Week 1 HW: Principles and Practices

1. 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.

The tool I want to develop is a Physarum-on-a-Chip environmental sensor (slime molds) that utilizes Controlled Chemotactic Gradient Arrays.

Ideas aournd: bio-inteligence, including the Memory Hack of slimemode;how does it memrozie the rounte. Even without a nervous system, they leave a trail of extracellular slime; enjoy projects training it and how deos the inetrcellualar pathways contribute to theirnetwork.

2. Next, describe one or more governance/policy goals related to ensuring that this application or tool contributes to an “ethical” future, like ensuring non-malfeasance (preventing harm). Break big goals down into two or more specific sub-goals. Below is one example framework (developed in the context of synthetic genomics) you can choose to use or adapt, or you can develop your own. The example was developed to consider policy goals of ensuring safety and security, alongside other goals, like promoting constructive uses, but you could propose other goals for example, those relating to equity or autonomy.

Because this tool integrates living organisms into computational and sensing infrastructures, ethical development requires attention to safety, ecological responsibility, transparency, and equitable use. Lab Safety -Physarum polycephalum is generally classified as Biosafety Level 1 (BSL-1). Because it is non-pathogenic and doesn’t cause disease in healthy humans, it is considered safe for most biology classrooms and general research labs

-Environmental Concern Because slime molds are highly adaptive “escape artists,” a primary goal is to prevent the accidental introduction of laboratory-optimized or potentially modified strains into local ecosystems.

Lecture Questions

1. Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy?

Error Rate is 1/10^{6 throughout 10 mS per base addition; throughput Error Rate Product Differential: ~10}8. The human genome is approximately 3.2 Gbp, and human genome is roughly 6*10^{9 base pairs. With an error rate of 10}-6, a single replication cycle would still introduce thousands of errors. The DNA polymerase enzyme proofreads to make instant corrections. The MutS Repair System identifies the errors too.

Note from class: Almost everything built in nature will have a sort of error, the error fixing mechanism is universal. -Single strand DNA is double strand’s workspace (Prof. Church)

2. How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest? For an average human protein, the coding sequence is about 1036 base pairs long. As mentioned earlier, there is a significant chance of error in translation, a random codon choices may accidentally creates a stop codon. Moreover, in the mRNA secondary structure, some sequences fold into strong hairpins and loops that disrupts the correct formation of protein.

3. What’s the most commonly used method for oligo synthesis currently? The dominant method is chemical solid-phase synthesis using phosphoramidite cycle.

4. Why is it difficult to make oligos longer than 200nt via direct synthesis? Because chemical synthesis has an error rate around 1/100 error per base, such error rate accumulate with every nucleotide addition.

5. Why can’t you make a 2000bp gene via direct oligo synthesis? Because the error rate increases exponentially as the length increases, too much errors would be created. (correction rate = 0.99^2000)

6. [Using Google & Prof. Church’s slide #4]   What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”? Arginine, Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Threonine, Tryptophan, Valine. Vertebrate animals cannot produce lysine, thus are structururally dependent on external biological systems.

7. [Given slides #2 & 4 (AA:NA and NA:NA codes)]   What code would you suggest for AA:AA interactions? NA:NA (Not sure)

8. [(Advanced students)]   Given the one paragraph abstracts for these real 2026 grant programs sketch a response to one of them or devise one of your own:

Week 3 HW: DNA Read, Write & Edit

Week 4 HW: Protein Design Part I

Part A — Conceptual Questions

1. How many molecules of amino acids are in 500 g of meat?

Assume meat is roughly 20% protein by weight. The mass of protein is:

500 × 0.20 = 100 grams of protein.

Let’s assume the average molecular weight of a protein is 100 g/mol. Therefore:

100 / 100 = 1 mole of amino acid molecules,

which equals $6.022 \times 10^{23}$ amino acid molecules.

2. Why do humans eat beef but do not become a cow?

I wish I could, but my mom and dad say no.

Our DNA is fixed at the moment the embryo is formed. During each cell replication, it follows the DNA instructions that produce our proteins and structures. When we consume protein, our digestive system breaks the long polymer chains down into their individual amino acids and turns them into nutrients that power our ribosomes. We cannot perform horizontal gene transfer (HGT) like bacteria.

3. Why are there only 20 natural amino acids?

Natural amino acids refer to the 20 standard amino acids that are encoded by the universal genetic code to build proteins. The triplet codon system provides a maximum of 64 possible combinations (4³). This system, once established early in evolution, became “frozen” and universal. It is easier to tweak an existing system than to invent a completely new one.

4. Can you make non-natural amino acids? Design some.

Yes. Synthetic biology now uses expanded genetic codes to incorporate non-canonical amino acids (ncAAs).

One strategy is to modify a standard amino acid such as lysine by attaching:

• A small, highly fluorescent organic molecule
• Connected through a long, flexible linker (e.g., a hydrocarbon chain)
• Attached to the side chain backbone

This allows proteins (such as GFP) to gain new chemical or optical properties.

5. Where did amino acids come from before life started?

They likely originated from abiotic synthesis. Prebiotic chemistry experiments (such as Miller–Urey-type reactions) demonstrate that amino acids can form from simple inorganic molecules under early Earth–like conditions — electrical discharges, UV radiation, and simple gases like CH₄, NH₃, and H₂O are sufficient to produce a variety of amino acids spontaneously.

6. If you make an α-helix using D-amino acids, what handedness would you expect?

Standard L-amino acids form right-handed α-helices. Because D-amino acids are mirror images of L-amino acids, they would naturally form left-handed α-helices to minimize steric clashes between side chains and the backbone.

7. Why are most molecular helices right-handed?

This is a consequence of biological homochirality. Life selected L-amino acids early in evolution. The most energetically favorable packing of L-amino acid side chains results in a right-handed helical twist.

If life had instead evolved using D-amino acids, biology would likely consist of a mirror world of left-handed helices.

8. Why do β-sheets tend to aggregate? What is the driving force?

β-sheets have exposed, “sticky” edges. Unlike α-helices, where hydrogen bonds are internally satisfied within the coil, β-strands expose backbone N–H and C=O groups along their sides.

The primary driving forces for aggregation are:

• Hydrogen bonding between exposed backbone groups
• The hydrophobic effect, as non-polar side chains cluster together to avoid water

9. Why do many amyloid diseases form β-sheets? Can you use them as materials?

Amyloids form β-sheets because the cross-β motif is an extremely stable, low-energy thermodynamic state. Once a protein misfolds into this structure, it can act as a template that induces other proteins to adopt the same conformation.

Materials Applications

Yes — amyloids can be useful materials. They are extremely strong (comparable to steel or silk), highly stable, and self-assembling. They are being researched for tissue engineering scaffolds and conductive biofilms

Part B — Rhodopsin Protein Analysis

Protein Selection

I selected Rhodopsin, a light-sensitive G protein-coupled receptor (GPCR) found in the rod cells of the retina. Its role in visual phototransduction converting light into a nerve signal via retinal isomerization that makes it both biologically fascinating and structurally iconic.

1. Amino Acid Sequence

Basic Properties

• Length: 221 amino acids
• Most Frequent Amino Acid: Glycine (Gly)
• Sequence: ETWWYNPSIVVHPHWREFDQVPDAVYYSLGIFIGICGIIGCGGNGIVIYLFTKTKSLQTPANMFIINLAF SDFTFSLVNGFPLMTISCFLKKWIFGFAACKVYGFIGGIFGFMSIMTMAMISIDRYNVIGRPMAASKKMS HRRAFIMIIFVWLWSVLWAIGPIFGWGAYTLEGVLCNCSFDYISRDSTTRSNILCMFILGFFGPILIIFF CYFNIVMSVSNHEKEMAAMAKRLNAKELRKAQAGANAEMRLAKISIVIVSQFLLSWSPYAVVALLAQFGP LEWVTPYAAQLPVMFAKASAIHNPMIYSVSHPKFREAISQTFPWVLTCCQFDDKETEDDKDAETEIPAGE

Sequence Homologs

• Number of homologs identified: 218

Protein Family

• Opsin family
• GPCR (G Protein-Coupled Receptor) superfamily
• Class A GPCR (Rhodopsin-like family)

2. Protein Structure (RCSB PDB)

• Resolution: 2.70 Å — good quality for a membrane protein

Structural Classification

Rhodopsin features seven transmembrane alpha-helices, characteristic of Class A GPCRs.

3. 3D Visualization (PyMol)

Secondary Structure

Predominantly alpha-helices, consistent with a 7-TM membrane protein.

Residue Distribution

• Hydrophobic residues concentrated in membrane-spanning regions
• Hydrophilic residues on extracellular and intracellular surfaces

Surface and Binding Pocket

A clear internal binding pocket accommodates retinal that makes it essential for light detection.

Part C — ML-Based Protein Design Tools

C1. Deep Mutational Scan (ESM2)

ESM2 generates a 221 × 20 mutational scan matrix giving the log-likelihood ratio of each mutation relative to wild-type.

Key patterns:

• Lys296 (retinal-binding residue) shows near-zero tolerance for mutation — K296A receives a strongly negative ΔLL score
• Transmembrane core residues are highly conserved
• Extracellular loop residues are more permissive

C1. Latent Space Analysis (UMAP)

Rhodopsin clusters alongside other Class A GPCRs (adrenergic, adenosine, muscarinic receptors), well-separated from non-GPCR 7-TM proteins.

C2. Protein Folding (ESMFold)

Wild-type: Seven-helix bundle correctly formed; high pLDDT (>80) on helices, lower (~55–65) on loops.

Mutation resilience:

• Single point mutations → structure unchanged
• K296A → fold maintained; stability is independent of chromophore linkage
• Full TM helix deletion → pLDDT drops significantly; bundle disrupted

C3. Protein Generation (ProteinMPNN)

• Sequence recovery: ~38–45% of native sequence recovered
• Lys296 has near-100% probability of being retained
• Lipid-facing residues diverge but remain hydrophobic

ESMFold of the designed sequence matches original backbone at RMSD ~2.0–2.5 Å.

Tool Justifications

Week 5 HW: Protein Design Part II

Part 1: Generate Binders with PepMLM

The original sequence of SOD1 is:

MATKAVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

Mutate the 4th amino acid A to V (A4V):

MATKVVCVLKGDGPVQGIINFEQKESNGPVKVWGSIKGLTEGLHGFHVHEFGDNTAGCTSAGPHFNPLSRKHGGPKDEERHVGDLGNVTADKDGVADVSIEDSVISLSGDHCIIGRTLVVHEKADDLGKGGNEESTKTGNAGSRLACGVIGIAQ

Generate four peptides of length 12 amino acids conditioned on the mutant SOD1 sequence:

Part 2: Evaluate Binders with AlphaFold3

We submitted each peptide paired with the mutant SOD1 (A4V) sequence to the AlphaFold Server as separate chains to model the protein–peptide complex. All runs used seed 2026616022 for reproducibility.

ipTM — interaction confidence between the two proteins (binder ↔ SOD1). Higher is better.
pTM — structural accuracy within each protein independently. Higher is better.

AlphaFold3 Prediction Results:

Structure previews:

HRY (ipTM=0.30, pTM=0.85)	WHY (ipTM=0.32, pTM=0.75)

WRV (ipTM=0.40, pTM=0.81)	WRY (ipTM=0.27, pTM=0.85)

The PAE (Predicted Aligned Error) matrix shows inter-chain confidence in the bottom-right block. Darker green = lower positional error = more confident interaction. The peptide chain corresponds to residues ~165+ in each plot.

Summary:

ipTM scores across the four PepMLM-generated peptides ranged from 0.27 (WRY) to 0.40 (WRV), all falling in the low-confidence range (ipTM < 0.5 is generally considered weak). WRVGAAAVRLKK achieved the highest ipTM of 0.40, suggesting the most confident predicted interaction with mutant SOD1 among our candidates. Visually, its peptide chain traces along the exterior β-barrel of SOD1, which is a plausible surface-accessible binding region. None of the PepMLM-generated peptides clearly localized to the N-terminus where A4V sits, suggesting they may engage peripheral surface patches rather than the mutation site directly. All four peptides showed high pTM scores (0.75–0.85), indicating that the SOD1 structure itself is predicted with high confidence regardless of peptide. Comparison to the known binder FLYRWLPSRRGG would require a separate AlphaFold3 run for a direct ipTM benchmark.

Part 3: Evaluate Properties of Generated Peptides in the PeptiVerse

Structural confidence alone is insufficient for therapeutic development. We evaluated each peptide using PeptiVerse, assessing solubility, hemolysis probability, net charge (pH 7), molecular weight, and additional properties against the A4V mutant SOD1 target. The known binder FLYRWLPSRRGG was included as a reference.

PeptiVerse Results:

PeptiVerse screenshots:

WHYYVAAAEHKK	HRYGAVVVELKK	WRVGAAAVRLKK

WRYPVTAAEWKE	FLYRWLPSRRGG (known binder)

Summary:

All four PepMLM-generated peptides and the known binder FLYRWLPSRRGG were predicted to be fully soluble (probability 1.000) and non-hemolytic, which is an encouraging baseline for therapeutic viability. Notably, binding affinity scores were unavailable in PeptiVerse without a full protein target input (“Requires protein target”), so structural comparisons from AlphaFold3 remain our primary binding reference.

The most striking difference between peptides is permeability: WRVGAAAVRLKK is the only peptide predicted to be permeable (0.914), which could be advantageous for intracellular access — relevant given that SOD1 is a cytosolic protein. Its hemolysis probability (0.036) and net charge (+3.76) are also comparable to the known binder FLYRWLPSRRGG (+2.76, hemolysis 0.047). WRYPVTAAEWKE, by contrast, carries a slight negative charge (−0.23) and the highest hemolysis probability among the four (0.182), making it less favorable.

Chosen peptide to advance: WRVGAAAVRLKK

WRVGAAAVRLKK best balances predicted therapeutic safety and functional potential. Its high membrane permeability is a key differentiator — since SOD1 operates in the cytosol, a peptide that can cross the membrane has a meaningful pharmacokinetic advantage. It is fully soluble, non-hemolytic, and has a charge profile closely resembling the known binder. Subject to confirmation of its ipTM score from AlphaFold3, it is the strongest candidate for further development.

Part 4: Generate Optimized Peptides with moPPIt

We used moPPIt (Multi-Objective Guided Discrete Flow Matching, MOG-DFM) to move from probabilistic sampling toward controlled, motif-directed peptide design. Unlike PepMLM, which conditions generation on the full target sequence, moPPIt allows explicit specification of which residues on SOD1 to target and simultaneously optimizes multiple therapeutic objectives.

Design choices:

• Target sequence: A4V mutant SOD1
• Target residues: Residues near position 4 (A4V mutation site) and the surrounding N-terminal region, which is destabilized by the mutation
• Peptide length: 12 amino acids
• Guidance enabled: Motif binding affinity, solubility, hemolysis

Part C: L-Protein Mutants

Shout out to Shitong for the reference work and pipeline that guided this section 🙏

The objective of this section is to improve the stability and auto-folding of the lysis protein (L-protein) of MS2-phage, and to identify mutations that stabilize its interaction with the chaperone protein DnaJ. This is relevant to phage therapy — a more stable L-protein improves lytic efficiency, which is critical for phages to overcome bacterial resistance.

Boltz-2: Wild-type Complex Structure Prediction

Week 6 HW: Genetic Circuits Part I

Part 1. Questions

1. Phusion High-Fidelity PCR Master Mix Components

• Phusion DNA Polymerase — high-fidelity polymerase with 3′→5′ proofreading exonuclease activity; ~50× lower error rate than Taq
• dNTPs — nucleotide building blocks (dATP, dCTP, dGTP, dTTP) incorporated during strand synthesis
• HF Buffer + Mg²⁺ — provides optimal pH and ionic conditions; Mg²⁺ is an essential cofactor for polymerase activity
• Stabilizers — maintain enzyme activity during storage and reaction setup

2. Factors That Determine Primer Annealing Temperature

• GC content — G·C pairs have 3 H-bonds vs. 2 for A·T, raising T_m
• Primer length — longer primers = higher T_m
• Salt/Mg²⁺ concentration — stabilizes duplexes, increases T_m
• Primer secondary structure — hairpins or self-dimers reduce effective T_m
• Polymerase used — Phusion tolerates higher T_a than Taq; use NEB Tm Calculator for Phusion

Rule of thumb: T_a ≈ T_m of the lower-melting primer (for Phusion)

3. PCR vs. Restriction Enzyme Digest

Prefer PCR when you need to add custom overhangs/sequences, there are no convenient RE sites, or when starting from complex template (genomic DNA).

Prefer RE digest when compatible cut sites already flank your insert, you want sticky ends for ligation, or you need to linearize a vector backbone without introducing mutations.

4. Ensuring Fragments Are Appropriate for Gibson Cloning

Gibson Assembly requires 20–40 bp of overlapping sequence between adjacent fragments. To ensure compatibility:

1. Design PCR primers with 20–40 bp 5′ tails homologous to the adjacent fragment
2. Verify overlaps in silico using Benchling or Asimov Kernel — confirm correct orientation and reading frame
3. Check overlap uniqueness — overlaps that appear elsewhere in the construct cause mis-assembly
4. For RE-digested fragments — PCR-amplify and add overlaps via primers before Gibson assembly

5. How Plasmid DNA Enters E. coli During Transformation

Chemical transformation (heat shock method):

1. Cells are made competent by treatment with ice-cold CaCl₂, which destabilizes the outer membrane and allows DNA to associate with the cell surface
2. Plasmid DNA is added and incubated on ice
3. A brief heat shock at 42°C (~45 sec) creates a thermal imbalance that drives DNA through the membrane (likely via transient pores)
4. Cells recover in SOC media, then are plated on selective antibiotic plates — only transformants survive

6. Alternative Assembly Method: Golden Gate Assembly

Golden Gate Assembly uses Type IIS restriction enzymes (e.g., BsaI), which cut outside their recognition sequence, generating custom 4-bp overhangs. Because the recognition site is destroyed upon cutting, the enzyme continuously re-cuts incorrect assemblies — driving the reaction toward the correctly assembled, scarless product. Each fragment is designed so that digestion produces unique 4-bp overhangs complementary only to its intended neighbor in the assembly. Digestion and ligation happen simultaneously in one pot by cycling between 37°C (cutting) and 16°C (ligation). The final product contains no scar, no extra bases, and no remaining restriction site at the junctions. This makes it ideal for assembling many fragments in parallel, such as in pathway engineering or combinatorial library construction.

Part 2. Asimov Kernel — Genetic Constructs

Construct 1: Rhodopsin Light-Sensitive Protein

new ideas from week 7 lec: modify it to make it like an activation function

How It Should Function

The promoter turns on, the rhodopsin protein gets made, and the terminator stops it. No feedback, no regulation — just expression. The pLacI promoter drives constitutive expression of CYPR_CALVI, a light-sensitive rhodopsin protein. When the promoter is active, the cell continuously produces the rhodopsin protein. Because there is no feedback or regulation, protein levels are expected to rise steadily in the simulator. Rhodopsins are membrane proteins that respond to light, making them useful for optogenetic applications — controlling cell behavior using light.

Construct Image

Construct 2: Negative Feedback Loop

How It Should Function

This circuit makes a glowing protein (GFP) AND a repressor at the same time. The repressor builds up and eventually turns the whole circuit off. The pLacI promoter drives simultaneous expression of both GFPL_DISST (green fluorescent protein) and LacI repressor. As more LacI accumulates in the cell, it begins to bind to and repress the pLacI promoter — slowing down production of both GFP and itself. This negative feedback loop acts as an auto-regulator: GFP levels rise initially, then stabilize or decline as LacI repression kicks in. The expected simulator output is a rise-then-plateau curve for GFP concentration.

Construct Image

Construct 3: Toggle Switch

How It Should Function

This construct makes a repressor (TetR) that silences the other half of the switch. The two halves silence each other, so the cell can only be in one state at a time. This construct is one half of a classic bistable toggle switch. When pLacI is active, TetR is produced, which represses the pTet promoter in a paired construct. That paired construct produces LacI, which would repress pLacI. Because each side silences the other, the system locks into one of two stable states.

• State 1 (TetR wins): pLacI ON → TetR high → pTet OFF → LacI low → pLacI stays ON
• State 2 (LacI wins): pTet ON → LacI high → pLacI OFF → TetR low → pTet stays ON

Construct Image

Week 7 HW: Genetic Circuits Part II

Part 1. Intracellular Artificial Neural Networks

Q1. Advantages of IANNs over Traditional Boolean Genetic Circuits

A traditional genetic circuit works like a panel of on‑off light switches. Each gene is either fully expressed or completely silent, and the circuit’s output is a strict Boolean function of those binary inputs. An IANN, by contrast, behaves more like a set of dimmer switches connected through a mixing board. Each input can take any value within a continuous range, the connections have adjustable weights, and the final output is a smooth, graded signal instead of a hard 0 or 1.

This difference brings several benefits. Because IANNs are built from sequestrons that process signals in the analog domain, they can represent and compute with concentrations across a wide dynamic range. Boolean circuits squeeze all that richness into just two bins, but IANNs preserve it. A large enough IANN can in principle approximate any input‑output function, which is the biological version of the universal approximation theorem from machine learning. IANNs are also compact and scalable. Instead of layering many different logic gates, each with its own set of genetic parts, they use a single repeatable building block called a sequestron. The weights are set simply by adjusting DNA concentrations, so adding complexity means adding more copies of the same module rather than inventing new gate designs. Tuning the weights is like turning knobs on a mixing board: you change the ratio of plasmids, and the circuit’s behavior changes without needing to redesign any genetic parts. Finally, IANNs degrade gracefully. A small disturbance in the input causes only a small change in the output. Boolean circuits, on the other hand, can flip from the correct answer to the wrong one because of a tiny fluctuation near the switching threshold.

Q2. Applications

IANN could be designed to detect early tumor urinary tumor DNA (utDNA) in dogs by using CRISPR‑based DNA sensors that convert the presence of tumor‑specific mutations into transcriptional inputs for the IANN.

Pre-processing (in vitro): Three CRISPR-Cas13a sensors, each with mutation-specific crRNAs, detect BRAF V595E, TP53 mutations, and aberrant methylation in urine cfDNA. The collateral cleavage activity de-represses synthetic promoters proportionally to how much mutant DNA is present — converting molecular detection into analog transcriptional signals.

Computation (in vivo, HEK293 cells): A two-layer IANN built from sequestrons receives those three graded promoter signals as endoribonuclease inputs. Layer 1 integrates the BRAF and TP53 channels; Layer 2 combines Layer 1’s output with the methylation signal to produce a final weighted decision.

Output: mCherry fluorescence intensity acts as a continuous “cancer probability score” — low for healthy, moderate for single-mutation/early-stage, high for multi-mutation/advanced disease.

The analogy throughout is a team of sniffer dogs reporting to a handler — each dog gives a graded intensity signal for its specific scent (mutation), and the handler weighs them all to decide whether to raise the alarm.

The limitations section covers the real practical hurdles: sensitivity floor of CRISPR sensors for dilute utDNA, the sensor-to-cell interface challenge, transfection variability, the 650 ng DNA budget, temporal lag, incomplete mutation panels, gaps in canine methylome data, and lack of tissue-of-origin discrimination.

CRISPR-to-IANN canine utDNA detection system

Figure 1: Early canine tumor detection via CRISPR-to-IANN biosensor. Urine cfDNA is amplified and split across three CRISPR-Cas13a sensors targeting BRAF V595E, TP53 mutations, and methylation signatures. Each sensor de-represses a promoter proportionally to mutant utDNA concentration. Inside HEK293 cells, these analog signals feed a two-layer IANN built from sequestrons. The mCherry fluorescence output serves as a continuous cancer probability score.

Q3 Single-layer intracellular perceptron

Figure 2: Single-layer intracellular perceptron. X₁ is DNA encoding the Csy4 endoribonuclease; X₂ is DNA encoding a fluorescent protein whose mRNA is regulated by Csy4. Tx: transcription; Tl: translation. The dashed circle represents the sequestron, where Csy4 (−) cleaves the fluorescent protein mRNA (+), and surviving mRNA is translated into the output Y.

Part 2. Fungal Materials

1.What are some examples of existing fungal materials and what are they used for? What are their advantages and disadvantages over traditional counterparts?

Mycelium packaging, such as Ecovative’s Mushroom Packaging, is made by growing mycelium on agricultural waste inside a mold. After a few days the material is heat‑killed and dried, producing a rigid, lightweight foam that can replace Styrofoam for protective packaging. It is completely biodegradable, grows on waste feedstocks, and uses little energy to manufacture. However, it has lower compressive strength than Styrofoam, is sensitive to moisture, and is slower to produce at scale.

Mycelium leather, like Bolt Threads’ Mylo and MycoWorks’ Reishi, is grown as a pure sheet in controlled fermentation, then tanned and finished much like animal leather. It is used in fashion and accessories. It requires no animal farming and has a much lower water and land footprint, and its thickness and texture can be tuned. On the downside, it is still expensive at small scale, its durability and aging properties are still being improved, and it needs chemical post‑processing to match the flexibility of animal leather.

Mycelium‑based building insulation is grown on straw or wood shavings and can be used as thermal and acoustic panels. It has heat insulation similar to synthetic foams and is naturally fire‑retardant. It is non‑toxic, sequesters carbon, and resists fire better than petroleum‑based foams. But it is not strong enough for structural uses and can degrade if it gets wet.

Mycoprotein foods like Quorn and Meati come from fermenting filamentous fungi to make high‑protein, fibrous biomass that feels like meat. These products are high in protein, have a complete amino acid profile, and produce far fewer greenhouse gases than animal farming. Still, some people are allergic, the feedstocks are sugar‑based, and the taste and texture are not yet exactly the same as real meat.

Mycelium automotive parts were explored by Ford and Ecovative for interior pieces like dashboards, door panels, and seat cushions, taking advantage of the material’s sound absorption and impact resistance. They are lightweight, need no adhesive because the mycelium acts as the binder, and can be composted at the end of their life. However, they are sensitive to water, there is not yet much data on long‑term durability, and they are not yet cost‑competitive with synthetic foams at automotive scale.

2. What might you want to genetically engineer fungi to do and why? What are the advantages of doing synthetic biology in fungi as opposed to bacteria?

Bacteria are like bicycles: fast, simple, and cheap, good for making small proteins and simple chemicals. Fungi are like trucks. They are eukaryotes, so they have the cellular machinery to fold complex proteins, add post‑translational modifications, and build intricate three‑dimensional structures that bacteria simply cannot make. One can make mycelium materials that are naturally stronger, more flexible, or more water‑resistant without needing chemical post‑processing. You can also make fungi produce high‑value proteins while they are growing. They could secrete antimicrobial peptides, fire‑retardant proteins, or pigments directly into the material, creating functional composites in a single step. You could create self‑healing materials by engineering dormant spores into dried mycelium composites that reactivate when water enters a crack, similar to how skin heals. You could also enhance bioremediation by engineering white‑rot fungi to produce extra versions of the enzymes that break down plastics, pesticides, or industrial dyes.

Compared to bacteria, fungi have many advantages for synthetic biology. They fold and modify proteins correctly because they have an endoplasmic reticulum and Golgi apparatus, which bacteria lack. Their hyphal growth lets them naturally form sheets, foams, and composites; bacteria only grow as single cells in liquid. Fungi can grow on cheap, unprocessed plant waste because they secrete powerful enzymes to break down cellulose and lignin, while most bacteria need processed sugars. Their eukaryotic compartments let them keep toxic intermediates separate and run incompatible pathways at the same time, which helps them make complex natural products. Genetic tools like CRISPR‑Cas9, promoter libraries, and selectable markers are now well developed in model fungi, making engineering much easier. And because fungi are multicellular, they can form different tissue types such as aerial hyphae or fruiting bodies, opening up possibilities for spatial organization and layered material architectures that are impossible with single‑celled bacteria.

Week 9 HW: Cell Free Systems

##Part 1 1.Advantages of Cell-Free Over In Vivo Expression Cell-free protein synthesis (CFPS) removes the cell as a “black box” and allows you directly control and observe every variable in real time: pH, redox potential, ionic strength, and cofactor concentration.

2.Main Components and Their Roles

3.Energy Provision and ATP Regeneration

Translation is enormously ATP-hungry: every peptide bond costs ~4 high-energy phosphate bonds. In a tube, the initial ATP pool depletes within 30–60 minutes, stalling ribosomes and collapsing yield.

Phosphocreatine / Creatine Kinase System

ADP + phosphocreatine → ATP + creatine (catalyzed by creatine kinase)

• Add phosphocreatine (~20 mM) and creatine kinase (~0.5 mg/mL).
• Extends productive reaction time from ~1 hour to 3–6 hours.
• Alternative: Maltose/maltodextrin system — a multi-enzyme cascade mimicking glycolysis, cheaper for large-scale reactions.

4.Prokaryotic vs. Eukaryotic Cell-Free Systems

• Prokaryotic Choice — T7 RNA Polymerase: Straightforward cytosolic protein, no PTMs needed, high yield required.
• Eukaryotic Choice — Erythropoietin (EPO): Requires N-linked glycosylation for proper folding. A prokaryotic system would produce misfolded, inactive protein.

Designing Cell-Free Expression of a Membrane Protein

Membrane proteins are hydrophobic — without a lipid bilayer, they aggregate instantly.

Three Solubilization Strategies

1. Detergent micelles (DDM, digitonin) — Simplest; add directly to reaction.
2. Nanodiscs — Pre-assembled lipid bilayer discs; co-translate so protein inserts immediately.
3. Liposomes — Lipid vesicles that capture the protein as it emerges from the ribosome.

##Part 2 Synthetic Minimal Cell: Gut Microbiome Inflammation Sensor

1. Function

1a. What It Does — Input and Output

A liposome-based synthetic cell that detects elevated reactive oxygen species (ROS) in the gut lumen — a molecular signature of intestinal inflammation — and responds by producing and releasing butyrate, a short-chain fatty acid that suppresses NF-κB signaling and restores epithelial barrier integrity.

Analogy: Think of it like a smoke detector hardwired to a fire sprinkler — the same signal that trips the alarm also triggers the response, with no human intervention needed. The synthetic cell is silent in a healthy gut and active only when and where inflammation occurs.

1b. Could Cell-Free Tx/Tl Alone Do This Without Encapsulation?

No. There are three primary reasons:

1. Directionality: Without a membrane boundary, butyrate produced freely in solution would diffuse away immediately with no directional delivery to the epithelium.
2. Protection: The ROS-sensing gene circuit would be exposed to gut proteases and nucleases, degrading within minutes.
3. Threshold Control: There is no mechanism for threshold-gated release — the entire reaction would fire at once rather than responding proportionally to local ROS concentration.

1c. Could a Genetically Modified Natural Cell Do This?

Partially — but with serious limitations compared to a synthetic liposome:

1d. Desired Outcome

A synthetic cell administered orally (enteric-coated capsule) that survives transit to the colon, remains transcriptionally silent in healthy tissue, and activates butyrate synthesis specifically at inflamed foci where $H_2O_2$ exceeds threshold (~50 µM).

2. Component Design

2a. Membrane Composition

The membrane is designed to survive the harsh gut environment (low pH, bile salts) while remaining functional at 37°C.

2b. Encapsulated Contents

The “cytoplasm” of the synthetic cell contains the following:

• Tx/Tl Machinery: E. coli cell-free extract (ribosomes, tRNA, chaperones), T7 RNA Polymerase, and the OxyR-responsive promoter plasmid.
• Pre-loaded Enzymes: Acetyl-CoA acetyltransferase (ThlA) for fast initial response.
• Small Molecules: Acetyl-CoA (2 mM), Phosphocreatine (20 mM) + creatine kinase (0.5 mg/mL), all 20 amino acids (5 mM each), and essential cofactors (NAD⁺/NADH, CoA).

2c. Tx/Tl System Origin: Bacterial (E. coli)

A prokaryotic system is preferred because the OxyR transcription factor and the butyrate synthesis enzymes (from Clostridium) are natively bacterial. No complex post-translational modifications (PTMs) are required, making the high-yield E. coli extract the most efficient choice.

2d. Communication with the Environment

• Sensing (IN — Passive): $H_2O_2$ crosses lipid bilayers freely via passive diffusion. Inside, it oxidizes OxyR, switching it from a repressor to an activator.
• Secretion (OUT — Active): We express VDAC-1 (voltage-dependent anion channel 1). While butyrate is anionic at physiological pH, the expressed VDAC-1 pores permit rapid efflux.

3. Experimental Details

3a. Complete Genes and Lipids

3b. Measuring System Function

Validation is performed through a tiered strategy:

1. Tier 1: ROS-responsive expression: Use a GFP reporter to confirm the OxyR circuit activates at the ~50 µM $H_2O_2$ threshold.
2. Tier 2: Butyrate synthesis: Quantify butyrate production in bulk extract using GC-MS or enzymatic assays.
3. Tier 3: Pore function: Use ANTS/DPX dye efflux assays to confirm VDAC-1 correctly inserts into the liposome membrane.
4. Tier 4: Integrated function: Measure butyrate secretion from encapsulated cells in simulated healthy (5 µM $H_2O_2$) vs. inflamed (100 µM $H_2O_2$) conditions.
5. Tier 5: Bioactivity: Apply the output to Caco-2 cells and measure the reduction in inflammatory markers (IL-8/NF-κB).

##Part 3. Aura-Weave (Smart Medical Wearables)

1. One-Sentence Summary Pitch

Aura-Weave is a smart, disposable textile liner for adult incontinence garments that uses freeze-dried cell-free extracts to seamlessly detect and visually report urinary tract infections (UTIs) by changing color when exposed to infected urine.

2. How the Idea Works in Detail

The Aura-Weave liner features a middle diagnostic layer composed of a highly absorbent cellulose matrix (similar to filter paper). This matrix is pre-loaded with a lyophilized (freeze-dried) cell-free extract (CFE), specific engineered DNA circuits, and pH buffers.

The Mechanism:

1. Dormancy: The system remains completely inactive on the shelf in its dry state.
2. Activation: When the wearer voids urine, the warm liquid acts as the natural rehydration trigger, “booting up” the biological transcription and translation machinery.
3. Detection: If specific UTI biomarkers are present—such as nitrites, leukocyte esterase, or bacterial quorum-sensing molecules—the genetic circuit is triggered.
4. Visual Output: The circuit drives the rapid expression of a vibrant chromoprotein (like AmilCP). Within 45 to 60 minutes, a clear blue warning symbol permeates to the outer visible edge of the textile.

Visual Indicator: A blue symbol or color change alerts the caregiver immediately without requiring a manual diagnostic test.

3. Societal Challenge and Market Need

This addresses the “silent crisis” of UTIs in elderly, bedridden, and cognitively impaired populations (such as those with Alzheimer’s or dementia).

• Communication Barriers: These patients often cannot communicate early symptoms like pain or urgency, leading to delayed diagnosis.
• Medical Risks: Late-stage UTIs frequently progress to severe kidney infections, sepsis, and costly hospitalizations.
• Non-Invasive Monitoring: Aura-Weave eliminates the difficult, messy, and stress-inducing process of collecting a clean urine sample from an uncooperative patient, allowing for continuous, passive health monitoring in nursing homes and home-care settings.

4. Addressing Cell-Free Limitations

Technical Specifications

• Target Biomarkers: Nitrites, Leukocyte Esterase, Quorum-Sensing molecules.
• Reporter Protein: AmilCP (Chromoprotein).
• Time to Result: 45–60 minutes post-activation.
• Storage Requirements: Room temperature (stabilized via Trehalose).

##Part 4

• The Challenge: Current missions detect “bricks” (organic molecules) but not “factories” (active life).
• Strategy: Use FD-CF systems to detect ATP Synthesis and 16S rRNA genes.
• Reasoning: Only active biology synthesizes ATP; abiotic chemistry (like meteorites) might have organics but no coordinated metabolism.
• Experimental Plan: Compare Mars simulant spiked with extremophiles against sterile simulant and abiotic meteorite extracts.