Altiplano rely on marginal soils, often where conventional fertilizers cannot be used effectively or are economically unaffordable and are a direct threat to local food security and livelihoods from salinization. This is why my proposed project aims to investigate the conceptual design for soil microorganisms that can sense such high salinity and improve soil structure and plant stress tolerance. However, beyond its technical feasibility, this application raises relevant ethical, environmental and governance issues surrounding environmental release and biosafety and also equitable access to biotechnology. Finally, as a Bolivian, I see this work as an opportunity to link cutting edge biological engineering with locally anchored solutions that address real challenges faced by vulnerable agricultural communities in my country.

  • Week 10 HW: Advanced Imaging & Measurement Technology

    Homework: Final Project 1. 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. I would like to measure multiple biological and functional aspects of the synthetic rhizosphere consortium composed of Pseudomonas fluorescens, Azospirillum brasilense, and Bacillus subtilis. Key variables include the production of osmoprotectants (such as proline or trehalose) under saline stress, nitrogen fixation efficiency, biofilm formation and exopolysaccharide (EPS) production, and the presence, sequence accuracy, and expression of engineered genetic constructs, including kill switch systems. At a higher level, the project will also assess microbial population dynamics and plant growth indicators such as root length and biomass, which serve as direct proxies for improved agricultural productivity under salt stress.

  • Week 11 HW: Bioproduction & Cloud Labs

    Part A: The 1,536 Pixel Artwork Canvas | Collective Artwork 1. My contribution Unfortunately, I was not able to contribute a pixel to the collective artwork, as I was in the middle of midterm exams at my university during that period, which limited my availability to participate.

  1. What I liked about the project I really liked the project because of its biological foundation and particularly its connection to cell-free fluorescent protein optimization and how it was used for a global pixel artwork designed by HTGAA students :)

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

    HOMEWORK 2 Part 1: Benchling & In-silico Gel Art See this week’s lab protocol “Gel Art: Restriction Digests and Gel Electrophoresis” for details. Overview: Make a free account at benchling.com Import the Lambda DNA. Simulate Restriction Enzyme Digestion with the following Enzymes: EcoRI HindIII BamHI KpnI EcoRV SacI SalI Create a pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artworks. You might find Ronan’s website a helpful tool for quickly iterating on designs! HOMEWORK RESULTS :)

  • Week 4 HW: Protein Design

    Part A. Conceptual Questions

  1. How many amino acid molecules do you take with 500 g of meat? If we assume that meat is approximately 20% protein, then 500 grams of meat contains about 100 grams of protein. The average molecular weight of an amino acid is roughly 100 Daltons (100 g/mol). Dividing 100 grams by 100 g/mol gives approximately 1 mole of amino acids and one mole contains 6.02 × 10²³ molecules, the Avogadro’s number. Therefore, consuming 500 grams of meat means ingesting on the order of 10²³ amino acid molecules.

  • 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 Part I

    Assignment: DNA Assembly Answer these questions about the protocol in this week’s lab:

  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 several key components necessary for efficient and accurate DNA amplification. First, it includes Phusion DNA polymerase, a high-fidelity enzyme with proofreading activity (3’ → 5’ exonuclease), which reduces errors during DNA replication. It also contains dNTPs (deoxynucleotide triphosphates), which are the building blocks used to synthesize new DNA strands. The mix includes a reaction buffer, optimized with the correct pH and salt concentrations to ensure proper enzyme activity. Additionally, it contains Mg²⁺ ions, which act as essential cofactors for the polymerase. Some mixes may also include stabilizers to maintain enzyme activity during thermal cycling.
  1. What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? Continuous signal processing: Unlike Boolean circuits that operate in binary (On/Off), IANNs can process graded inputs and outputs, enabling more nuanced cellular responses. Integration of multiple inputs: IANNs can combine many signals simultaneously and compute a weighted response, similar to an artificial neural network. Instead of being limited to simple logic gates (and, or, not), IANNs can model nonlinear relationships between inputs and outputs.

  • Week 9 HW: Cell-Free Systems

    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. Cell-free protein synthesis (CFPS) offers significant advantages over traditional in vivo expression systems, primarily due to its flexibility and precise control over experimental conditions. Because CFPS operates in an open environment without living cells, researchers can directly manipulate the concentrations of DNA templates, ions, cofactors, and other components in real time. This eliminates constraints associated with cellular viability, such as toxicity or metabolic burden. As a result, CFPS is particularly advantageous for the production of proteins that are toxic to host cells, such as antimicrobial peptides or pore-forming proteins. Additionally, CFPS enables rapid prototyping of genetic constructs, making it highly suitable for applications like synthetic biology circuit testing, where speed and iterative design are essential.

  • Week 3 HW: Lab Automation

    Assignment: Python Script for Opentrons Artwork Link: https://colab.research.google.com/drive/1k7nG8YrBwt0K0HMJtZNGSgcDcVOCriU7#scrollTo=JpjyYDE79Dfl MY CODE: import math ################################ GREEN SECTION (Body + Flagella) ################################ pipette_20ul.pick_up_tip() center = center_location Oval body: a = 16 b = 8 points = 40 for i in range(points): if i % 8 == 0: pipette_20ul.aspirate(8, location_of_color('Green')) angle = 2 * math.pi * i / points x = a * math.cos(angle) y = b * math.sin(angle) loc = center.move(types.Point(x=x, y=y, z=0)) dispense_and_detach(pipette_20ul, 1, loc) Flagella: