Homework
Weekly homework submissions:
Week 1 HW: Principles and Practices
Class Assignment Describe a biological engineering application or tool you want to develop and why. The project aims to develop a tool to promote Parkinson’s disease phenotype manifestation in human brain organoids by controllable induction of alpha-synuclein protein expression in dopaminergic neurons. The tool is a genetic construct containing switches and regulators to produce alpha-synuclein beyond normal levels in a subpopulation of cells in patient-derived brain organoids for the investigation of patient-specific pathogenic mechanisms, pathways, and phenotypes.
Week 10 HW: Imaging and Measurement (Mass Spectrometry)
Waters Part I — Molecular Weight Theoretical molecular weight Based on the 247 aa sequence (including the initiator methionine, linker, and His-tag), the calculated average molecular weight is 28006.60 Da. Chromophore maturation (an autocatalytic post-translational modification where residues T66-Y67-G68 undergo cyclization resulting in −18.02) Da and oxidation (resulting in −2.02 Da) contribute to a 20.04 Da mass loss. The corrected predicted mass would therefore be 27,986.56 Da.
Week 11 HW: Bioproduction and Cloud Labs
Part A: The 1,536 Pixel Artwork Canvas | Collective Artwork Artwork link I contributed a pixel in the middle of the letter O in the word LOVE, but the pixel was later replaced. What I did like was that there is a chance to contribute to the automation protocol design. What I guess could be better is if we had access to the past year’s experiments (at least for the baseline concentrations) in order to estimate the variability, better plan their 3 wells, and see what changes worked for specific fluorescent proteins. Overall, I think for the problem presented as is (identify a fluorescent protein to work with and present experiments using just 3 wells, with no prior knowledge available), the nature of experiments each student proposes could reveal some interesting personality traits that characterise the HTGAA community; that would be interesting to collect and analyse this information involving those students who have a psychology background.
Week 2 HW: DNA Read, Write, and Edit
Part 1: Benchling & In-silico Gel Art This virtual digest of phage Lambda DNA was performed in Benchling. The enzymes used to process the DNA are listed below each column. Part 3: DNA Design Challenge I chose to practice designing a fluorescent-tagged human tyrosine hydroxylase (TH), relevant for my project on Parkinson’s disease. Tyrosine Hydroxylase converts tyrosine to dopamine and is an essential marker for my target cell population, dopaminergic neurons. Although in real applications, GFP under the TH promoter is used to trace dopaminergic neurons, and GFP fused to large (~56 kDa) TH can disrupt tetramerization, enzymatic activity, and folding, I chose to design a TH-GFP construct for training purposes.
Part 1: Opentrons Artwork This design was generated using the GUI at opentrons-art.rcdonovan.com and can be accessed through https://opentrons-art.rcdonovan.com/?id=1s7h4g7m1kn174o Coordinates (click to expand) mrfp1_points = [(27.5, 25.3),(25.3, 23.1),(23.1, 18.7),(20.9, 16.5),(18.7, 14.3),(36.3, 5.5),(38.5, 5.5),(27.5, 3.3),(29.7, 3.3),(31.9, 3.3),(34.1, 3.3),(36.3, 3.3),(20.9, 1.1),(23.1, 1.1),(25.3, 1.1),(16.5, -1.1),(18.7, -1.1)] mscarlet_i_points = [(29.7, 25.3),(27.5, 23.1),(29.7, 23.1),(25.3, 20.9),(27.5, 20.9),(25.3, 18.7),(23.1, 16.5),(20.9, 14.3),(16.5, 12.1),(18.7, 12.1),(16.5, 9.9),(14.3, 7.7),(38.5, 7.7),(12.1, 5.5),(34.1, 5.5),(9.9, 3.3),(38.5, 3.3),(14.3, -1.1)] electra2_points = [(31.9, 23.1),(29.7, 20.9),(31.9, 20.9),(34.1, 20.9),(27.5, 18.7),(29.7, 18.7),(31.9, 18.7),(25.3, 16.5),(27.5, 16.5),(29.7, 16.5),(23.1, 14.3),(25.3, 14.3),(27.5, 14.3),(20.9, 12.1),(23.1, 12.1),(18.7, 9.9),(20.9, 9.9),(16.5, 7.7),(18.7, 7.7),(14.3, 5.5),(16.5, 5.5),(12.1, 3.3),(9.9, 1.1),(9.9, -1.1)] mturquoise2_points = [(34.1, 18.7),(-5.5, 16.5),(31.9, 16.5),(34.1, 16.5),(36.3, 16.5),(29.7, 14.3),(31.9, 14.3),(34.1, 14.3),(25.3, 12.1),(27.5, 12.1),(29.7, 12.1),(23.1, 9.9),(25.3, 9.9),(27.5, 9.9),(20.9, 7.7),(23.1, 7.7),(1.1, 5.5),(18.7, 5.5),(14.3, 3.3),(16.5, 3.3),(12.1, 1.1),(23.1, -14.3),(7.7, -16.5),(9.9, -16.5),(12.1, -16.5),(14.3, -16.5),(-7.7, -18.7),(-5.5, -18.7),(-3.3, -18.7),(-1.1, -18.7),(1.1, -18.7),(3.3, -18.7),(5.5, -18.7),(7.7, -18.7),(9.9, -18.7),(12.1, -18.7),(16.5, -18.7),(-9.9, -20.9),(-7.7, -20.9),(-5.5, -20.9),(-3.3, -20.9),(-1.1, -20.9),(1.1, -20.9),(3.3, -20.9),(5.5, -20.9),(7.7, -20.9),(9.9, -20.9),(12.1, -20.9),(14.3, -20.9),(18.7, -20.9),(-12.1, -23.1),(-9.9, -23.1),(-7.7, -23.1),(-5.5, -23.1),(-3.3, -23.1),(-1.1, -23.1),(1.1, -23.1),(3.3, -23.1),(5.5, -23.1),(7.7, -23.1),(9.9, -23.1),(12.1, -23.1),(14.3, -23.1),(16.5, -23.1),(-14.3, -25.3),(-12.1, -25.3),(-9.9, -25.3),(-7.7, -25.3),(-5.5, -25.3),(-3.3, -25.3),(-1.1, -25.3),(1.1, -25.3),(3.3, -25.3),(5.5, -25.3),(-16.5, -27.5),(-14.3, -27.5),(-12.1, -27.5),(-9.9, -27.5)] azurite_points = [(-5.5, 14.3),(-3.3, 14.3),(-5.5, 12.1),(-1.1, 12.1),(-5.5, 9.9),(-3.3, 9.9),(-1.1, 9.9),(1.1, 9.9),(-7.7, 7.7),(-5.5, 7.7),(-3.3, 7.7),(-1.1, 7.7),(3.3, 7.7),(-7.7, 5.5),(-5.5, 5.5),(-3.3, 5.5),(-1.1, 5.5),(3.3, 5.5),(5.5, 5.5),(-7.7, 3.3),(-5.5, 3.3),(-3.3, 3.3),(-1.1, 3.3),(1.1, 3.3),(5.5, 3.3),(7.7, 3.3),(-7.7, 1.1),(-5.5, 1.1),(-3.3, 1.1),(-1.1, 1.1),(1.1, 1.1),(5.5, 1.1),(7.7, 1.1),(-9.9, -1.1),(-7.7, -1.1),(-5.5, -1.1),(-3.3, -1.1),(-1.1, -1.1),(1.1, -1.1),(3.3, -1.1),(5.5, -1.1),(7.7, -1.1),(-9.9, -3.3),(-7.7, -3.3),(-5.5, -3.3),(-3.3, -3.3),(-1.1, -3.3),(1.1, -3.3),(3.3, -3.3),(5.5, -3.3),(7.7, -3.3),(9.9, -3.3),(12.1, -3.3),(14.3, -3.3),(9.9, -5.5),(12.1, -5.5),(14.3, -5.5),(16.5, -5.5),(-12.1, -7.7),(-9.9, -7.7),(-7.7, -7.7),(-5.5, -7.7),(-3.3, -7.7),(-1.1, -7.7),(1.1, -7.7),(3.3, -7.7),(5.5, -7.7),(7.7, -7.7),(9.9, -7.7),(12.1, -7.7),(14.3, -7.7),(16.5, -7.7),(18.7, -7.7),(-12.1, -9.9),(-9.9, -9.9),(-7.7, -9.9),(-5.5, -9.9),(-3.3, -9.9),(-1.1, -9.9),(1.1, -9.9),(3.3, -9.9),(5.5, -9.9),(7.7, -9.9),(9.9, -9.9),(14.3, -9.9),(16.5, -9.9),(18.7, -9.9),(20.9, -9.9),(-12.1, -12.1),(-9.9, -12.1),(-7.7, -12.1),(-5.5, -12.1),(-3.3, -12.1),(-1.1, -12.1),(1.1, -12.1),(3.3, -12.1),(5.5, -12.1),(7.7, -12.1),(9.9, -12.1),(12.1, -12.1),(14.3, -12.1),(16.5, -12.1),(18.7, -12.1),(20.9, -12.1),(23.1, -12.1),(-12.1, -14.3),(-9.9, -14.3),(-7.7, -14.3),(-5.5, -14.3),(-3.3, -14.3),(-1.1, -14.3),(1.1, -14.3),(3.3, -14.3),(5.5, -14.3),(7.7, -14.3),(9.9, -14.3),(12.1, -14.3),(16.5, -14.3),(18.7, -14.3),(20.9, -14.3),(-12.1, -16.5),(-9.9, -16.5),(-7.7, -16.5),(-5.5, -16.5),(-3.3, -16.5),(-1.1, -16.5),(1.1, -16.5),(3.3, -16.5),(5.5, -16.5),(16.5, -16.5),(18.7, -16.5),(20.9, -16.5),(-12.1, -18.7),(-9.9, -18.7),(14.3, -18.7),(18.7, -18.7),(-14.3, -20.9),(-12.1, -20.9),(16.5, -20.9),(-14.3, -23.1),(-16.5, -25.3)] sfgfp_points = [(36.3, 14.3),(31.9, 12.1),(34.1, 12.1),(36.3, 12.1),(29.7, 9.9),(31.9, 9.9),(25.3, 7.7),(27.5, 7.7),(20.9, 5.5),(23.1, 5.5),(18.7, 3.3),(14.3, 1.1)] venus_points = [(34.1, 9.9),(36.3, 9.9),(29.7, 7.7),(25.3, 5.5),(20.9, 3.3),(16.5, 1.1)] mko2_points = [(-36.3, 12.1),(-38.5, 9.9),(-36.3, 9.9),(-34.1, 9.9),(38.5, 9.9),(-38.5, 7.7),(-36.3, 7.7),(-34.1, 7.7),(-31.9, 7.7),(31.9, 7.7),(34.1, 7.7),(36.3, 7.7),(-34.1, 5.5),(-31.9, 5.5),(-29.7, 5.5),(27.5, 5.5),(29.7, 5.5),(31.9, 5.5),(-29.7, 3.3),(-27.5, 3.3),(-25.3, 3.3),(23.1, 3.3),(25.3, 3.3),(-25.3, 1.1),(-23.1, 1.1),(-20.9, 1.1),(18.7, 1.1),(-20.9, -1.1),(-18.7, -1.1),(-16.5, -1.1),(12.1, -1.1),(-14.3, -3.3)] mjuniper_points = [(-3.3, 12.1),(1.1, 7.7),(3.3, 3.3),(3.3, 1.1),(-9.9, -5.5),(-7.7, -5.5),(-5.5, -5.5),(-3.3, -5.5),(-1.1, -5.5),(1.1, -5.5),(3.3, -5.5),(5.5, -5.5),(7.7, -5.5),(12.1, -9.9),(14.3, -14.3)] Part 2: Post-Lab Questions Although the paper is still unpublished, the paper describes a unit for drug development using brain organoids and patient-derived cells, which hasn’t been achieved before. Mainly, three automation tools are used: automated high-content imaging, automated image and PCR analysis pipelines. High-content imaging is achieved with a screening platform using 96-well plates; this way, throughput bottleneck s removed and the platform deals with variability of standard imaging. The data is processed with complementary custom image data analysis tools, created to trace specific disease hallmarks for the disease. For PCR, an automated outlier removal and relative-expression calculations are achieved through a custom Python-based algorithms. The tools allowed to have both acquisition and analysis automated and run dose-response experiments over time (identified myricetin and resveratrol as dose-dependent inhibitors of aggregate formation in a model of Parkinson’s). This is essentially the core of the drug discovery screening platform with an opportunity to scale a Parkinson’s model in human iPSC-derived neurons (typically done in rodent cells, on a smaller scale).
Part A. Conceptual Questions Q1: 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) 1 Da equals 1.66053906892(52)×10−27 kg, so 1 aa is 1.66053906892(52)×10−25 kg. The average protein fraction in meat is ~20%. Therefore, the total amount of protein is 100g. The number of aa in 100g = 0.1kg of protein is ~6×10²³ or Avogadro number, 1 mole.
Week 5: Protein design, part II
Part A: SOD1 Binder Peptide Design Part 1: Generate Binders with PepMLM Alpha-synuclein — Sequence UniProt ID: P37840 MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ A4V mutation introduced:
Week 6 HW: Genetic Circuits Part I: Assembly Technologies
DNA Assembly Components in the Phusion High-Fidelity PCR Master Mix and their purpose* The mix contains: A thermostable high-fidelity and high-speed DNA polymerase with 5´→ 3´ polymerase activity and 3´→ 5´exonuclease activity for proofreading and correcting (the enzyme was engineered so that a polymerase is fused to a small DNA-binding domain, and the domain gives the polymerase high processivity and low error rate; the exonuclease activity, unlike that of Taq polymerase, gives blunt-ended products);
Week 7 HW: Genetic Circuit Part II: Neuromorphic Circuits
Part 1: Intracellular Artificial Neural Networks (IANNs) Advantages IANNs have over traditional genetic circuits There are several advantages: 1) IANN processing uses graded values and the output is analog, just like continuous biochemical signals being graded and variable, not digital on/off; 2) adaptability of the circuit design as any function can be designed and simulated with changed weights instead of rebuilding circuit topology; 3) weights in IANN correspond directly to a real biological interactions (activation/repression of a gene); 4) IANNs more efficiently use limited cellular resources (weights are tuned instead of gates chained).
Part A: General and Lecturer-Specific Questions general questions Advantages of cell-free protein synthesis (CFPS) over in vivo methods Mainly, there is no living cell and no cell membrane involved, and so any component of the reaction can be added or removed in the course of the controlled reaction. There are fewer variables to control compared to an experiment and synthesis in a living cell. No transformation process is involved, and so the reaction is faster. Cell-free is more beneficial when a toxic protein is synthesized or a protein that incorporates non-canonical amino acids.