Homework
Weekly homework submissions:
Week 1 HW: Principles and Practices
Describe a biological engineering application or tool you want to develop and why. Virus Hunting The usage of virus hunting to discover viruses in animal populations that might become a pandemic and exploit it as a gene therapy tool. first of all the viruses are isolated from hosts of interest, then sequencing their genome, then characterize the virus. Following steps will be:
Week 2 HW: DNA Read, Write and Edit
Part 0: Attend or watch all lecture and recitation videos. 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 Create a pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artworks. I imagine the pattern as a hand making number one Part 3: DNA Design Challenge Choose your protein. I chose tau protein that it’s hyperphosphorylation is involved in Alzheimer’s disease progression I chose UniProt to get its sequence Reference: https://rest.uniprot.org/uniprotkb/P10636.fasta
Python Script for Opentrons Artwork I chose to make the egyptian beetle inspiration artistic design using the GUI link: https://opentrons-art.rcdonovan.com/?id=1xb86617h0wq061
Week 4 HW: Protein Design Pt. 1
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) Why do humans eat beef but do not become a cow, eat fish but do not become fish? Why are there only 20 natural amino acids? Ref: https://www.chemistryworld.com/features/why-are-there-20-amino-acids/3009378.article
Week 5 HW: Protein Design Pt. 2
Part A: SOD1 Binder Peptide Design (From Pranam) Pt 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. human SOD1 sequence
Week 6 HW: Genetic Circuits Pt.1
DNA Assembly What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose? The components of the Phusion High-Fidelity PCR Master Mix are the following: Phusion DNA Polymerase, incorporates nucleotides to “fill in” the gaps in the annealed DNA fragments. it is a hot-start, proofreading PCR enzyme, enabling generation of PCR amplicons with high sequence accuracy, sensitivity, and specificity. Phusion DNA Polymerase is a thermostable polymerase that possesses 5´→ 3´ polymerase activity, 3´→ 5´ exonuclease activity and will generate blunt-ended products. nucleotides: building blocks for new DNA strands during amplification. Buffer: it provides the optimal pH, ionic strength, and Mg²⁺ concentration (1.5 mM final) required for Phusion DNA polymerase to bind primers, extend DNA efficiently, and maintain its high fidelity What are some factors that determine primer annealing temperature during PCR? the specific primer annealing temperature depends on specific length and sequence of the primers. it depends also on melting temperature of the primers and therefore GC content TM = 4(G + C) + 2(A+T) 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. Restriction Enzyme Digest
Week 7 HW: Genetic Circuits Pt.2
Part 1: Intracellular Artificial Neural Networks (IANNs) What advantages do IANNs have over traditional genetic circuits, whose input/output behaviors are Boolean functions? The main advantage IANNs hold over traditional genetic circuits is scalability and the ability to support multilayer networks for complex decision-making. Traditional genetic circuits limitations include poor predictability and the struggle to reliably program multiple functions simultaneously due to inherent scalability limitations. On the other hand, ANNs have good predictability offering improved robustness for complex designs. Because of multiple layers and non-linear activations, neural networks can model complex, non-linear decision boundaries Traditional genetic circuits have input/output behaviors that function as Boolean operations. They process discrete signals (ON/OFF, high/low expression) through logic gates like AND, OR, and NOT, producing binary outputs based on truth tables. Moreover, the output layer in the ANNs producing the final prediction may be binary, multi-class or a continuous value. 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 of CNNs: tumor and MSI detection in gastrointestinal cancer Convolutional Neural Networks (CNNs) are deep learning models designed to analyze structured grid-like data such as images. the CNNs were used as automatic tumor detector to predict MSI (Microsatellite instability) that determines if the patient with gastrointestinal cancer will respond will to immunotherapy. The authors used hematoxylin and eosin (H&E)-stained histology slides as an input For tumor detection in gastrointestinal cancer, the authors trained a convolutional neural network with deep residual learning (resnet18)12 model to classify tumor versus normal tissue by transfer learning. Transfer learning means reusing a pre-trained neural network model on a new but related task, instead of training from scratch. For MSI detection, we trained another resnet18 model for each tumor type. input/output behavior Input: Tiles extracted from digitized histology slides. Output: For each tile, a probability score indicating tumor vs. normal or MSI vs. MSS status. Behavior: The neural network processes image features within each tile to generate these probability scores, enabling localized tissue characterization and subsequent patient-level molecular classification. The mentioned limitations of CNN were: Classifying ability is limited to cancer type and ethnicity in the training set. therefore, larger training cohorts are needed to boost classification performance because rare morphological variants can be learned by the network The required tissue size. To define its lower limit, they generated ‘virtual biopsies’ and found that performance plateaued at approximately 100 tiles of 256 μm edge length, suggesting that biopsies are sufficient for MSI prediction Below is a diagram depicting an intracellular single-layer perceptron where the X1 input is DNA encoding for the Csy4 endoribonuclease and the X2 input is DNA encoding for a fluorescent protein output whose mRNA is regulated by Csy4. Tx: transcription; Tl: translation. References
Part A: General and Lecturer-Specific Questions General homework questions Explain the main advantages of cell-free protein synthesis over traditional in vivo methods, specifically in terms of flexibility and control over experimental variables. Name at least two cases where cell-free expression is more beneficial than cell production. The main advantages of cell-free protein synthesis (CFPS) over traditional in vivo methods include
Part A: The 1,536 Pixel Artwork Canvas | Collective Artwork I contributed 123 pixels to the global artwork experiment by making HTGAA letters at the bottom left. I liked the collaborative work and that it represents all of us. I think it can be better by not allowing the replacement of anyone’s work. Part B: Cell-Free Protein Synthesis | Cell-Free Reagents Component Role E. coli Lysate • BL21 (DE3) Star Lysate (includes T7 RNA Polymerase) Salts/Buffer • Potassium Glutamate • HEPES-KOH pH 7.5 • Magnesium Glutamate • Potassium phosphate monobasic • Potassium phosphate dibasic Energy / Nucleotide System • Ribose • Glucose • AMP • CMP • GMP • UMP • Guanine Translation Mix (Amino Acids) • 17 Amino Acid Mix • Tyrosine • Cysteine Additives • Nicotinamide Backfill • Nuclease Free Water