Week 1 HW: Principles and Practices

What

I would like developing a Microbial Oxytocin-Sensing Network，a decentralized, “invisible” biosensing system designed for non-invasive ecological monitoring. These “living sentinels” utilize chimeric GPCRs to detect mammalian oxytocin and record signals via CRISPR-genomic storage. To maintain ecological integrity, the data is expressed through near-infrared bioluminescence, creating an interface that is sensory yet non-intrusive to the local fauna.

Why

1. Upgrading from Survival Stats to Population Resilience Traditional monitoring methods (such as infrared cameras and satellite positioning) can only tell us that the animals “are alive” or “where they are”, but cannot answer the question “Are they doing well?”. Sometimes the increase in numbers can mask social breakdown or failure in raising offspring within the population. Therefore, this technology provides “leading indicators” for predicting the long-term survival ability of the population, rather than just being a lagging indicator of population statistics. This project attempts to conduct true zero-contact monitoring by distributing sensors at the microbial level. We capture biochemical signals in the animals’ most relaxed and natural social scenarios without interfering with their natural behaviors.

2. Challenging “Genomic Essentialism”: From Frozen DNA to Dynamic Associations (Beyond Genomic Essentialism) The mainstream conservation paradigm represented by the San Diego Frozen Zoo mainly focuses on storing the “sole and independent essence” of species, that is, the static DNA sequence. This “freezing logic” simplifies species into a set of digital codes. However, life is not just the accumulation of codes; it is a dynamic relationship. When a species is separated from its social context, it has already been fragmented ontologically. And the project refuses to view species as a “static asset”. It uses synthetic biology methods to capture the transient molecule oxytocin and attempts to preserve those “physiological moments” that cannot be recorded by DNA. It emphasizes: The essence of a species exists not only in its cell nucleus, but also in the “unstable” biochemical bond between individuals.

3. Eliminating “Technical Intrusion”: As a Hidden Infrastructure for Fail-Safe In current wildlife management, mechanical noise, reflection and physical presence can disturb sensitive species, trigger stress responses, and force them to alter their natural behaviors or even abandon their offspring. This project aims to establish an entirely invisible and decentralized biological network. It blends seamlessly with the environment and does not cause any physical disturbances.

Governance / Policy Goal

1 Ensure minimal impact on non-human life and ecosystems
MOSN must never introduce biological, ecological, or behavioral harm to animals, microbial ecologies, or habitats—even indirectly or over long temporal scales. AI Prompts：How do changes in soil microorganisms affect species and ecosystems?
Sub-goal 1.1: Microbial sensors must have strict ecological boundary control (such as environmental adaptability restrictions and self-limiting reproductive design), and the genetic editing operation must not have irreversible ecological spill-over risks. Sub-goal 1.2: The monitoring process neither triggers stress responses in the animals (eliminating physical or sensory disturbances) nor interferes with the species’ natural secretion of oxytocin, social interactions, and other biochemical processes.

2 Preventing the instrumental abuse of biochemical signals and safeguarding the dynamic essence of species’ lives
MOSN must not be reduced to a tool for secretly monitoring, extracting resources from, or commercializing animal intimate behaviors, reproduction, and sociability; it is necessary to reject simplifying species as “static genetic assets” and prohibiting the binding of biochemical signal data with digital and instrumentalized species management methods. AI Prompts：the potential risks of instrumental abuse of biochemical signals
Sub-goal 2.1: Anti-tooling monitoring protection Oxytocin signals cannot be correlated with location tracking, facial recognition, tagging systems, or behavioral profiling technologies. Data access must be restricted to ecological research, conservation ethics
Sub-goal 2.2: Establish a use-purpose firewall: data cannot be repurposed beyond the originally stated ethical and epistemic aims.

3 Ensure Ethical Human–Nonhuman Interface Design
The perception and interpretation of MOSN’s output data by humans require the cultivation of responsibility, restraint, and empathy towards non-human life (rather than the desire for control, possession, or prying); the core of interface design is to “capture biochemical bonds” rather than “monitoring life conditions”, emphasizing the understanding of species dynamic relationships rather than the control of individuals. Sub-goal 3.1:Design language should emphasize listening rather than seeing or controlling through.
Sub-goal 3.2:Require periodic ethical re-evaluation as ecosystems, technologies, and social contexts interaction evolve.

Potential Governance “Actions”

1 Establish “cross-species sensory avoidance” review criteria
Executor: Environmental Regulatory Agency / Ethical Committee
Purpose: To prevent near-infrared (NIR) bioluminescence from causing behavioral disturbances in animals with specific visual ranges.
Design: Similar to the allocation of radio frequency spectrum. All MOSN deployments must pass the “redshift compliance test” before implementation. Developers must adjust the expression frequency of luciferase (Luciferase) based on the visual curves of the target species (such as rodents, reptiles, etc.) at the deployment site, ensuring that the light-emitting points are within the “visual blind zone” of that ecological niche species. AI Prompts：What design and research methods are there to prevent bioluminescence from interfering with animals?
Assumptions: We assume that we have sufficient scientific data to support the retinal sensory anatomy of all protected species in this area.
Risks/Failure: Certain insects or organisms in the deep soil may be extremely sensitive to heat or weak photons, causing changes in their migration or feeding behaviors.
The side effect of “success”: To completely avoid being detected by animal vision, it may result in extremely long signal wavelengths, which increases the technical difficulty and equipment cost for human receivers to process the data.

2 Establish a Global Stewardship Regime
Purpose：Establish a MOSN Global Stewardship Regime—a unified framework of mandatory rules, technical standards, and incentive mechanisms
Executor: Federal Regulators (e.g., EPA, EU EBA, national biotech regulatory bodies)
Design：Enact a Specialized MOSN Biotech Mandate (analogous to drone flight restriction zones) that classifies MOSN microbial sensors as “non-instrumental conservation biotech”—mandating technical compliance with signal irreducibility standards (no individual-level data capture) and banning any integration with wildlife tracking/genomic extraction technologies; approve only MOSN deployments with a certified “dynamic life purpose plan
Assumptions: Federal regulators across jurisdictions will harmonize the MOSN biotech mandate (no cross-country regulatory arbitrage for non-compliant deployments).AI Prompts：If you were part of the federal regulator, how would you assess the Risks of Failure and “Success” of MOSN?
Risks/Failure:1.Regulatory inconsistencyDifferent countries or agencies set different rules, creating loopholes where unethical MOSN projects can operate. 2.Regulators lack technical expertiseThey cannot verify whether the technology actually avoids individual-level data or tracking, so rules exist only on paper.
Risk of success：1.Standards have become uniform.The rules ignore the local ecological needs and the viewpoints of the indigenous people, resulting in the Environmental Protection Agency being compliant in form but having no effect in terms of ecology.2.Excessive standard uniformity leads to the suppression of technological innovation: The regulation “succeeded” in achieving global uniformity of MOSN technical standards and standardization of the approval process. However, in order to avoid the risk of tooling up, overly conservative technical restriction clauses were formulated.

3 Ethical Human–Nonhuman Interface Design
Purpose:Current conservation data and interfaces often reinforce human-centrism, framing non-human life as resources, subjects, or study objects. Use MOSN’s output and interface as a form of anti-anthropocentric education in the Anthropocene — to cultivate public empathy for the dynamic, relational vitality of non-human species, rather than treating them as measurable assets or controllable systems.
Executor:Interface designers + artists + ethicists + conservation biologists
Design:Co-design interfaces that prioritize listening over surveillance, using near‑infrared bioluminescence patterns and ambient expressions rather than individual tracking or control-style dashboards and by using remote information transmission and visualization tools.
Assumptions:That people can understand and connect with abstract, non‑instrumental “relational signals” without demanding clearer, more intrusive, or more “useful” data.That ethical review panels will meaningfully update standards as ecosystems, technologies, and social values change.
Risks of Failure: The interface becomes just another “monitoring tool”; ethical reviews become symbolic; data is interpreted as a way to predict or manage animals rather than respect their autonomy.
Risks of “Success”: The interface is so abstract it fails to educate; “ethical listening” becomes a branding label for projects that still treat non‑human life as a source of information or spectacle.

Recommendation and Priority

I prioritize a strategic integration of Option 1 (Cross-species Sensory Avoidance) and Option 3 (Ethical Human–Nonhuman Interface Design). My recommendation is directed toward the International Union for Conservation of Nature (IUCN) and National Environmental Regulatory Agencies (such as the EPA).
The reason why is while Option 2 (Global Stewardship) offers a necessary regulatory shell, it risks becoming a bureaucratic bottleneck that could stifle the very innovation needed for urgent conservation. By combining Options 1 and 3, we address the core ethical tension of MOSN: the paradox of observing without “intruding.”
Option 1 Cross-species Sensory Avoidance: Ensures the technology respects the “sensory sovereignty” of the fauna. It moves biosafety from “preventing human infection” to “preventing ecological disruption.”
Option 3 Ethical Human–Nonhuman Interface Design: Ensures that the data produced does not become a tool for “digital colonialism.” It forces us to engage with the wild through empathy and “listening” rather than through the lens of a control-oriented dashboard.

Trade-offs Considered

1. Innovation vs. Bureaucratic Control Option 2 (Global Stewardship) focuses on strong international regulation. This can provide legal authority and global coordination. However, it may also create slow decision-making processes and administrative delays.In urgent conservation contexts, technology needs to adapt quickly to local ecological conditions. If every development must go through heavy global approval systems, innovation may slow down. By choosing Option 1 and 3, we allow governance to happen through design principles rather than only through top-down control. This keeps flexibility while still setting ethical boundaries. Trade-off: We sacrifice some global regulatory consistency in order to maintain speed and adaptability.

2. External Oversight vs. Built-in Ethical Design Option 2 relies mainly on external monitoring and enforcement. It assumes that rules and compliance systems can prevent misuse.However, once a technology is widely distributed, enforcement becomes difficult. In some regions, regulations may be weak or politically influenced. Option 1 and 3 instead build ethical limits directly into the technology:Option 1 limits sensory disturbance biologically.Option 3 limits how much and what type of data can be accessed.This reduces the need to rely only on legal systems.

Trade-off: We accept less centralized authority in exchange for stronger built-in ethical safeguards.

In conclusion, prioritizing Option 1 and Option 3 shifts governance from external control to internal ethical design. Instead of relying mainly on international regulatory systems, ethical responsibility is embedded directly into the technology itself.
This approach reduces pressure on geopolitical systems and minimizes conflicts caused by uneven regulatory capacity between countries. When ethical limits are built into the design — such as sensory protection and restricted data access — the technology becomes less dependent on political stability or advanced enforcement infrastructure.
In this way, internal monitoring through design ethics can help bridge global inequalities in environmental governance and information technology. It creates a more resilient and adaptable framework, especially in regions where formal regulation may be slow, inconsistent, or politically sensitive.

Assumptions and Uncertainties

Assumption: I assume that “relational vitality” (social bonding) is a universally accepted indicator of ecological health that can bridge the gap between Western science and indigenous knowledge.

Uncertainty: There is a significant knowledge gap regarding the retinal anatomy of soil micro-fauna. While we can protect mammals from light pollution, we do not fully know how NIR-emitting bacteria might disrupt the behaviors of deep-soil invertebrates or fungi.

Week 10 HW: Imaging and Measurement

Homework: Final Project

1. Measurement Object (Elements to be Measured)

In this project, I will focus on measuring the following three interrelated indicators to verify the success of the conversion of heart rate data into biological signals:

A luorescence Emission Intensity: This is a core measurement item. It directly corresponds to the intensity or frequency of the heartbeat signal. By measuring the brightness of fluorescence within a unit area or volume, it verifies whether the biological system has generated corresponding visual feedback based on the input heartbeat data.

B Cell metabolic state/Optical Density (OD600) Measures the growth concentration of host cells (such as Escherichia coli) carrying heartbeat information. This is done to eliminate the variable of “increased fluorescence due to an increase in cell numbers”, ensuring that the change in fluorescence intensity is solely caused by the differences in protein expression driven by the heartbeat data.

2. Technologies & Detailed Description

A. Microplate Fluorescence Analysis Technique used: Enzyme-linked immunosorbent assay (ELISA) plate reader Detailed description: I will place the biological samples representing different heart rate profiles (such as heart rate data under different frequencies and emotional states) in a 96-well plate. Using a microplate reader, quantitative detection will be conducted at specific excitation wavelengths (such as the typical 488nm for GFP) and emission wavelengths (such as 510nm). Data processing: Obtain the relative fluorescence units (RFU) data and conduct a linear regression analysis with the original heart waveforms. If the change curve of RFU is highly correlated with the change in heart rate, it indicates that the data implantation was successful.

B. Quantitative Live-cell Imaging Techniques used: Confocal Microscopy or Fluorescence Microscopy Detailed description: If the heart rate data is designed to be spatially distributed within the biological tissue (for example, simulating the fluctuating trajectory of an electrocardiogram), I will use confocal microscopy to capture the fluorescence distribution within the sample. Operation details: Utilize the Z-stack function of the microscope for three-dimensional scanning, and measure the spatial distribution density and intensity gradient of the fluorescence.

Homework: Waters Part I — Molecular Weight

3 Answer: No. Reason: The individual isotopic peaks are not resolved in this mass spectrum for a protein of this size (≈28kDa). The spacing between isotopic peaks would be 1/z, which is approximately 0.05m/z. Due to the instrument’s resolution limit or natural peak broadening, these peaks merge into a single envelope. Therefore, the charge state cannot be determined by observing isotope spacing and must be calculated using the adjacent charge state approach.

Week 11 HW: Imaging and Measurement

Part B: Cell-Free Protein Synthesis | Cell-Free Reagents

Recording to Gemini：

Component Roles in CFPS

E. coli Lysate (BL21 (DE3) Star): Provides the essential molecular machinery, including ribosomes and chaperones, while the T7 RNA Polymerase drives the transcription of DNA into mRNA.

HEPES-KOH pH 7.5: Functions as a chemical buffer to maintain a stable pH, ensuring that enzymes and proteins remain folded and active throughout the reaction.

Potassium & Magnesium Glutamate: These salts provide the necessary ionic strength; specifically, magnesium is a critical cofactor for ribosome stability and polymerase activity.

Potassium Phosphates: Act as secondary buffers and provide a source of inorganic phosphate required for various metabolic cycles.

Ribose & Glucose: Serve as secondary energy sources that can be metabolized to regenerate ATP and provide carbon backbones for the reaction.

AMP, CMP, GMP, UMP: These are nucleoside monophosphates (NMPs) that serve as the building blocks for RNA synthesis and are phosphorylated into high-energy triphosphates (NTPs).

Guanine: A nitrogenous base that acts as a precursor for GMP, ensuring a steady supply of guanosine nucleotides.

Translation Mix (Amino Acids): Provides the 20 standard building blocks required for the ribosomes to assemble the polypeptide chain of the target protein.

Nicotinamide: Often added as a precursor for NAD +or NADP +, which are essential cofactors for the metabolic pathways that regenerate energy. Nuclease-Free Water: Acts as the solvent for the reaction, ensuring no contaminating enzymes degrade the DNA template or RNA transcripts.

Comparison of Master Mixes

The 1-hour PEP-NTP mix is designed for speed, utilizing high-energy Phosphoenolpyruvate (PEP) and pre-phosphorylated Nucleoside Triphosphates (NTPs) for immediate protein production. In contrast, the 20-hour NMP-Ribose-Glucose mix is built for sustained yield; it uses slower-metabolizing carbon sources and monophosphates (NMPs) to gradually regenerate energy, preventing the rapid inorganic phosphate buildup that typically inhibits shorter reactions.

Bonus: Transcription without GMP

Transcription can still occur because the E. coli lysate contains endogenous enzymes (such as phosphoribosyltransferases) that can salvage the Guanine base. By reacting Guanine with PRPP (phosphoribosyl pyrophosphate), the system can synthesize GMP de novo or through salvage pathways, which is then phosphorylated into the GTP required by the RNA polymerase.

Resource：https://medicoapps.org/m-purine-salvage-pathway/

Part C: Planning the Global Experiment | Cell-Free Master Mix Design

6 Fluorescent Proteins: Biophysical & Functional Properties

1.Fluorescent Protein Property Analysis recording Gemini

sfGFP (superfolder GFP): Exhibits extremely strong folding stability, enabling it to fold correctly and maintain high fluorescence intensity even under rapid synthetic pressure in complex extracellular systems (cell-free systems).

mRFP1: Its main limiting property is oxygen dependence, as chromophore formation requires molecular oxygen, which may lead to limited fluorescence signal in closed 384-well plate reactions.

mKO2: The maturation time of this protein is significantly affected by pH, maturing faster under slightly alkaline conditions, thus requiring a high buffering capacity of the extracellular reaction system.

mTurquoise2: Possesses a high quantum yield, but its folding rate remains the limiting step, making its fluorescence output highly sensitive to translational kinetics.

mScarlet_I: Exhibits strong acid sensitivity; its fluorescence intensity may significantly decrease in the later stages of the reaction when the pH drops due to the accumulation of metabolic byproducts.

Electra2: As an ultra-fast maturation protein, its core advantage lies in its extremely short maturation time, making it ideal for real-time monitoring of protein expression in extracellular systems.

2. Hypothesis for Cell-Free Optimization Since your goal is to maximize fluorescence over a long 36-hour incubation, the primary limiting factors are usually energy depletion and the degradation of the protein’s folding environment.

Protein: sfGFP (Green) Reagent(s): Magnesium Glutamate and Amino Acid Mix. Hypothesis: “By increasing the concentration of Magnesium Glutamate to optimize ribosomal activity and supplementing additional Amino Acids, I aim to extend the metabolic window of the cell-free reaction. Since sfGFP folds rapidly, providing a higher density of building blocks and enzymatic cofactors will maximize the total fluorescence yield over the full 36-hour incubation period.”

4.My Customized 2 μL Supplement Plan:

Selected Protein: sfGFP (Well #98)

Components: 1 μL Magnesium Glutamate + 1 μL Amino Acid Mix.

Reasoning: Since sfGFP is a highly robust folder, the primary limiting factor for a 36-hour incubation is the depletion of building blocks and cofactors. By doubling the available amino acids and increasing magnesium levels (which stabilizes the translation machinery), I expect to maintain a high rate of protein synthesis late into the incubation period, leading to a higher cumulative fluorescence signal compared to the standard mix.

[ { “id”: “nuclease_free_water”, “supplemental_volume_nl”: 1900 }, { “id”: “magnesium_glutamate”, “supplemental_volume_nl”: 50 }, { “id”: “aa_mix_17”, “supplemental_volume_nl”: 50 } ]

Week 2 HW：Dna Read Write and Edit

Part 1 Benchling & In-silico Gel Art

Limbda Sequence Import

Part 2 Gel Art - Restriction Digests and Gel Electrophoresis

Limbda gel electrophoresis

The style of Paul Vanouse’s Latent Figure Protocol artworks

Part 3 DNA Design Challenge

Protein choose: Oxytocin-neurophysin 1 (Prairie Vole)
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Why: I chose this protein from the prairie vole (Microtus ochrogaster) because these rodents are famous models in neuroscience for studying monogamy and social bonding. Oxytocin is the key molecular driver of their lifelong pair-bonding behavior. Sequence (FASTA):
sp|E0V840|OXYT_MICOC Oxytocin-neurophysin 1 OS=Microtus ochrogaster OX=10031 GN=Oxt PE=3 SV=1
MACPSLACCLLGLLALTSACYIQNCPLGGKRAALDLDTRKCLPCGPGGKGRCFGPNICCADELGCFVGTAEALRCQEENYLPSPCQSGQKPCGSGGRCAAAGVCCSPDGCRMDPACDPESAFSER

• I take the Oxytocin（Prairie Vole）sequence from the Uniprot

• I use bioinformatic to reverse translate the protein by bioinformatic

Reverse Translate: Protein (amino acid) sequence to DNA (nucleotide) sequence

gcgtgcgatgaatttggccatattaaactgatgaacccgcagcgcagcaccgtgtggtat taaatggcgggcccgagcctggcgtgctgctttctgggcctgctggcgctgaccagcgcg tgctatattcagaactgcccgctgggcggcaaacgcgtgctggatctggatgtgcgcacc tgcccgccgtgcggcccgggcggccgcggccgctgctttggcccgagcatttgctgcgcg gatgaactgggctgctttgtgggcaccgcggaagcgctgcgctgccaggaagaaaactat ctgccgagcccgtgccagagcggccagaaagcgtgcggcagcggcggccgctgcgcggcg ctgggcctgtgctgcagcccggatggctgccatgcggatccggcgtgcgatgcggaagcg acctttagccagcgc

Codon Optimization I have chosen Escherichia coli (E. coli) as the host organism for optimization.

Reason: E. coli is the gold standard “workhorse” in synthetic biology and biotechnology. It has a very rapid growth rate, its genetics are thoroughly understood, and it is highly cost-effective for large-scale recombinant protein production. By optimizing the Prairie Vole oxytocin sequence for E. coli, we ensure that the bacteria can efficiently “read” the instructions and produce high concentrations of the hormone for research or industrial use

Sequence：

gcgtgcgatgaatttggccatatcaagctgatgaacccgcagcgttccaccgtgtggtat taaatggcgggtccgagcctggcgtgctgcttcctgggcctgctggcgctgaccagcgcg tgctacatccagaactgcccgctgggcggcaaacgtgtgctggatctggatgtgcgcacc tgcccgccgtgcggtccgggcggtcgtggccgttgctttggcccgagcatctgctgcgcg gatgaactgggctgctttgtgggcaccgcggaagcgctgcgttgccaggaagaaaactac ctgccgagcccgtgccagagcggccagaaagcgtgcggtagcggcggtcgttgcgcggcg ctgggcctgtgctgcagcccggatggctgccatgcggatccggcgtgcgatgcggaagcg accttcagccagcgt

https://benchling.com/s/seq-8tGivOKSZJUUVEjLY7cE?m=slm-y1fK0BVnbJ3uF2UYnwf2

Part 4: Prepare a Twist DNA Synthesis Order

Build MY DNA Insert Sequence

• I imported Oxytocin-neurophysin 1 (Prairie Vole) using Benchling and created an annotation to describe each piece.

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• And then, in the Twsit, the DNA sequence was virtually combined with the vector and exported as a Genbank document.

• This is the plasmid i just built with my expression cassette included. Congratulations on building my first plasmid!

Part 5: DNA Read/Write/Edit

DNA Read
What DNA would you want to sequence (e.g., read) and why? This could be DNA related to human health (e.g. genes related to disease research), environmental monitoring (e.g., sewage waste water, biodiversity analysis), and beyond (e.g. DNA data storage, biobank).

Target DNA: The frozen cell DNA of endangered or extinct species (such as the Bucardo Pyrenean goat or the mammoth) stored in biological repositories like the San Diego Zoo’s “Frozen Zoo”. +2 Reason: These DNA molecules are not merely chemical entities; they are carriers of “Salvage Genomics”. By reading these DNA sequences, we can transform the organism into a “biological information unit”, which is the first step towards achieving “functional de-extinction”, that is, identifying the key genetic characteristics (Functional Traits) that define the species.

(ii) In lecture, a variety of sequencing technologies were mentioned. What technology or technologies would you use to perform sequencing on your DNA and why?
Is your method first-, second- or third-generation or other? How so?
What is your input? How do you prepare your input (e.g. fragmentation, adapter ligation, PCR)? List the essential steps.
What are the essential steps of your chosen sequencing technology, how does it decode the bases of your DNA sample (base calling)?
What is the output of your chosen sequencing technology?

Technology: Illumina short-read sequencing (NGS) combined with PacBio or Oxford Nanopore (long-read sequencing). Reason: Biological sample libraries (such as cells frozen for several decades) may be subject to degradation. Illumina offers high-precision base coverage, and long-read sequencing is helpful in reconstructing the complete genomic map of extinct species without a reference genome, through De Novo Assembly.

Input and Preparation Steps (Library Preparation):

The genomic DNA extracted from the Frozen Zoo samples, which have been frozen for several decades. Key steps:

Fragmentation: Break down the extracted long-chain DNA. Adapter Ligation: This process connects the specific primers required for sequencing with the sample labels. Library enrichment: The target fragment is amplified through PCR (if it is a second-generation method).

Core steps of sequencing and base identification (Base Calling):

Step: Synthesize fluorescently labeled bases on the flow cell (for sequencing by synthesis method). Base Calling: The machine captures the color of the fluorescence signal produced by each cycle, and converts it into an A, T, C, G sequence through an algorithm.

What is the output?

Output: FASTQ format file containing sequence information and quality scores

DNA Write (i) What DNA would you want to synthesize (e.g., write) and why?

Objective: To synthesize DNA fragments encoding specific functional traits of mammoths (such as cold-resistant hemoglobin genes or thick fur genes). Reason: During the process of extinction, we cannot directly “revive” the old life forms. Instead, by writing these specific sequences, we use them as “designable materials” to create a new organism capable of performing ecological functions (as Novak described it as an ecological substitute). +1

（ii) What technology or technologies would you use to perform this DNA synthesis and why?

Technology: Inkjet-based (inkjet-based) high-throughput DNA synthesis or enzymatic synthesis. Reason: The enzymatic synthesis method is more environmentally friendly and can synthesize longer DNA fragments, making it suitable for synthesizing complex and large-scale genetic circuits like that of a mammoth.

DNA Edit (i) What DNA would you want to edit and why?

Objective: Use the genome of Asian elephant stem cells as a template to remove the specific sites that do not match those of the mammoth. +2

Reason: This editing behavior exemplifies the “deconstruction of species essentialism” as mentioned in RP - species are no longer fixed boundaries, but rather a set of “programmable function libraries” that can be modified, stored, and recombined.

(ii) What technology or technologies would you use to perform these DNA edits and why?
How does your technology of choice edit DNA? What are the essential steps?
What preparation do you need to do (e.g. design steps) and what is the input (e.g. DNA template, enzymes, plasmids, primers, guides, cells) for the editing?
What are the limitations of your editing methods (if any) in terms of efficiency or precision?

What technology is being used?

Technology: CRISPR-Cas9 gene editing technology. Reason: It can precisely and efficiently perform multi-site editing within the vast elephant genome, which is the core technology currently adopted by companies like Colossal.

How to conduct editing? Key steps

Design: Design sgRNAs that specifically recognize the genetic loci of mammoth animals. Cutting: The Cas9 protein cuts the double-stranded DNA under the guidance of the gRNA. Repair: Introduce synthetic mammoth DNA template and use homologous recombination repair (HDR) to insert the new sequence into the genome.

Preparation and Input

Input: Asian elephant fibroblast cells, Cas9 protein, designed guide RNA (gRNA), and donor DNA template containing the mammoth sequence.

Limitations of Technology

Off-target effects: May accidentally damage the key survival genes in the Asian elephant genome. Efficiency issue: It is extremely difficult to carry out thousands of gene edits simultaneously within a living cell. Ethical challenges: As you stated in your report, this kind of editing blurs the boundaries between “nature” and “design”, and may give rise to entirely new and difficult-to-define “new life (Neolife)”.

Week 3 HW：Lab Automation

Python Script for Opentrons Artwork

My Inspiration

In taxonomy, Bucardo belongs to the genus Capra (goats). In my Opentrons practice, I discovered that the mosaic pattern formed by fluorescent bacteria blurred the boundaries between specific species and macroscopic classification. I am not depicting an exact sheep, but rather using the universality of the “Capra” attribute to reconstruct an image of “Resurrection for 7 Minutes”. This mosaic-like visual language is not only a numerical metaphor for Bucardo’s shattered life, but also a vague expression about ’existence and disappearance’ that transcends species classification.

Process

Firstly, I initially used the Automatic Art Interface to generate the image I desired. However, due to the color restrictions mentioned in the Colab document, I had to limit the use of the design sheep’s colors.

Since I had no coding experience, at the beginning I simply copied the coordinates and fed them to the AI along with the reference seven, in order to generate the code.

However, during the process, several errors occurred, including confusion between instructions and spaces, a situation where the machine pipette’s printing exceeded the capacity, and undefined terms such as: mko2_points and mrfp1_points.

After resolving these minor errors, when I copied the code given by the AI, there were still several errors in the generated code. I did not achieve the pattern I wanted.

After reflection, taking into account the previous mistakes, I realized that I had not clearly stated where to draw specifically. Therefore, I shifted my focus to how to make the coordinates and colors I obtained clearly expressed in the coding. After correcting the overflow error, I continued to give instructions to the AI.

Promote：Based on the code you provided, the image I generated is the first one. Please refer to the coordinates I set in the generator and help me modify the code further.

This time, the instruction, combined with the errors I had already fixed before, enabled me to generate my target image. The coding is divided into two parts. 1.Define the coordinate list 2.The drawing process.

Coding

Outcome

Post-Lab Questions

I have found two papers that I am particularly interested in.

Automation of protein crystallization scaleup via Opentrons-2 liquid handling

Description: This paper demonstrates how to scale up and automate the sitting drop protein crystallization process using the Opentrons-2 (OT-2) robot. The research focuses on using Python scripts to precisely control microliter-level liquid handling to optimize crystallization conditions for proteins such as lysozyme. Technical Highlights: The authors developed a flexible automated workflow, including: Serial Dilution: The robot can automatically configure gradients of different precipitant concentrations. Custom Hardware: To accommodate non-standard crystallization plates, the authors used 3D printing to create dedicated adapters. High Throughput and Accuracy: The system can automatically set up 24-well crystallization plates, ensuring highly consistent component proportions in each tiny droplet (approximately 4 μL).

What I intented to do

I plan to use the OT-2 automation platform to create a project titled “Crystal Architecture: Programming Microscopic Landscapes”. This project aims to leverage the self-organizing nature of protein crystallization, under artificial intervention (code), to “grow” microscopic artworks with complex geometric structures on specific biological substrates.

Automation of protein crystallization scaleup via Opentrons-2 liquid handling

Description: This paper introduces a highly integrated automated system called Piccolo, which is specifically designed to address the bottlenecks in protein production. The core innovation of this system lies in its ability to automate the entire process from cell culture, growth monitoring (OD value), induction of expression, to protein purification. Technical highlights: Unlike traditional timed operations, the Piccolo system can dynamically adjust (Reschedule) the induction time based on the real-time growth curve of cells in each 24-well bioreactor. This means that the robot can “sense” the rhythm of life and add the inducer at the most appropriate moment.

What I intented to do

This system not only supports the cultivation of Escherichia coli, but also that of insect cells (animal-derived cells). In my exploration of biological art, this provides an opportunity to view the “molecular purification” process as “artistic transformation of matter”. It demonstrates how, through precise automated control, pure biological art media with specific colors (such as fluorescent proteins) can be extracted from turbid biomass.

Final Idea

Week 4 HW: Protein design part 1

Part A. Conceptual Questions

1.Why do humans eat beef but do not become a cow, eat fish but do not become fish?

Because the human body completely breaks down exogenous proteins into free amino acids. And the DNA re-directs these amino acids to be arranged in the human sequence, rather than the sequence of cows or fish.

2.Why are there only 20 natural amino acids?

The 20 amino acids can already fulfill the basic chemical requirements such as hydrophobicity, hydrophilicity and electronegativity. Adding more amino acid types would require corresponding tRNAs and synthetases, and the evolutionary cost would be too high.

3.Can you make other non-natural amino acids? Design some new amino acids.

Possible: By expanding the genetic code, scientists have synthesized over 100 types of non-natural amino acids. For example, photosensitive amino acids: The side chains carry light-sensitive groups, allowing the proteins to be controlled by ultraviolet light.

4.Where did amino acids come from before enzymes that make them, and before life started?

Miller-Urey experiment: In the original atmospheric environment (lightning + water vapor + methane + ammonia), simple amino acids (such as glycine and alanine) can be spontaneously synthesized.

5.If you make an α-helix using D-amino acids, what handedness (right or left) would you expect?

Left-handed

6.Can you discover additional helices in proteins?

π helix: wider and flatter, often found near functional sites.

7.Why are most molecular helices right-handed?

The steric hindrance of the side chains of L-amino acids determines that they have lower energy and are more stable when forming right-handed helices.

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

Open hydrogen bond network: The peptide bonds (backbone) at the edges of β-sheets have unpaired hydrogen bond donors (-NH) and acceptors (-C=O). The main driving force: Inter-strand Hydrogen Bonding.

Physical mechanism: Since the hydrogen bonds at the edge do not get completely counteracted as in the α-helix, they act like “nylon snaps”, constantly attracting and capturing adjacent peptide chains to lower the system energy.

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

Causes of the disease: Extremely high thermodynamic stability: The β-sheet can be infinitely extended through the “cross-β” structure, forming stable fibers with extremely low energy. Error folding cascade: A misfolded β-template will induce the normal protein to also transform into β-folding, resulting in a domino-like aggregation effect, which can lead to diseases such as Alzheimer’s or Parkinson’s.

As for material application: Feasibility: Absolutely possible. Amyloid β-fibers possess astonishing mechanical strength (comparable to steel wires or silk) and biocompatibility. Usage: It has currently been applied in the design of biological nanoscaffolds, conductive nanowires, and controlled-release drug carriers.

Part B: Protein Analysis and Visualization

Protein：Green fluorescent protein

Reason：

GFP is an ideal model for studying protein “folding resilience”. Its compact “cylindrical” structure ensures that the overall structure remains stable even when there are significant mutations on the surface. As a researcher focusing on species design and life information, GFP is not only a fluorescence labeling tool but also a symbol of information visualization in synthetic biology. It demonstrates how the genetic code can directly be transformed into visible colors for the naked eye.

Sequence：

MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFSYGVQCFSRYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYK

Account：

Family：

Using the UniProt BLAST tool, I identified 205 sequence homologs for the GFP sequence (P42212). The protein is a member of the GFP family (Pfam: PF01353), characterized by its conserved structural and functional domains.

Identify the structure page of your protein in RCSB：

It was published by J AM Chem at 2006，really good quality structure only 1.2 Å

• Structure Quality: This structure was solved on April 18, 2006, using X-ray diffraction. It is of exceptional quality with a resolution of 1.20 , which is significantly better than the 2.70 
• Ligands and Molecules: Apart from the protein chain, the solved structure contains a Magnesium ion (MG) as a unique ligand, as well as several water molecules.
• Classification and Family: The protein belongs to the GFP family (Pfam: PF01353) and is classified under the GFP-like structural family. It features a distinct “light-can” fold that protects the internal chromophore.

3D molecule visualization：

Visualize the protein as “cartoon”, “ribbon” and “ball and stick”.
These structures have been particularly helpful in allowing me to understand the different structural perspectives of proteins.
Visualizing the protein in different modes allows for a comprehensive understanding of its multi-level organization. The cartoon mode highlights the global architecture (the β-barrel), the ribbon mode tracks the path of the polypeptide backbone, and the ball-and-stick model reveals the intricate atomic interactions and the precise positioning of side chains within the internal chromophore environment.

Secondary Structure：

• Observation result: In the rendered image of PyMOL (such as Secondary Structure.jpg), the protein is colored as purple β-sheet and light green α-helix.
• Structural comparison: This protein clearly possesses a significantly greater number of folding sheets.
• Detailed analysis: As a classic β-barrel structure, it is formed by 11 antiparallel β-sheet chains, enclosing a closed cylindrical shell. The α-helices are only present in the center of the barrel and in the short loops connecting the folding segments.

Hole：
The surface of GFP is highly compact with no functional pores or binding pockets. The cyan-colored indentations are simply the result of the 11 β-strands packing tightly together. This ‘molecular cage’ creates a shielded, hydrophobic core that protects the chromophore from external quenchers, ensuring stable fluorescence。

Part C. Using ML-Based Protein Design Tools

C1. Protein Language Modeling

One noticeable pattern in the heatmap is that mutations to cysteine (C) （W） across many positions show very low scores (dark purple). This suggests that introducing cysteine residues is generally unfavorable for this protein. A possible explanation is that cysteine can form disulfide bonds, which may disrupt the existing protein structure if introduced at inappropriate positions.

I was not able to clearly locate my specific sequence in the map, the overall distribution indicates that the sequences occupy a shared region of latent space, consistent with proteins that have related sequence characteristics.

C2. Protein Folding

C3. Protein Generation

Week 5 HW：Protein Design Part-ii

Part A: SOD1 Binder Peptide Design

Part 1: Generate Binders with PepMLM

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

Among the generated sequences, WRYPAAAAELKK (7.61) stands out the most. It has a lower perplexity than the other generated sequences, indicating that it may be more compatible with the pocket of the SOD1 A4V mutation site in terms of chemical environment and geometric conformation.

Part 2: Evaluate Binders with AlphaFold3

For each peptide, I submit the mutant SOD1 sequence followed by the peptide sequence as separate chains to model the protein-peptide complex. And I analysis these 4 peptide from different perspective inculding engagement of the β-barrel region,surface-bound, ipTM values ,peptide matches.

Part 3: Evaluate Properties of Generated Peptides in the PeptiVerse

1. Therapeutic Properties and Structural Observations: Comparative Analysis

By comparing the predicted data from PeptiVerse with the structural observations of AlphaFold3, we can draw the following conclusions:

Conformity between Binding Force and ipTM: Generally, short peptides with higher ipTM scores exhibit more stable interfacial interactions. In your prediction list, WRYPAAAAELKK showed the lowest perplexity score (7.61), which typically indicates stronger binding potential.

Solubility & Hemolysis:

Solubility: All candidate peptides (Binder 0-3) were predicted as Soluble (probability 1.000), which is a very desirable signal in therapeutic development.

Hemolysis: Most designs performed well and were predicted as Non-hemolytic. WRYPAAAAELKK showed an extremely low probability of hemolysis (0.005), indicating very high safety. In comparison, WLYYVVAVAWKK has a slightly higher hemolysis prediction probability (0.172), and although it is still classified as non-hemolytic, its safety profile is slightly inferior to other candidates.

The best performer in terms of attribute balance: WRYPAAAAELKK achieves the optimal balance between binding confidence, complete solubility, and extremely low risk of hemolysis.

2. Final Candidate Short Peptide Selection and Rationale

WRYPAAAAELKK

Rationale: This short peptide stood out in the PepMLM score, possessing the lowest Pseudo Perplexity (7.61), indicating the model’s strongest confidence in its binding to SOD1 (A4V). In therapeutic property prediction, it not only demonstrated perfect solubility prediction (1.000) but also exhibited excellent safety indicators, with an ultra-low hemolysis probability of 0.005, making it the most robust therapeutic candidate. Its acid-base balance (pI 9.38) and hydrophobicity index (GRAVY -0.72) also suggest good biochemical stability in the complex physiological environment within cells.

Part 4: Generate Optimized Peptides with moPPIt

Week 6 HW: Genetic Circuits Part 1

Assignment: DNA Assembly

1.What are some components in the Phusion High-Fidelity PCR Master Mix and what is their purpose?

Phusion is an enzyme commonly used for high-precision cloning. Its Master Mix typically contains the following core components:Phusion DNA Polymerase: The core enzyme. It possesses 3’→5’ exonuclease activity (proofreading function), ensuring high fidelity DNA replication with an extremely low error rate. dNTPs (dATP, dTTP, dCTP, dGTP): The “raw materials” for building new DNA strands.Mg²⁺ (usually MgCl₂): A cofactor for the polymerase. It stabilizes primer-template binding and activates the enzyme’s catalytic center. Buffer: Maintains a constant pH and provides suitable ionic strength to ensure enzyme activity.

2.What are some factors that determine primer annealing temperature during PCR?

The primer annealing temperature (Ta) is typically 3-5°C lower than the primer melting temperature (Tm). Influencing factors include:1. Primer length: Usually between 18-30 bp; longer primers generally result in higher Tm. 2.GC content: G-C base pairs have 3 hydrogen bonds, while A-T pairs only have 2. Therefore, higher GC content leads to higher Tm. 3.Salt ion concentration: The concentration of monovalent cations (such as Na+, K+) and divalent cations (Mg2+) in the reaction system neutralizes the negative charge of the DNA backbone; higher concentrations result in higher Tm.4.Mismatch degree: Imperfect matching between primers and templates significantly reduces annealing efficiency and temperature.

3.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.

Reaction Mechanism and Principle:PCR is an in vitro enzymatic amplification process that uses primers to guide polymerase to synthesize new strands de novo on a DNA template. Restriction enzyme digestion, on the other hand, is an enzymatic cleavage process that uses restriction endonucleases to recognize specific sequence sites and “cut” the DNA; it does not involve the synthesis of new DNA.

Product properties and yield: PCR can achieve exponential amplification of target fragments, obtaining a large number of DNA fragments from a very small amount of template; in contrast, restriction enzyme digestion produces fragments in a fixed ratio to the starting material, with no increment.

Design flexibility: PCR has extremely high flexibility. Researchers only need to design corresponding primers based on the target sequence to obtain DNA fragments at almost any position. In contrast, restriction enzyme digestion is strictly limited to the enzyme sites that are naturally present or artificially constructed in the DNA sequence. Without suitable recognition sites, it is impossible to perform cleavage.

Advantages, disadvantages, and accuracy: The advantages of PCR are its speed and high yield, which can quickly obtain a large amount of target DNA. However, since it is artificially synthesized, base mutations may be introduced during the process. Restriction enzyme digestion can completely maintain the accuracy of the original sequence and will not produce new mutations, but its application is highly dependent on the specific sequence background.

4.How can you ensure that the DNA sequences that you have digested and PCR-ed will be appropriate for Gibson cloning?

Designing Overlapping Regions: Adjacent fragments must have 20-40 bp of homologous overlapping sequences. These are typically added to the 5’ end of PCR primers.

End Integrity: PCR products require purification to remove excess primers and single nucleotides; restriction enzyme digestion must ensure clean cuts that conform to the overlap design.

Conflict Elimination: Ensure that the fragments do not contain extremely strong secondary structures that conflict with the Gibson reaction temperature (50°C).

5.How does the plasmid DNA enter the E. coli cells during transformation?

Competent cell treatment: Cells are treated with CaCl₂, which bridges the negatively charged DNA phosphate backbone to the equally negatively charged phospholipid layer of the cell membrane via Ca²⁺ ions.

Heat shock: The mixture is rapidly transferred from 0°C to 42°C. This abrupt temperature change creates transient “thermal pores” on the cell membrane, allowing DNA to enter the cell via an electrochemical gradient.

6.Describe another assembly method in detail (such as Golden Gate Assembly)

Golden Gate Assembly utilizes type II S restriction enzymes (such as BsaI or BsmBI) and T4 DNA ligase in the same reaction system. A characteristic of type II S enzymes is that their cleavage sites are located outside the recognition sites, allowing researchers to fully customize the resulting 4bp sticky ends. Through careful design, these recognition sites are removed after cleavage, preventing further cleavage of the product. This method can precisely assemble multiple fragments (typically more than 10) at once, and it is directional. Because the recognition sites are ultimately eliminated, it is also known as “traceless assembly.”

Assignment: Asimov Kernel

Functional Expectation Based on the logic of the negative feedback loop, it is expected that this system will not reach a steady state but will instead generate continuous periodic oscillations. When the concentration of one protein increases, it will suppress the expression of the next gene, causing the concentration of the next protein to decrease, thereby releasing the suppression on the next gene.

Simulation Results & Data Analysis

The chart shows the distinct alternating oscillation behavior of the three components (LacI, LambdaCI, TetR). There is a fixed phase difference between the peaks of each component, which proves that the delay inhibition logic in the loop is operating normally. The oscillation tends to stabilize after approximately 10 hours, maintaining a constant amplitude and frequency.

Construct 1

Based on the classic Repressilator (with three genes: LacI, LambdaCI, and TetR) circuit, I added an sfGFP expression cassette. The fourth group of components is driven by the pTetR promoter and expresses sfGFP. Since pTetR is inhibited by the TetR protein within the system, the expression of sfGFP should be able to reflect the real-time dynamic state of the oscillator.

It is expected that within the 70-hour simulation period, sfGFP will generate continuous periodic fluctuations in conjunction with LacI, LambdaCI and TetR. The simulation curve shows that the system exhibits distinct oscillatory characteristics in the first 10 hours of the simulation. However, as time progresses, the amplitude of the oscillations gradually diminishes (Damped Oscillation), and eventually reaches a stable steady state (Steady State) approximately 20 hours later.

Construct 2

Unlike the independent report branch of Plan 1, in Plan 2, the coding sequence of sfGFP is directly connected to the end of the repressor protein LacI, forming a single transcription unit: pTetR -> A1 RBS -> LacI -> sfGFP -> Terminator.

Plan 2 did not exhibit attenuation! This indicates that the way of fusing the proteins has a lesser impact on the system’s dynamic equilibrium, thus maintaining a long-term periodic behavior.

Week 7 HW: Genetic Circuits Part 1

Assignment Part 1: Intracellular Artificial Neural Networks (IANNs)

1.IANNs vs. Traditional Genetic Circuits (Boolean Functions)

Traditional genetic circuits are typically Boolean logic (0 or 1), meaning the output is only triggered when both inducers reach high concentrations simultaneously. In contrast, IANNs offer the following advantages:

Analog/Graded Response Processing: Traditional circuits are prone to abrupt changes near a threshold. IANNs can handle analog inputs, achieving “weighted summarization,” allowing cells to respond linearly or with finer nonlinear responses to subtle changes in signal strength.

High-Dimensional Integration: The intracellular environment is extremely complex. With each additional input to a Boolean gate, circuit complexity and crosstalk increase exponentially; IANNs, however, can aggregate signals through multiple “weak connections,” leveraging the robustness of neural networks to filter biological noise.

2.Potential Applications, Inputs, Outputs, and Limitations of IANNs

IANN can be used for precise multi-signal cancer detection. It takes three different concentrations of microRNAs (miRNAs) in the cell as input and outputs apoptosis-inducing proteins only when multiple indicators are abnormal at the same time. However, it has obvious limitations. Maintaining the deep network requires the expression of a large number of intermediate proteins, which puts metabolic pressure on the cell and consumes too much energy. Also, because transcription and translation of each layer takes time, the more layers there are, the more obvious the cell response delay becomes.

3.Draw a diagram for an intracellular multilayer perceptron where layer 1 outputs an endoribonuclease that regulates a fluorescent protein output in layer

This design depicts a two-layer Intracellular Artificial Neural Network (IANN) where Layer 1 (X1) produces an intermediate endoribonuclease (ERNA) that acts as a hidden inhibitory signal; this signal modulates the combined transcriptional activity of Layer 2 (X2) and the bias term (B1), collectively determining the final expression level of the fluorescent protein output (Y).

Assignment Part 2: Fungal Materials

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

The mycelium (fungal mycelium) as a practical application of sustainable and environmentally friendly packaging materials. By mixing the mycelium (the root-like structure of fungi) with agricultural waste (such as straw or rice husks) and placing it in a specific-shaped mold to grow, custom protective packaging blocks can be created. It is completely biodegradable and compostable: as an alternative to traditional plastic packaging such as Styrofoam, it can be safely returned to nature after use. Compared with other materials:

1. It is customizable: Specific molds can be used to grow packaging blocks of any shape and size as needed, to precisely cushion and protect delicate or fragile items.
2. Excellent protective performance: The mycelial network is not only sturdy but also has certain elasticity and shock absorption properties, making it highly suitable for use as protective packaging.

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?

I would like do Engineered Fungal Logic Gates for Environmental Monitoring

Objective: To genetically engineer a fungal mycelium network that functions as a biological “Logic Gate” to detect and report environmental pollution (e.g., heavy metals and pesticide runoff).

Why Fungi for this task?

Bioremediation Integration: Fungi don’t just detect pollution; they often naturally break down hydrocarbons or accumulate heavy metals. We are essentially giving a “voice” to an organism already doing the cleanup work.

Large Scale Sensing: A single fungal colony can span acres. By engineering the network, we create a “living sensor” that can monitor an entire forest floor or industrial site without needing electronic sensors or batteries.

Durability: Unlike sensitive electronic equipment, a fungal logic gate is self-healing and can survive in acidic or fluctuating soil conditions where traditional sensors might corrode.

Week 9 HW: Cell Free System

Homework Part A: General and Lecturer-Specific Questions

1.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.

Advantages:1.Flexibility and Control: You can directly adjust the reaction system, such as changing pH, ion concentration, or adding non-natural amino acids, without worrying about “killing” cells.2.Openness: The system is open; you can monitor the reaction process in real time or directly add linear DNA without constructing complex plasmids.

Applications:Synthesis of Toxic Proteins: Some proteins kill host cells but can be safely produced in a cell-free system.

2.Describe the main components of a cell-free expression system and explain the role of each component.

Crude Extract: Contains ribosomes, RNA polymerase, and translation factors (the “factories” for protein synthesis).

Energy Source: Such as phosphoenolpyruvate (PEP), providing the driving force for the reaction.

Amino Acids: The raw materials for protein synthesis.

Cofactors and Salts: Such as Mg²⁺ and K⁺, maintaining enzyme activity.

DNA Template: Contains instructions that encode the target protein.

3.Why is energy provision regeneration critical in cell-free systems? Describe a method you could use to ensure continuous ATP supply in your cell-free experiment.

Importance: Protein synthesis is an extremely energy-intensive process. If ATP is depleted, the reaction will immediately cease, and the accumulation of byproducts (such as inorganic phosphate) will inhibit the reaction.

Methods to ensure a continuous supply of ATP: Use an energy regeneration system: Add creatine phosphate and creatine kinase. Creatine phosphate continuously converts ADP back into ATP, maintaining stable energy levels.

4.Compare prokaryotic versus eukaryotic cell-free expression systems. Choose a protein to produce in each system and explain why.

1.Structure

The prokaryotic system has a very simple structure; transcription and translation occur almost simultaneously, resulting in an extremely fast reaction rate. The eukaryotic system has a more complex mechanism, better mimicking the internal environment of complex organisms.

2.Protein folding and modification capabilities

Prokaryotic systems typically lack the ability to handle complex folding. Eukaryotic systems, on the other hand, are equipped with various “helpers” (molecular chaperones) that enable them to perform crucial post-translational modifications (such as glycosylation and disulfide bond formation), which are essential for proteins to exhibit biological activity.

5.Imagine you observe a low yield of your target protein in a cell-free system. Describe three possible reasons for this and suggest a troubleshooting strategy for each.

Cause: Template DNA degradation. Strategy: Check DNA purity, or add RNase inhibitors to prevent mRNA degradation.

Cause: Energy depletion or pH shift. Strategy: Optimize the energy regeneration system, or use dialysis to continuously replenish substrate and remove metabolic waste.

Cause: Protein misfolding leading to degradation. Strategy: Lower the reaction temperature (e.g., from 37°C to 25°C), or add molecular chaperones to assist folding.

Homework question from Peter Nguyen

Freeze-dried cell-free systems can be incorporated into all kinds of materials as biological sensors or as inducible enzymes to modify the material itself or the surrounding environment. Choose one application field — Architecture, Textiles/Fashion, or Robotics — and propose an application using cell-free systems that are functionally integrated into the material. Answer each of these key questions for your proposal pitch:

Write a one-sentence summary pitch sentence describing your concept. How will the idea work, in more detail? Write 3-4 sentences or more. What societal challenge or market need will this address? How do you envision addressing the limitation of cell-free reactions (e.g., activation with water, stability, one-time use)?

Summary Pitch Develop a “smart protective suit” with built-in cell-free sensors that can detect toxic chemicals or pathogens in the air in real time through color changes, thus protecting the wearer’s safety.

Detailed Mechanism This concept encapsulates a freeze-dried, cell-free reaction system (containing DNA sensors, ribosomes, and energy substances) within microcapsules of fibers. When the wearer is exposed to specific contaminants (such as heavy metals or specific bacterial signaling molecules), these molecules penetrate the microcapsules. These molecules act as “switches,” initiating the transcription and translation of DNA templates to synthesize chromogenic proteins (such as red fluorescent protein). Ultimately, a visible color change occurs on the surface of the garment, thus triggering an alarm.

Societal Challenge This addresses the issue of hidden threats to occupational safety. Chemical workers, frontline epidemic prevention personnel, and miners are frequently exposed to colorless and odorless hazardous gases or pathogens. Existing electronic sensors are often bulky and require batteries, while this biosensor integrated into fabric requires no power source, is lightweight, and provides full-body monitoring.

Homework question from Ally Huang

Rapid detection of radiation damage in space environment

Background Information During long-term deep-space exploration, astronauts are exposed to intense cosmic radiation, which may cause DNA damage and increase the risk of cancer. In a resource-constrained space environment, real-time and convenient monitoring of the biological effects induced by radiation is crucial for the health of astronauts.

Scientific Interest Traditional live-cell detection relies on complex culture equipment. By using the freeze-dried cell-free system (BioBits®), we can convert the molecular changes induced by radiation into visible fluorescence signals without maintaining the survival of live cells, providing immediate warnings for space biological safety.

Molecular or Genetic Target Protein expression driven by the p53 response element. P53 is the crucial “genomic guardian” in the human body, and its activity significantly increases in response to DNA damage.

Hypothesis / Research Goal Objective: To verify whether the BioBits® system can function as a reliable, non-temperature-controlled biosensor in a microgravity environment, by detecting the level of p53 protein to indicate the degree of DNA damage.

Hypothesis: We hypothesize that the fluorescence intensity triggered by the p53 protein is positively correlated with the radiation dose.

Reasoning: In the Earth laboratory, the p53-mediated reaction is a standard biomarker for cellular stress. The freeze-dried BioBits® reaction components have extremely high stability and are suitable for storage on the International Space Station (ISS). By combining with the P51 fluorescence imaging microscope, astronauts can quickly determine the radiation exposure risk by observing the color brightness without having to send the samples back to Earth for analysis, significantly reducing the response time.