Homework
Weekly homework submissions:
Week 1 HW: Principles and Practices
Part 1: Class Assignment 1. “The Big Idea” In the world of Big Farm, nutrient pollution is a big problem, particularly near farms where fertilizers and manure release excess phosphorus and nitrogen into the environment. This leads to issues like eutrophication, dead zones, and human health impacts. This also leads to losses in other industries such as fishing or recreational activity. Paradoxically, we also frequently see cases of nutrient depletion, particularly in the context of agriculture. Monocropping and poor agricultural practices has led to the depletion of topsoil, making it one of the scarcest resources in the world. According to the UN Food and Agricultural Organization, 90% of our world’s topsoil is at risk by 2050. To combat this, I’m interested in seeing if a circular nutrient economy is possible:
Week 2 HW: DNA READ, WRITE & EDIT
PART 0: BASICS I have attended all lectures and recitation necessary to prepare for this week. PART 1: GEL ART & BENCHLING I made my free account on Benchling, following Ice’s tutorial in class on Lambda DNA. I then played around with Ronan’s website and Benchling’s Digest feature to try to come up with something I liked. Ultimately, I came up with something that looks vaguely like a lucky cat (if you squint).
Part 1: Python Script Despite having taking 6.100A, I am still not very adept with Python. I am even less skilled with Google Colab, so I had no choice but to use the GUI to generate my Python script. I produced this beautiful piece of art: I also created this one:
Week 4 HW: Protein Design Part I
Part 1: Conceptual Questions Assuming protein mass of meat is 20%, 0.20 x 500=100g of actual protein in this meat. If average residue is roughly 100 Da, I can probably assume that’s roughly 100g/mol, meaning I now have 1 mol of residue. In one mole, there’s roughly 6 x 1023 molecules. Thus, there are roughly 6 x 1023 molecules of amino acids in 500 grams of meat We digest these proteins, not incorporate them into our bodies. We rebuild our own proteins using molecular tools in our body, that may or may not come from what we eat. There are only 20 “natural” amino acids because that’s what biology standardized our amino acids as. These amino acids have good chemical diversity, synthetic accessibility and different constraints. Now that we have evolutionarily reached this place, it is difficult to incorporate more without rewiring our whole system. As to why there’s only 20 versus like 100, this could be because if there were too many, it would be rather costly and inefficient, so it’s better to keep our biological systems simple. Yes, noncanonical amino acids are frequently made, and they can also probably be genetically encoded as well. For example, fluoroleucine or p-iodo-phenylalanine. Before enzymes & life, amino acids came from multiple plausible sources. For example, strecker synthsis in watery environments, delivery by meteorites (they were found in chondrites), or even atomsphere/UV activity. They are left handed Yes, proteins contain other helical motiefs like π-helix, collagen triple helix, β-helix / solenoid-like helices. New helical patterns can be found by analyzing high resolution structures. Most molecular helices are right hanaded because of chirality bias in L-amino acids. Another reason could be that right-handed packing avoids clashes and are better for L residues in general (so as a result of sterics). They have sticky edges where backbone hydrogen bond donors/acceptors are exposed at the sheet edges, and adding on another strand would satisfy H-bonds. This allows β-sheets to extend into larger assemblies easily. Drivers include bacbkone hydrogen bonding, hydrophobic effect and shape complentarity. Growth is probably also kinetically favorable. Amyloid diseases tend to form β-sheets because the cross-β amyyloid architecture is very stable and can form many sequences once misfolded. This ultimately templates further misfolding and thus createspersistent aggregates that disrupt cells. Yes, they are often found in nanofibers, hydrogels, templates for mineralization, etc. A simple, reliable design is a β-hairpin that self-assembles with controlled registry: Design rules:
Week 5 HW: Protein Design Part II
Part 1: SOD1 Binder Peptide Design Part A: The retrieved SOD1 sequence is: MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ Upon introducing the A4V Mutation, we get: MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ The following amino acids were generated with their subsequent perplexity scores:
Week 6 HW: Genetic Circuits Part I
Part 1: Homework Questions Phusion High-Fidelity PCR Master Mix contains several components required for DNA amplification. The Phusion DNA polymerase is the enzyme that synthesizes new DNA strands; it has proofreading (3’→5’ exonuclease) activity, which greatly reduces mutation rates compared with standard Taq polymerase. The mix also includes dNTPs (deoxynucleotide triphosphates), which are the nucleotide building blocks incorporated into the newly synthesized DNA. A reaction buffer provides the correct chemical environment (pH, salts, stabilizers) to maximize enzyme activity and fidelity. Mg²⁺ ions (usually from MgCl₂) act as essential cofactors for polymerase function and influence enzyme efficiency. Primer annealing temperature is mainly determined by the melting temperature (Tm) of the primers, which depends on their sequence composition. Primers with higher GC content generally have higher Tm because G–C pairs form three hydrogen bonds compared with two in A–T pairs. Primer length also affects Tm, as longer primers form more stable duplexes with the template. Salt concentration and Mg²⁺ levels in the reaction buffer influence DNA duplex stability and therefore the optimal annealing temperature. Additionally, primer secondary structure or mismatches can reduce binding stability and may require lower annealing temperatures. PCR and restriction enzyme digestion both produce linear DNA fragments but operate through different mechanisms. PCR uses primers and a DNA polymerase to amplify a specific DNA region from a template through thermal cycling. This method is highly flexible because primers can introduce mutations, overhangs, or homologous regions, making PCR useful when generating fragments for cloning or modifying sequences. In contrast, restriction enzyme digestion uses enzymes that recognize specific short DNA sequences and cut at those sites, producing predictable fragments with defined ends (often sticky or blunt). The digest protocol is simpler and faster if the required restriction sites already exist in the DNA. PCR is preferable when amplifying small regions, adding sequences, or working from low DNA amounts, while restriction digests are preferable when cutting large plasmids or isolating fragments with existing restriction sites without introducing polymerase errors. For Gibson Assembly, DNA fragments must have overlapping homologous sequences at their ends so they can anneal during the assembly reaction. These overlaps are usually designed into PCR primers, ensuring that adjacent fragments share complementary sequences. After PCR amplification or digestion, the fragments should be checked by gel electrophoresis to confirm the correct size and purity. It is also important to verify that the overlaps match the intended assembly order and that no incompatible restriction sites remain within the overlaps. Golden Gate Assembly is a DNA cloning method that uses Type IIS restriction enzymes and DNA ligase in a single reaction to assemble multiple DNA fragments in a defined order. Unlike standard restriction enzymes, Type IIS enzymes cut outside their recognition site, producing custom overhangs that can be designed to be unique for each fragment. During the reaction, the restriction enzyme cuts the DNA to create compatible overhangs, and DNA ligase simultaneously joins the fragments together. Because the recognition sites are removed after ligation, the final assembled product cannot be cut again, allowing the reaction to proceed efficiently toward the correct construct. To model this, I just chose two random sequences (LACMG that we worked with once, and a gibberish one that I have for some reason). I went to Benchling to model the assembly, and this is what came out of it:
Week 7 HW: Genetic Circuits Part II
Part 1: Homework Questions IANNs allow cells to perform analog, weighted, decision-making rather than simple binary logic. Traditional genetic circuits usually implement Boolean gates, where inputs are treaed as on/ofof signals and outputs are discrete. In contrast, IANNS use components whose activities can vary continuously, allowing inputs to contribute different weights to a final output. This allows cells to integrate multiple signals simultaneously, filter noise and produce grade responses. IANNs overall can scale more easily to complex behaviors, making them better suited for biological environments with continuos noisy signals. A useful application of IANN would be a smart probioitic diagnostic cell that detects complex disease states in the gut. Inputs: The circuit could receive several molecular signals associatied with inflammation, such as nitric oxide levels, reactive oxygen species or other responsive promoters. Each input drives production of regulators that act with different weights on the expression of a reporter gene. Processing: Each regulator modifies the stability or translation of the reporter mRNA. If the combined signal exceeds a threshold, the cell expresses a fluorescent protein or therapeutic molecule. This allows the cell to classify complex physiological states, rather than triggering on a single biomarker that might fluctuate naturally. Output: Low combined signal → little or no reporter expression. Moderate signal → weak expression. High combined signal → strong reporter or drug release. Limitations: There are several constraints that could limit implementation. For example, gene expression fluctuations can distort weights and thresholds, making outputs inconsistent. Promoters and translation systems may saturate, preventing precise analog weighting. Large networks can slow cell grwoth or destabilize circuits. Furhtermore, large networks could slow cell growth or dsetabilize circuits and tuning these weights rqequires iterative experimental optimization. stuff Part 2: Fungal Materials Several commercial materials are made from fungal mycelium. One example is mycelium-based packaging produced by Ecovative, which grows fungal mycelium through agricultural waste to create molded protective packaging that replaces polystyrene foam. Mycelium composites are also used for insulation panels and structural building materials, such as mycelium bricks and boards that can be grown into shape. Another emerging product is mycelium leather, developed by companies like MycoWorks and Bolt Threads, which produces flexible sheet materials that mimic animal leather for fashion products. These fungal materials offer several advantages over traditional materials. They are renewable and biodegradable, can be grown from agricultural waste, and require much lower energy input than plastics or synthetic foams. Mycelium materials can also be grown directly into molds, reducing manufacturing steps and waste. However, they also have disadvantages: mechanical strength and durability are generally lower than plastics or synthetic composites, they can be sensitive to moisture, and scaling production with consistent material properties remains challenging. One useful direction would be engineering fungi to produce stronger or more functional mycelium materials. For example, genes could be modified to increase chitin or glucan crosslinking in the cell wall to improve stiffness and toughness of mycelium composites used in construction or packaging. Fungi could also be engineered to produce functional biomaterials, such as mycelium that incorporates conductive proteins for bioelectronics or that secretes adhesives or antimicrobial compounds. Another application could be fungi engineered to capture pollutants, such as heavy metals or microplastics, allowing grown fungal materials to act as environmental filtration systems. Fungi offer several advantages as engineering hosts compared with bacteria. Because fungi are eukaryotes, they perform complex post-translational modifications and protein folding, which are necessary for many enzymes and biomaterials that bacteria cannot produce efficiently. Filamentous fungi naturally grow large structural networks (mycelium), allowing them to form macroscopic materials without external scaffolds, something bacteria generally cannot do. Fungi also secrete large amounts of enzymes and proteins, making them good platforms for producing extracellular biomolecules or structural polymers. However, fungi are generally harder to genetically manipulate than bacteria: transformation efficiencies are lower, genetic tools are less standardized, and growth is typically slower.