Homework
Weekly homework submissions:
Week 1: PRINCIPLES & PRACTICES
Week # 1 Homework Principles & Practices A look at the ethics, safety and security considerations for a biological engineering application with the proposed governance policy goals and actions. Most countries like Kenya in the developing countries have a waste problem that causes a lot of health issues to the people who live near them while damaging the ecosytem around them that creates a burden for the country in dealing with the financial implications. Synthetic genomic has made it possible through the use of biological organisms that clean up environmental waste and simultaneously produce energy, making this one of the most active fields in biotechnology often referred to as the Circular Bioeconomy. In the latest research which is moving toward Genetically Modified Organisms (GMOs) that can perform multiple tasks at once. Using CRISPR-Cas9, scientists have been able to ceate “super-microbes” that can: • Detect a specific pollutant (like a biosensor). • Break down that pollutant (bioremediation). • Synthesize a fuel molecule (valorization) simultaneously. There is the need to produce biofuels more sustainably than the traditional way with the use of synthetic biology. The problem in Kenya right now we have a lot of second hand clothes that are piled up as waste in dump site, also plastics chocking waterways and scattered all over the streets with no central place to collect them or few collection centers. E-Waste where Kenya generates over 53,000 tonnes annually creating a waste problem. The new technology from synthetic biology would help to eradicate the problem and at the same time generate energy that will help counter the large import bill for gasoline, diesel and kerosine we purchase every year.
Week 2: DNA READ, WRITE, AND EDIT
Week # 2 Homework DNA READ, WRITE & EDIT A look at the sequencing and synthesis workflows, restriction digests and gel electrophoresis, and early genome-editing frameworks. Part 1: Benchling & In-silico Gel Art See the Gel Art: Restriction Digests and Gel Electrophoresis protocol 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!
Week # 3 Lab Automation LAB AUTOMATION To get hands-on (or at least code-on) with pipetting robots. Your task this week is to Create a Python file to run on an Opentrons liquid handling robot. 0. Review this week’s recitation and this week’s lab for details on the Opentrons and programming it. 1. Generate an artistic design using the GUI at opentrons-art.rcdonovan.com. 2. Using the coordinates from the GUI, follow the instructions in the HTGAA26 Opentrons Colab to write your own Python script which draws your design using the Opentrons. ◦ You may use AI assistance for this coding — Google Gemini is integrated into Colab (see the stylized star bottom center); it will do a good job writing functional Python, while you probably need to take charge of the art concept. ◦ If you’re a proficient programmer and you’d rather code something mathematical or algorithmic instead of using your GUI coordinates, you may do that instead. Ask for help early! 3. If the Python component is proving too problematic even with AI and human assistance, download the full Python script from the GUI website and submit that: Use the download icon pointed to by the red arrow in this diagram. The Python component was problematic and I sent the the python script (1 OTDesign_02-26-26_22-49-52.py)
Week # 4 Protein Design Part I PROTEIN DESIGN PART I To look at how sequence, structure, and energetics can be modeled and manipulated to create or optimize proteins with specified functions. 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) For a Tilapia Fish: Assuming : meat = 20% protein by weight; average amino acid ≈ 100 Da (g/mol). Calculation: • Protein mass = 500 g × 0.20 = 100 g • Moles of amino-acid residues = 100 g ÷ 100 g·mol⁻¹ = 1.00 mol • Number of amino-acid molecules using Avogadro’s number ≈ 1.00 × ≈ 6.02 × 1023 = 6.02 × 1023 amino-acid molecules. Why do humans eat beef but do not become a cow, eat fish but do not become fish? The beef meat is in the form of amino acids that our body needs which is broken down by the enzymes in our stomach to the amino acids required by our body. The amino acids are the building blocks of DNA. Beef also provides protein, zinc and several D vitamins used for muscle health, iron that boosts our immune system
Week 5: PROTEIN DESIGN PART II
Week # 5 Protein Design Part II PROTEIN DESIGN PART II To learn how cutting-edge AI and protein language models are used to design functional proteins and peptides “in silico”. 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. Your challenge: 1. Design short peptides that bind mutant SOD1. 2. Then decide which ones are worth advancing toward therapy. You will use three models developed in our lab: • PepMLM: target sequence-conditioned peptide generation via masked language modeling • PeptiVerse: therapeutic property prediction • moPPIt: motif-specific multi-objective peptide design using Multi-Objective Guided Discrete Flow Matching (MOG-DFM)
Week 6: GENETIC CIRCUITS PART I
Week # 6 Genetic Circuits Part I GENETIC CIRCUITS PART I To learn core molecular biology tools and techniques for processing and assembling DNA, including PCR and Gibson Assembly. Assignment: DNA Assembly Answer these questions about the protocol in this week’s lab: What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose? Phusion High-Fidelity PCR Master Mix is a comprehensive formulation that supplies all the essential components required for precise and efficient DNA amplification through the polymerase chain reaction (PCR). The mixture contains Phusion polymerase, an enzyme renowned for its exceptional accuracy in synthesizing new DNA strands during the amplification process. It also includes deoxynucleotide triphosphates (dNTPs), which serve as the molecular building blocks that polymerase incorporates into the growing DNA chains. Additionally, magnesium chloride (MgCl₂) is present as a critical cofactor—an enabling molecule that the polymerase enzyme requires to function optimally and catalyze the formation of new DNA bonds. Finally, the formulation includes a reaction buffer solution that maintains the proper chemical environment throughout the PCR process. This buffer preserves stable pH levels and regulates salt concentration, ensuring that all enzymatic reactions proceed smoothly and that the overall amplification process achieves maximum efficiency. In essence, Phusion High-Fidelity PCR Master Mix eliminates the need to manually combine individual components—it is a ready-to-use formulation where all necessary ingredients are already optimized and proportioned for reliable, high-fidelity DNA amplification.
Week 7: GENETIC CIRCUITS PART II
Week # 7 Genetic Circuits Part II GENETIC CIRCUITS PART II To learn neuromorphic genetic circuits, showing how engineered gene networks can implement neural-network “perceptron”-like computation and learning Assignment Part 1: Intracellular Artificial Neural Networks (IANNs) What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? To understand the advantages of IANNs (In silico Artificial Neural Networks / Integrated Artificial Neural Networks in synthetic biology) over traditional Boolean genetic circuits, it helps to look at how biological computing is evolving. Traditional genetic circuits act like classic computer chips: they take inputs (like the presence of a specific molecule) and use logic gates (AND, OR, NOT) to produce a definitive, binary ON/OFF response. IANNs, however, mimic the brain’s neural networks using biological components. Here is why IANNs are a massive step up from traditional Boolean genetic circuits:
Week # 9 Cell Free Systems CELL FREE SYSTEMS To learn synthesis of proteins using cellular machinery outside of a cell. General homework questions 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 Advantages Cell-free protein synthesis (CFPS) provides substantial benefits compared to conventional cell-based protein production methods, particularly in terms of experimental flexibility and precise control over reaction parameters. In contrast to traditional in vivo approaches that require cell transformation, growth in culture media, and cell disruption, CFPS enables rapid protein production without these intermediate steps, significantly accelerating the research timeline.
Week 10: IMAGING AND MEASUREMENT
Week # 10 Imaging and Measurement IMAGING AND MEASUREMENT To learn a range of advanced technologies to do precision measurement of proteins at atomic scales, characterizing chemical composition, and detecting protein sequence and structure. Homework: Waters Part I — Molecular Weight
Week # 11 Building Genomes BUILDING GENOMES To inspire collaboration and creativity while designing a scientifically rigorous cell-free fluorescent protein optimization experiment together. Part A: The 1,536 Pixel Artwork Canvas | Collective Artwork 1. Contribute at least one pixel to this global artwork experiment before the editing ends on Sunday 4/19 at 11:59 PM EST. ◦ A personalized URL was sent to the email address associated with your Discourse account, and you can discuss the artwork on the Discourse. https://rcdonovan.com/synbiobeta I contributed 3 on the in the middle of the artwork