Homework
Weekly homework submissions:
Week 1 HW: Principles and Practices
- 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. Inspired by the MELiSSA project (Micro-Ecological Life Support System Alternative) from ESA, this project proposes an ecosystem composed of microorganisms and higher plants using their metabolic waste products as a substrate for the next compartment. This project is designed to study the behavior of artificial ecosystems and to develop the technologies required for future regenerative life-support systems in long-duration human space missions, such as lunar bases or missions to Mars. The system comprises five different compartments, each one colonized respectively by anoxygenic thermophilic bacteria, photoheterotrophic bacteria, nitrifying bacteria, photosynthetic bacteria, higher plants, and the human crew. I would like to conceptually integrate these microorganisms and higher plants with a plasmids-based control system, through the use of reporter genes and inducible regulatory elements. This would increase the security (allowing real-time monitoring of metabolics states, for example) and predictability of the system.
week 2 HW: DNA Read, Write and Edit
Week 02 - Lecture Questions Professor Jacobson The fidelity of DNA replication is governed by DNA polymerase and its associated repair systems. The intrinsic error rate of DNA polymerase, in the absence of proofreading, is approximately 10-4 to 10-5 per nucleotide. In eukaryotes, replicative polymerases utilize 3’ —} 5’ exonuclease activity for proofreading, which enhances fidelity to an error rate of approximately 10-7. When integrated with post-replicative mismatch repair (MMR) mechanisms, the effective error rate is further optimized to roughly 10-9 to 10-10 per nucleotide.Given that the human genome comprises approximately 3.2 x 109 base pairs, replication without these multi-layered fidelity mechanisms would result in a mutational load incompatible with cellular viability. Biological systems mitigate this risk through a hierarchy of safeguards—polymerase proofreading, mismatch repair, and various DNA damage response pathways—ensuring that the mutation rate per genome remains within a range that sustains evolutionary stability and life. A typical human protein consists of approximately 300 to 400 amino acids. Due to the degeneracy of the genetic code—where 64 codons encode 20 amino acids—the theoretical number of DNA sequences capable of encoding a single protein is exceptionally high. However, functional constraints significantly restrict this theoretical diversity. Key limiting factors include:
Week 03 - Python Script for Opentrons Artwork I was not able to write the code entirely by myself. The closest I got was generating concentric circles, wich reminded me of the Argentine “Escarapela” (with the help AI). My original idea, however, was to made an Argentine Mate which I did in https://opentrons-art.rcdonovan.com/ I also did a Cherry!
Week 4 HW: Protein Design Part I
Week 04 - Part A: Conceptual Questions 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) 500 g of meat has more or less 22% of protein, so 500 g x 0.22 =110 g of protein Average amino acid ≈ 100 Daltons and 1 Dalton ≈ 1 g/mol, so 100 Da≈100 g/mol, in order to convert grams of protein to moles of amino acids
Week 5 — Protein Design Part II
Week 5 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. Using the PepMLM Colab linked from the HuggingFace PepMLM-650M model card: Generate four peptides of length 12 amino acids conditioned on the mutant SOD1 sequence. To your generated list, add the known SOD1-binding peptide FLYRWLPSRRGG for comparison. Record the perplexity scores that indicate PepMLM’s confidence in the binders. Part 2: Evaluate Binders with AlphaFold3 Navigate to the AlphaFold Server: alphafoldserver.com For each peptide, submit the mutant SOD1 sequence followed by the peptide sequence as separate chains to model the protein-peptide complex. Record the ipTM score and briefly describe where the peptide appears to bind. Does it localize near the N-terminus where A4V sits? Does it engage the β-barrel region or approach the dimer interface? Does it appear surface-bound or partially buried? In a short paragraph, describe the ipTM values you observe and whether any PepMLM-generated peptide matches or exceeds the known binder. Part 3: Evaluate Properties of Generated Peptides in the PeptiVerse Structural confidence alone is insufficient for therapeutic development. Using PeptiVerse, let’s evaluate the therapeutic properties of your peptide! For each PepMLM-generated peptide:
Week 6 — Genetic Circuits Part I: Assembly Technologies
Week 6 — Genetic Circuits Part I: Assembly Technologies 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? The Phusion High-Fidelity PCR Master Mix contains several components: Phusion DNA polymerase → a high-fidelity enzyme that synthesizes DNA with very low error rates (With a failure rate 50 times lower than Taq and 6 times lower than Pfu, these polymerases are an excellent choice for cloning and other applications requiring high fidelity), which is critical when amplifying fragments of the amilCP gene. dNTPs (deoxynucleotide triphosphates) → building blocks for new DNA strands MgCl₂ → cofactor necessary for polymerase activity Buffer system → maintains optimal pH and ionic conditions These components work together to ensure accurate and efficient DNA amplification, also Phusion DNA polymerases offer robust performance with short protocol times, even in the presence of PCR inhibitors. They generate higher yields with less enzyme than other DNA polymerases. In this protocol, the master mix is used to amplify amilCP fragments that will later be assembled using Gibson Assembly. What are some factors that determine primer annealing temperature during PCR? Primer annealing temperature depends on: Primer length → longer primers have higher melting temperatures, GC content → higher GC increases stability and raises Tm. Higher melting temperatures are caused due to stronger hydrogen bonding. In this protocol, primers include additional overhangs (20–22 bp) for Gibson Assembly, but only the binding region determines the annealing temperature. The annealing temperature is typically set a few degrees below the melting temperature (Tm) to ensure specific binding. There are two methods from this class that create linear fragments of DNA: PCR, and restriction enzyme digests. Compare and contrast these two methods, both in terms of protocol as well as when one may be preferable to use over the other. In this protocol, PCR amplify specific regions of the amilCP gene, including mutated regions in the chromophore, allowing precise control over sequence design In contrast, restriction digestion (using PvuII) is used to linearize the pUC19 plasmid backbone. PCR is more flexible and allows introduction of mutations and overlaps, while restriction digestion relies on specific enzyme recognition sites. PCR is preferable for designing new constructs, whereas digestion is useful for preparing existing plasmid backbones.
Week 07: Genetic Circuits Part 2
Assignment Part 1: Intracellular Artificial Neural Networks (IANNs) What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? Traditional genetic circuits primarily rely on Boolean logic (AND, OR, NOT gates), which results in “all-or-nothing” digital responses. Intracellular Artificial Neural Networks (IANNs) offer several distinct advantages: Non-linear Signal Integration: Unlike Boolean gates that require strict thresholds, IANNs use activation functions (like Hill functions) to process analog chemical gradients, allowing for more nuanced environmental sensing. Weighted Inputs: IANNs allow for “tunable” inputs. By varying promoter strength or ribosome binding site (RBS) efficiency, the cell can assign different weights (w) to various biological signals, prioritizing one metabolite over another. Noise Filtering: Biological environments are inherently “noisy.” The summation and thresholding architecture of a perceptron acts as a natural buffer, preventing the circuit from misfiring due to minor stochastic fluctuations in gene expression. Computational Density: A single-layer IANN can perform complex classifications that would require a much larger and more metabolically taxing combination of traditional logic gates. Describe a useful application for an IANN; include a detailed description of input/output behavior, as well as any limitations an IANN might face to achieve your goal. Application: An engineered E. coli strain that acts as a therapeutic diagnostic tool within the human gut.