Homework
Weekly homework submissions:
Week 1 HW: Principles and Practices
First, describe a biological engineering application or tool you want to develop and why Bioengineered watercress salad plants with nanoparticles enhancing conductivity and emitting light (phluorescence or phosphorecence). Along with Venus flytrap plant which naturally performs electrophysiological reactions closer to those in animals these research objects will be studied at Open BioArt Lab course on bioart practices related with plant electrophysiology. The course will be based on collaboration of Art&Science Center and Hybrid nanophotonics lab at ITMO University (Russia).
Week 2 HW: DNA read, write, and edit
Part 1: Benchling & In-silico Gel Art Simulation of Lambda genome Restriction Enzyme Digestion Using the same enzymes I tried to perform Gel Art. Perhaps, columns would have been closer in real life, but this preview reminds me sunset (if look on spare place).
Assignment: Python Script for Opentrons Artwork Being a CL at Designer Cells Node, I had an opportunity for my image being printed! Here’s my code which was written with assistance of multiple AI agents, Gemini was not sufficient to correct all my mistakes. I used this Art Nouveau-ish ornament with Ginkgo leaves as a reference, manually assigning printing dots via Ronan’s website.
Week 4 HW: Protein Design Part I
Conceptual Questions How many amino acid molecules are in 500 g of meat? Average amino acid ≈ 100 Da = 100 g/mol. 500 g meat (assuming mostly protein for estimation) ≈ 500 g protein ≈ 500 g / (100 g/mol) = 5 mol amino acids. Number of molecules = 5 × 6.02×10²³ ≈ 3.0 × 10²⁴ amino acid molecules (approximate).
Week 5 HW: Protein Design Part II
SOD1 Binder Peptide Design (From Pranam) Generate Binders with PepMLM Human SOD1 sequence: MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ A4V mutant SOD1 sequence: MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ I inserted mutant sequence and set 12 as peptide length in PepMLM Google Colab code. To compare FLYRWLPSRRGG binding peptide pseudo perplexity, I added another code cell into Google Colab:
Week 6 HW: Genetic Circuits Part I: Assembly Technologies
DNA Assembly Components in Phusion High-Fidelity PCR Master Mix and their purpose Phusion High-Fidelity PCR Master Mix typically contains a high-fidelity DNA polymerase (such as Phusion polymerase), dNTPs, Mg²⁺ (often as MgCl₂), and a reaction buffer. The high-fidelity polymerase has proofreading (3’→5’ exonuclease) activity, which reduces DNA replication errors.
Week 7 HW: Genetic Circuits Part II: Neuromorphic Circuits
Part 1: Intracellular Artificial Neural Networks (IANNs) Advantages of IANNs over traditional Boolean genetic circuits Intracellular Artificial Neural Networks (IANNs) differ from traditional genetic circuits because they process information in a graded, weighted, and adaptive manner rather than using only binary ON/OFF logic.
Part A: General and Lecturer-Specific Questions Advantages of cell-free protein synthesis (CFPS) over in vivo expression Greater experimental control Open reaction environment Faster design-build-test cycles Toxic protein production Better access to non-natural chemistry Easier monitoring and automation Cases where cell-free expression is more beneficial than cell production Case 1: Toxic proteins Example:
Week 10 HW: Advanced Imaging & Measurement Technology
Final Project Aspects of the project that will be measured Several aspects of the nanoparticle-mediated plant transfection system will be measured to evaluate delivery efficiency, functional gene expression, and electrophysiological response. The primary measurable parameters include: efficiency of nanoparticle-mediated DNA delivery into plant tissues expression of fluorescent reporter proteins functional activity of Channelrhodopsin-2 electrophysiological responses after light stimulation physicochemical properties of peptide-based nanoparticles (size and charge) Additional measurements may include comparison between magnetic nanoparticles and peptide-based nanoparticles in terms of transfection efficiency and signal intensity.