Homework
Weekly homework submissions:
Week 1 HW: Principles and Practices
Does the option: Option 1 Option 2 Option 3 Enhance Biosecurity • By preventing incidents 1 2 NA • By helping respond 3 NA NA Foster Lab Safety • By preventing incident 3 1 NA • By helping respond NA 1 NA Protect the environment • By preventing incidents NA 1 NA • By helping respond NA 2 NA Other considerations • Minimizing costs and burdens to stakeholders 2 2 3 • Feasibility? 2 1 3 • Not impede research 3 1 1 • Promote constructive applications 1 1 1
Homework Questions from Professor Jacobson DNA Polymerase Error Rates and the Human Genome Error Rate of Polymerase: In biological synthesis, error-correcting polymerase has an error rate of approximately $1:106$. This is significantly more accurate than raw chemical synthesis, which has an error rate of roughly $1:102$. Comparison to the Human Genome: The human genome is approximately 3 billion base pairs ($3 \times 109$) in length. At an error rate of $1:106$, copying the entire human genome would result in roughly 3,000 errors per replication cycle. How Biology Deals with the Discrepancy: Biology utilizes specific enzymatic functions to manage and correct these errors to ensure genomic integrity. This includes 3’-5’ proofreading exonuclease activity and 5’-3’ error-correcting exonuclease functions that work alongside template-dependent primer extension to identify and remove incorrect bases. Coding for Human Proteins Ways to Code for an Average Human Protein: The average human protein is 1,036 base pairs long. Because the genetic code is redundant (multiple different codons can code for the same amino acid), there are an astronomical number of possible DNA sequences that can result in the same protein sequence. The sources highlight that biology must find a balance between this codon redundancy and diversity to maintain “fabricational complexity”. Reasons Some Codes Do Not Work: In practice, many DNA sequences that technically code for the correct protein are “impossible” or difficult to use for synthesis or expression due to several biological and mechanical factors: Secondary Structures: Sequences that form hairpins or inverted repeats can interfere with replication and transcription machinery. Extreme GC Content: Regions with very high (≥90%) or very low (≤10%) GC content are often unstable or difficult for polymerase to navigate. Repetitive Sequences: Long terminal repeats, tandem repeats, or clusters of repeats can lead to “slippage” and errors during synthesis. Homopolymers: Long runs of an identical base (e.g., more than 30bp of A) are particularly prone to errors. RNA Cleavage and Stability: Certain nucleotide combinations may inadvertently trigger RNA cleavage rules (such as targets for RNase III), leading to the degradation of the mRNA before it can be translated. Codon Optimization: Not all redundant codons are treated equally by the cell’s translational machinery; choosing the “wrong” codons can lead to inefficient protein production. Homework Questions from Dr. LeProust
Week 2 HW: DNA read write and edit
Part 3. Chose Protein I chose glucokinase (GCK) because in my biochemistry classes I found it to be a very interesting enzyme due to its unique functions and its critical role as a glucose sensor. According to the sources, what makes this enzyme particularly fascinating is that, unlike other members of the hexokinase family, it is not inhibited by its product(glucose-6-phosphate). This allows the enzyme to remain active even when glucose is abundant in the system.
PART 1.  artistic design using the GUI LINK: https://opentrons-art.rcdonovan.com/?id=98conne30870554 PART 2. ARTICLE “An Automated Versatile Diagnostic Workflow for Infectious Disease Detection in Low-Resource Settings” DOI: https://doi.org/10.3390/mi15060708 The article highlights how implementing Opentrons for automated workflows in hospital and clinical settings helps significantly reduce turnaround times and accelerates overall logistics. By increasing sample throughput and enabling the simultaneous processing of multiple samples, the system greatly enhances operational efficiency. Furthermore, automation reduces the risk of human error inherent in manual repetitive tasks and minimizes the possibility of sample contamination or compromising the diagnostic process, ensuring more reliable results.
Week 4 HW: Protein design part 1
Part A. Conceptual Questions Why do humans eat beef but do not become a cow, or eat fish but do not become fish? This is because the genetic code acts as an algorithm that dictates how proteins are assembled specifically for each organism. When humans consume animal proteins, these are broken down into amino acids; subsequently, the body uses its own transcription and translation machinery to reorganize those amino acids according to its own DNA instructions, creating human-specific proteins rather than those of the animal consumed.
Week 5 HW: Protein Design Part II
Part A: SOD1 Binder Peptide Design (From Pranam) List peptides Index Peptide Pseudo Perplexity Notes 0 WHTSHVAAGSGG 10.870029 Generated peptide 1 AHTGVVAVFSGH 13.127205 Generated peptide 2 AHSGAVALEHGP 12.848826 Generated peptide 3 VSTVHAAVEHHG 8.987529 Generated peptide 4 FLYRWLPSRRGG — SOD1-binding peptide Index Peptide Pseudo Perplexity ipTM_score Binding Location Type 0 WHTSHVAAGSGG 10.870029 0.75 surface-bound Generated 1 AHTGVVAVFSGH 13.127205 0.68 near N-terminus Generated 2 AHSGAVALEHGP 12.848826 0.82 at a shallow pocket Generated 3 VSTVHAAVEHHG 8.987529 0.71 interface with a loop region Generated 4 FLRYWLSPSRRGG 26.569499 0.85 deeply buried pocket Known Binder Peptide 1
Week 6 HW: Genetic circuits part 1
DNA ASSEMBLY Phusion High-Fidelity PCR Master Mix Components Phusion HF PCR Master Mix is provided as a 2X stock that is diluted to a 1X concentration in the final reaction. While the sources do not list every chemical ingredient, they indicate its purpose is to amplify specific DNA sequences (such as the amilCP gene and the mUAV backbone) with high accuracy. Standard high-fidelity master mixes like Phusion typically contain: Phusion DNA Polymerase: A highly accurate enzyme with proofreading activity to minimize mutations. dNTPs (Deoxynucleotide Triphosphates): The building blocks (A, T, C, G) used to synthesize the new DNA strand. Buffer: Maintains the optimal pH and ionic strength for the enzyme. $MgCl_2$: A necessary cofactor for the DNA polymerase to function. Factors Determining Primer Annealing Temperature
Week 7 HW: Genetic Circuits Part II: Neuromorphic Circuits
Part 1 HW Intracellular Artificial Neural Networks (IANNs), also known as neuromorphic circuits, provide significant advantages over traditional genetic circuits that rely on Boolean (digital) logic: a) Advantages of IANNs Biological Substrate Compatibility:** While digital logic attempts to force binary “on/off” behavior onto cells, IANNs operate through analog computation, which is much closer to the natural language of biology. Handling Non-linear Complexity:** Biological systems naturally manage highly non-linear and complex input-output relationships that Boolean logic oversimplifies. IANNs allow for the capture of these non-linearities and non-monotonic behaviors more robustly. Precision in Decision Boundaries: Unlike digital logic, which only recognizes “high” or “low” thresholds, IANNs can be programmed to respond to specific analog relationships (for example, activating only when two inputs are equal or when a weighted combination exceeds a bias), allowing for much more exact classification of cellular states. Flexibility and Scalability: The behavior of the circuit can be adjusted simply by modifying the translation rates of the components, allowing decision boundaries to be shifted without needing to redesign the entire system. b) Useful Application: Cancer Cell Classifier A primary application for IANNs is the creation of high-precision cell classifiers for cancer immunotherapy. Because there is rarely a single “magic” biomarker to distinguish a cancer cell from a healthy one, a sophisticated program is required to evaluate multiple signals simultaneously.