Homework
Weekly homework submissions:
Week 1 HW: Principles and Practices
Homework Week 1: Class Assignment Biological Engineering Application First Steps towards “Intelligence in a (warehouse)-dish” Guided by the vision of building a biological general computing system, the goal of the proposed tool is to provide a minimal, yet replicable brain organoid based system, that can be engineered to exhibit controllable, learning-like signal processing behaviour. The system consists of 3 conceptual parts (input - computation - output), that manifest in 2 integrated physical devices.
Week 2 HW: DNA Read, Write & Edit
Part 0: Basics of Gel Electrophoresis Watch Week 2 Lecture (Zoom) Watch Week 2 Recitation (Zoom) Watch BioBootcamp Day 1 - Day 3 (Zoom) Part 1: Benchling & In-silico Gel Art 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 Artwork After struggling quite some time with the task of creating artwork with the limited amount of restriction enzymes, in the end decided to stick to a relatively easy and repetitive pattern that with a little imagination has a lot of versatile interpretations: It can be two friends hanging out It can be DNA (or at least a rought estimation of the firrst two loops) To help you visualize it a bit better i created some generative AI art
Python Script for Opentrons Artwork Review this week’s recitation and this week’s lab for details on the Opentrons and programming it. Generate an artistic design using the GUI at opentrons-art.rcdonovan.com. I generated a quick design using the above mentioned tool: BioPunk Initials See the: 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. 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: If you use AI to help complete this homework or lab, document how you used AI and which models made contributions. As its good practice in Software Engineering, not to reinvent the wheel, I had a look at the provided examples, and figured Example 7 would be a good basis for my requirements. Nevertheless, significant updates needed to be done to make the code useful for my purposes. These include:
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) Assuming that approx 20-25% of the weight is protein, that leaves us with 100-125g protein in a 500 gram meat piece. The average amino acid molar mass is approx. 100 g/mol. With Avogadro’s number 6.022×1023 the number of molecules is between: 1 x 6.022×1023 = 6.022×1023 and 1.25 x 6.022×1023 = 7.5 ×1023 (approx)
Part A: SOD1 Binder Peptide Design (From Pranam) Part 1: Generate Binders with PepMLM Begin by retrieving the human SOD1 sequence from UniProt (P00441) and introducing the A4V mutation. The Protein can be found with this link The Protein sequence is:
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? I didn’t find anything pertaining the matter in the protocol itself, though a quick websearch (Link 1, Link 2), revealed that the PCR Master Mix contains 4 main ingreadients.
Week 7 HW: Genetic Circuits Part II
Part 1 What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? The key advantage of intracellular analog neural networks (IANNs) over traditional genetic circuits lies in the shift from discrete logic to continuous computation. Classical genetic circuits are typically engineered as Boolean systems: inputs are interpreted as “on” or “off”. This abstraction is convenient for engineering and design, but it is fundamentally misaligned with how biology actually operates, where signals exist as continuously varying concentrations and reaction rates.