Week 1 HW: Principles and Practices

GammaShroom

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 project I want to develop is called “GammaShroom”, a biological engineering platform that uses radiation-absorbing fungi to help remediate and protect environments exposed to nuclear radiation. This idea is inspired by the discovery of radiotrophic fungi found in places like Chernobyl, where certain species are able to survive and even grow in high-radiation environments by using melanin to interact with ionizing radiation.

The goal of this project is to engineer or optimize these fungi so they can be used as living biological tools for radiation shielding and environmental cleanup. For example, they could be deployed in contaminated sites, nuclear waste storage facilities, or even future space missions where radiation protection is critical. I am interested in this application because it combines microbiology, synthetic biology, and environmental engineering to address a real-world problem. It also represents a sustainable alternative to traditional chemical or mechanical radiation barriers, using biological systems that can self-repair and adapt to harsh conditions.

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.

It’s important to have clear that RadiomycoShield involves releasing or using engineered microorganisms in sensitive environments, it is important to establish governance goals that prioritize safety, environmental protection, and responsible innovation. One major goal is to ensure biosafety and environmental containment. This means preventing unintended ecological disruption if engineered fungi were to spread beyond their intended location. A related sub-goal is to develop strict monitoring systems that track how these organisms behave over time in real environments.

Another crucial governance goal is to promote beneficial and equitable use of the technology. Since radiation contamination affects communities worldwide, access to this technology should not be limited only to wealthy countries or private corporations. A sub-goal here is to encourage international collaboration and shared standards so that remediation tools can be safely and fairly distributed. Together, these goals aim to balance innovation with ethical responsibility, ensuring that the technology reduces harm while maximizing its positive environmental and social impact.

3. Next, describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”). Try to outline a mix of actions (e.g. a new requirement/rule, incentive, or technical strategy) pursued by different “actors” (e.g. academic researchers, companies, federal regulators, law enforcement, etc). Draw upon your existing knowledge and a little additional digging, and feel free to use analogies to other domains (e.g. 3D printing, drones, financial systems, etc.).

Purpose: What is done now and what changes are you proposing?

Design: What is needed to make it “work”? (including the actor(s) involved - who must opt-in, fund, approve, or implement, etc)

Assumptions: What could you have wrong (incorrect assumptions, uncertainties)?

Risks of Failure & “Success”: How might this fail, including any unintended consequences of the “success” of your proposed actions?

1. International Safety Framework for Radiotrophic Fungal Engineering

Purpose

-Current Situation: Research on radiation-absorbing fungi is still emerging and is regulated under general biosafety frameworks that were not designed specifically for organisms deployed in radioactive environments.

-Proposed Change: Develop a specialized international safety framework focused on engineered radiotrophic fungi used for environmental remediation, including stricter evaluation before field deployment.

Design

-Actors: International environmental agencies (e.g., IAEA, UNEP), national biosafety regulators, academic research institutions, and biotech companies.

-Implementation:

-Require environmental risk assessments before outdoor fungal deployment.

-Establish standardized containment and monitoring protocols.

-Create certification systems for laboratories working with engineered fungi.

-Promote international collaboration to harmonize safety standards.

Assumptions

-Specialized regulation will improve safety without severely slowing innovation.

-Researchers and companies will comply with new international standards.

-Environmental impact can be reasonably predicted through controlled testing.

Risks of Failure & “Success”

-Failure Risks: Inconsistent enforcement across countries and regulatory loopholes.

-Unintended Consequences of Success: Excessive regulation may discourage research investment and slow the adoption of beneficial remediation technologies.

2. Funding Incentives for Sustainable Radiation Bioremediation Technologies

Purpose

-Current Situation: Development of fungal bioremediation technologies is limited by high research costs and uncertain commercial returns.

-Proposed Change: Introduce financial incentives and public funding programs to support safe and sustainable fungal remediation technologies.

Design

-Actors: Government science agencies, environmental ministries, international funding organizations, and biotech startups.

-Implementation:

-Offer research grants for radiation bioremediation projects.

-Provide tax incentives for companies developing eco-friendly remediation tools.

-Support public-private partnerships to scale pilot projects.

-Fund long-term safety and environmental impact studies.

Assumptions

-Financial support will accelerate innovation and responsible development.

-Companies will prioritize sustainability when incentives are aligned.

-Governments can effectively evaluate project impact.

Risks of Failure & “Success”

-Failure Risks: Misallocation of funds or exaggerated sustainability claims.

-Unintended Consequences of Success: Overinvestment in one technology could reduce funding for alternative remediation approaches.

3. Global Open Environmental Monitoring Network for Fungal Remediation

Purpose

-Current Situation: Monitoring of radioactive remediation sites is fragmented and data is often inaccessible across institutions.

-Proposed Change: Create a shared international platform that tracks fungal remediation performance and environmental safety indicators in real time.

Design

-Actors: Academic institutions, environmental agencies, international organizations, and data scientists.

-Implementation:

-Develop a centralized open-access monitoring database.

-Use standardized sensors and reporting protocols.

-Establish international data-sharing agreements.

-Apply AI tools to analyze environmental trends.

Assumptions

-Institutions will be willing to share environmental data.

-Cybersecurity systems can protect sensitive information.

-Standardized data collection can be widely adopted.

Risks of Failure & “Success”

-Failure Risks: Limited participation and inconsistent data quality.

-Unintended Consequences of Success: Open environmental data may raise security or geopolitical concerns.

4. Next, score (from 1-3 with, 1 as the best, or n/a) each of your governance actions against your rubric of policy goals. The following is one framework but feel free to make your own:

5. Last, drawing upon this scoring, describe which governance option, or combination of options, you would prioritize, and why. Outline any trade-offs you considered as well as assumptions and uncertainties. For this, you can choose one or more relevant audiences for your recommendation, which could range from the very local (e.g. to MIT leadership or Cambridge Mayoral Office) to the national (e.g. to President Biden or the head of a Federal Agency) to the international (e.g. to the United Nations Office of the Secretary-General, or the leadership of a multinational firm or industry consortia). These could also be one of the “actor” groups in your matrix.

I would prioritize a hybrid governance strategy that combines the Global Open Environmental Monitoring Network with targeted funding incentives for responsible innovation, supported by limited international safety regulations for high-risk deployments. The monitoring network is essential because it enables early detection of ecological risks and provides transparency about how radiotrophic fungal systems behave in real environments. At the same time, financial incentives encourage researchers and companies to invest in safer and more effective remediation technologies. Focused international regulations should act as a safeguard for projects involving environmental release, ensuring that innovation proceeds responsibly.

The main trade-off in this approach is balancing rapid technological progress with precautionary oversight. Too much regulation could slow innovation, while insufficient oversight could increase ecological risks. This recommendation assumes that sustained international cooperation and funding are achievable, although both remain uncertain. My recommendation is directed toward international environmental and nuclear governance organizations such as the International Atomic Energy Agency and the United Nations Environment Programme, which are positioned to coordinate global monitoring and safety standards.

Weekly Assignment

Reflecting on what you learned and did in class this week, outline any ethical concerns that arose, especially any that were new to you. Then propose any governance actions you think might be appropriate to address those issues. This should be included on your class page for this week.

Reflecting on this week’s material, I developed a deeper understanding of how modern biological engineering builds complexity using modular design principles similar to engineering systems. The concept of design cores and universality showed how complex biological circuits can be assembled hierarchically from composable elements, allowing systems to scale in sophistication while remaining controllable. At the same time, biology introduces a unique layer of complexity through self-replication, meaning engineered systems are not static machines but living programs that can grow and evolve. Learning about advances in protein design, genetic circuits, and large-scale genome engineering highlighted how synthetic biology is rapidly expanding our ability to design biological functions from scratch.

This technical power raises important ethical concerns. One major issue is the intentional release of engineered organisms into complex ecosystems. Even systems designed for remediation or beneficial purposes could disrupt microbial communities or behave unpredictably because living systems replicate and interact dynamically with their environments. Another concern is how access to advanced biological technologies may become uneven, especially for communities most affected by environmental disasters.

To address these challenges, governance strategies should include mandatory long-term ecological monitoring of deployed organisms, transparent reporting of experimental and environmental data, and international cooperation to ensure equitable access to beneficial technologies. Integrating modular engineering principles with ethical oversight can help ensure that increasing biological complexity leads to safer and more responsible innovation.

Assignment (Final Project)

As part of your final project, design one or more strategies to ensure that your project, and what it enables, contributes to growing an ethical biological future.

My final project requires a multi-faceted strategy to ensure that the development of radiation-absorbing fungal technologies contributes to an safe and ethical biological future. The first key approach is integrating biosafety engineering directly into the fungal system, including biological containment strategies and long-term ecological monitoring to minimize unintended environmental effects. The second approach is establishing transparent and secure data-sharing practices that allow researchers and regulatory bodies to evaluate performance and risks while protecting sensitive information from misuse. The third approach is promoting equitable and sustainable deployment by prioritizing access for communities affected by nuclear contamination and ensuring that remediation efforts do not create new ecological burdens. Together, these strategies support a research framework that balances innovation with responsibility, fostering environmental protection, social fairness, and public trust in emerging biotechnologies.

Prompt used for the task (they told us to put it, I think, just in case sjsjs)

I would like to clarify that I did use AI for this work, but as you will see, it was mainly for information organization, because I did the research myself, as well as improving the writing to make it more comfortable for the reader. This is evident in the prompts I used. Thank you very much for reading.

For the pictures:

“A futuristic scientific illustration of radiotrophic fungi absorbing radiation in a post-nuclear environment inspired by Chernobyl. Show dark melanin-rich fungi growing on cracked concrete and metallic surfaces, glowing softly as they absorb invisible radiation waves represented by subtle blue and green energy streams. Include a cross-section view where fungal cells convert radiation into biochemical energy, with stylized mitochondria and molecular structures inside. The scene should blend realism and sci-fi aesthetics, with atmospheric lighting, high detail, and a clean scientific visualization style. Add a sense of environmental recovery, with small plants growing nearby to symbolize bioremediation. Use a cool color palette with luminous accents, high resolution, cinematic lighting, and a professional scientific poster style.”

“A futuristic biotech logo featuring a stylized mushroom inspired by radiation-absorbing fungi, glowing with soft neon green and purple energy. The mushroom cap resembles a subtle mushroom cloud shape but abstract and scientific, not violent. Clean minimal design, smooth vector style, centered composition. Include subtle radiation symbol elements integrated into the mushroom texture. Modern biotech aesthetic, sleek typography reading “GammaShroom” below the icon. White or dark gradient background, high contrast, professional scientific branding style.”

For the homework:

“I am developing a research project on a fungal platform for radiation attenuation and environmental bioremediation. Below is a curated set of academic and institutional sources related to fungal radiation resistance, synthetic biology, environmental remediation, and governance frameworks.

Please synthesize and organize the information from all the provided links into a structured analytical report. The goal is to create a clear, evidence-based overview that helps consolidate current knowledge and identify how each source informs the development of my project.

Organize the response into the following sections:

1. Overview of Sources Provide a concise summary of each link individually. For each source, identify its main focus, key findings, and relevance to fungal bioremediation or synthetic biology. Explain how it contributes to the broader understanding of the field.

2. Scientific and Technical Foundations Integrate the sources to describe the core biological and engineering principles involved, including mechanisms of radiation resistance in fungi, bioremediation processes, and relevant synthetic biology tools.

3. Current Applications and Research Landscape Summarize existing case studies, experimental systems, or technological applications described in the sources. Identify demonstrated capabilities and remaining technical gaps.

4. Governance, Safety, and Ethical Context Extract and synthesize information related to biosafety, environmental governance, and ethical considerations. Explain how these frameworks relate to responsible project development.

5. Integrated Insights for Project Development Based on the combined evidence from all sources, summarize key insights that are most relevant to refining and strengthening the project. Highlight opportunities, limitations, and areas requiring further investigation.

The report should maintain an academic tone, use clear scientific language, and explicitly reference how the sources relate to one another. Focus on synthesis and organization rather than speculation.

Sources: Fungal Radiation Attenuators

Melanized fungi thrive on radiation. Studies of Chernobyl isolates and other radiotrophic fungi show that dense melanin layers in cell walls absorb and transduce ionizing radiation. In effect, melanin-rich fungi can “harvest” gamma rays much like plants harvest light. This underpins the idea that engineered, melanized fungal biomass could serve as a living radiation shield.

Space-grown fungi reduce ambient radiation. An ISS experiment with Cladosporium sphaerospermum found that the fungal lawn grew rapidly in microgravity and caused a measurable drop in radiation beneath it compared to a no-fungus control. In quantitative terms, fungal biomass attenuated the local gamma dose rate on orbit. This real-world result supports using fungi as bio-shielding in high-radiation settings.

Directed growth toward radiation (radiotropism). Research notes that some fungi actively grow toward radiation sources (positive radiotropism) and use melanin as an “energy transporter” for metabolism. For example, Chernobyl black molds express more melanin near strong sources and grow faster under irradiation. These observations imply that a radiation-biased growth stimulus could help a bioremediation platform concentrate fungi in hotspot areas.

Fungal Bioremediation Cases

Accumulation of radionuclides. Fungal mycelium naturally binds metals and radionuclides. DOE studies note that fungi accumulated substantial ⁹⁰Sr, ¹³⁷Cs and other isotopes in Chernobyl soils. In fact, a 2003 DOE primer explicitly states “fungi are also known to accumulate metals, particularly radionuclides (as observed following the 1986 Chernobyl accident)”. This natural bioaccumulation suggests engineered fungi could be tuned to sequester radioisotopes from contaminated media.

Engineered radiation-resistant strains. Screening of extreme environments has yielded fungi tolerating both radiation and toxins. For instance, Rhodotorula taiwanensis MD1149 (isolated from a contaminated site) grows under 36 Gy/h of gamma radiation at pH 2.3 and survives acute 2.5 kGy doses. It also forms robust biofilms in the presence of mercury and chromium. Such traits make MD1149 a promising chassis for fungal bioremediation of mixed radioactive/heavy-metal wastes. (The genome of MD1149 is sequenced, enabling genetic engineering for enhanced uptake or melanin production.)

Cost‐effective mycoremediation. Fungi are abundant and fast-growing, offering a low-cost cleanup strategy. The DOE primer notes that mycoremediation could rival plant-based phyto-remediation and be deployed on contaminated soils with added nutrients. In practice, researchers have demonstrated fungal biosorption of U, Pu, and other metals in lab reactors. While large-scale field trials remain limited, these case studies show feasibility. Together, these findings motivate designing fungal bioreactors or biofilters for nuclear waste sites.

Synthetic Biology Governance (Risk and Ethics)

Precautionary risk assessment. Reviews of synthetic biology governance emphasize anticipating environmental hazards. For example, Bohua et al. (2023) propose an ethical framework that prioritizes the precautionary principle and rigorous environmental risk assessment before release. This includes analyzing gene flow, competition with native species, and other non-target effects. Applying such frameworks means a fungal bioremediation platform would require case-by-case safety studies and stakeholder input prior to deployment.

Anticipatory and agile regulation. Policy experts argue that regulation must co-evolve with technology. Kim et al. (2025) call for a “co‐evolutionary” governance model based on OECD guidelines: combining R&D with strategic foresight, public engagement, rapid regulatory adaptation, and international cooperation. In practice, this suggests regulators should work alongside scientists developing radiotrophic fungi—setting provisional guidelines for field use (e.g. containment measures) as the tech develops.

Codes of conduct and “safety-by-design.” International efforts have produced nonbinding standards to foster responsible research. The OECD report highlights the “Tianjin Biosecurity Guidelines” and other biosafety codes that encourage researchers to embed ethics and self-monitoring in their work. For example, an engineered fungus could be designed with genetic “kill switches” or metabolic dependencies to limit persistence. Upholding these principles would be part of an ethical development plan (consistent with many national synthetic biology roadmaps).

International Guidelines and Policies

UN Convention on Biological Diversity (CBD). The CBD has explicitly considered synthetic biology. A 2015 CBD Secretariat report notes that engineered microbes (including fungi) are being developed for bioremediation and pollution control. It also underlines that existing regulatory regimes – notably the Cartagena Protocol on Biosafety – cover “living modified organisms.” In essence, any engineered fungus released into the environment would fall under international biosafety rules requiring risk assessment and notification. This supports governance by tying fungal bioremediation to the same safety processes used for GMOs.

Cartagena Protocol on Biosafety. This UN treaty (under the CBD) mandates that Parties assess and consent to the cross-border transfer or intentional release of any living modified organism (LMO). An engineered radio-attenuating fungus would be considered an LMO. Thus, developing such a platform must follow the Protocol’s risk assessment and public consultation procedures. Compliance ensures that bioremediation deployments meet internationally agreed safety standards.

IAEA and nuclear remediation standards. The International Atomic Energy Agency issues safety guides on radioactive waste and site cleanup. Though not always specific to biotech, IAEA documents (e.g. Policy and Strategies for Environmental Remediation) stress systematic planning, multi-stakeholder oversight, and comparisons of remediation options. A fungal platform would fit into these guidelines as a novel remediation method; IAEA frameworks would require demonstrating its effectiveness and safety relative to conventional methods.

WHO and other agencies. The World Health Organization has historically guided biosafety of medical and agricultural biotech (e.g. risk assessment of GM foods and drugs), and would advocate evaluating any health impacts of environmental releases. WHO’s “One Health” approach also emphasizes that environmental and human health are linked, reinforcing the need for ecological risk checks. Global bodies like the OECD and UN also call for transparency and public engagement on emerging biotechnologies. In sum, international policies urge that a radiation‐absorbing fungal system be developed under strong biosafety oversight – integrating ecological risk assessments, containment planning, and emergency response strategies from the outset.

Sources: Peer-reviewed studies and institutional reports provide the above insights. For example, lab and spaceflight experiments confirm fungi’s radiotrophic capabilities. Bioremediation research identifies metal-accumulating strains and genomic tools for engineering them. Governance analyses and UN documents outline the ethical, legal, and procedural frameworks (precautionary principle, Cartagena Protocol, OECD anticipatory governance, etc.) needed to safely develop and release engineered organisms. Each source thus helps shape a science-based, policy-informed approach to a radiation-absorbing fungal bioremediation platform.”

“I am providing a draft document that contains research notes and project descriptions. Please revise and improve the text while preserving its original meaning and technical content.

Your task is to:

• Correct grammar, spelling, and punctuation errors • Improve clarity, flow, and sentence structure • Replace repetitive or informal wording with appropriate academic synonyms • Strengthen the professional and scientific tone • Ensure consistency in terminology and style throughout the document • Maintain the original intent, arguments, and factual content without adding new information

If any sections are unclear or ambiguous, rewrite them for precision while keeping the author’s meaning intact. Avoid unnecessary complexity; prioritize readability and academic professionalism.”

Week 2 HW: DNA r/w/e

Part 0: Basics of Gel Electrophoresis Attend or watch all lecture and recitation videos. Optionally watch bootcamp.

Part 1: Benchling & In-silico Gel Art See the Gel Art: Restriction Digests and Gel Electrophoresis protocol for details. Overview:

Make a free account at benchling.com Import the Lambda DNA. Simulate Restriction Enzyme Digestion with the following Enzymes: EcoRI HindIII BamHI KpnI EcoRV SacI SalI Create a pattern/image in the style of Paul Vanouse’s Latent Figure Protocol artworks. You might find Ronan’s website a helpful tool for quickly iterating on designs!

Part 0: Basics of Gel Electrophoresis

Part 0 reviews the fundamental biological principles that support the rest of this project. Understanding how genetic information flows inside cells is essential for designing and interpreting molecular biology experiments.

DNA as the Information Storage Molecule

DNA (deoxyribonucleic acid) is the molecule that stores genetic information in living organisms. It consists of two complementary strands arranged in a double helix. Each strand is made of nucleotides containing four bases: adenine (A), thymine (T), cytosine (C), and guanine (G).

The sequence of these bases encodes instructions for building proteins. DNA is chemically stable, making it ideal for long-term information storage. During experiments such as restriction digests and gel electrophoresis, we manipulate DNA directly to analyze or modify genetic information.

RNA and Transcription

RNA (ribonucleic acid) is a temporary copy of genetic instructions. During transcription, an enzyme called RNA polymerase reads a DNA template strand and synthesizes messenger RNA (mRNA).

RNA differs from DNA in three key ways:

It contains ribose sugar instead of deoxyribose

It uses uracil (U) instead of thymine (T)

It is usually single-stranded

mRNA carries genetic instructions from DNA to ribosomes, where proteins are produced.

Proteins and Translation

Proteins are functional molecules that perform most cellular tasks, including catalysis, structure, and signaling. During translation, ribosomes read mRNA in groups of three nucleotides called codons. Each codon corresponds to a specific amino acid.

A chain of amino acids folds into a three-dimensional structure that determines the protein’s function. In this project, designing DNA sequences ultimately aims to control which proteins are produced.

The Central Dogma of Molecular Biology

The relationship between DNA, RNA, and protein is summarized by the central dogma:

DNA → RNA → Protein

This directional flow explains how genetic information is expressed inside cells. All molecular biology techniques used in this assignment — including cloning, restriction digests, and gene expression — rely on manipulating this pathway.

Restriction Enzymes and DNA Manipulation

Restriction enzymes are proteins that cut DNA at specific sequences. These enzymes allow scientists to divide DNA into predictable fragments. By selecting particular enzymes, researchers can design DNA pieces that generate specific band patterns during gel electrophoresis.

This precise cutting ability is the foundation of genetic engineering and is essential for both analytical and creative gel art design.

Gel Electrophoresis Principles

Gel electrophoresis separates DNA fragments by size. Because DNA carries a negative charge, it migrates toward the positive electrode in an electric field.

Smaller fragments move faster through the agarose gel matrix, while larger fragments move more slowly. This separation produces visible bands that correspond to fragment length.

By comparing observed bands to predicted fragment sizes, researchers can verify DNA structure and confirm successful restriction digests.

Part 1: Benchling & In-silico Gel Art

Part 1 focuses on designing a gel electrophoresis experiment using virtual simulation tools before performing any physical lab work.

The primary goal of this design phase is to create a controlled DNA banding pattern through selective restriction enzyme digestion. Instead of randomly cutting DNA, the experiment is planned so that specific fragment sizes generate a visual composition on an agarose gel.

This approach transforms gel electrophoresis from a purely analytical technique into a hybrid scientific and artistic exercise. At the same time, it reinforces essential molecular biology concepts such as enzyme specificity, fragment prediction, and experimental reproducibility. Benchling’s virtual digest tool is used to simulate how restriction enzymes cut a known DNA substrate. By testing different enzyme combinations digitally, predicted fragment lengths can be analyzed without consuming physical reagents.

After creating a free account on benchling.com and importing the Lambda DNA, restriction enzyme digestion was simulated using the following enzymes:

EcoRI

HindIII

BamHI

KpnI

EcoRV

SacI

SalI

Resulting in:

Then, go to the virtual digest tab to see how the digest looks. This visualization uses all the enzymes on the list.

After seeing what could be done with the enzymes, I continued testing more combinations. For faster iteration, I used Ronan’s website to get more images. After several attempts, I ended up with the following iteration:

I liked it a lot because when I saw it, I don’t know why, a sculpture of the ancient Incas came to mind at that moment.

I don’t know if you see it too, but here are a few lines to see if it makes it easier to detect.

Anyway, I tried to make that drawing look like Paul Vanouse’s Latent Figure Protocol artwork. But I didn’t know how to do it, so I decided to ask Gemini how I could do it. This is the result:

It’s not exactly what I expected; it doesn’t really resemble that style of art, but I ended up liking it.

Then, I tried to replicate it in Benchling using the enzymes the website mentioned. The bad thing is that it didn’t turn out as I expected. I’m still not sure what went wrong, but I didn’t make many attempts to recreate it; I didn’t have much time.

But if you look closely, it could easily resemble a level you’d find while playing Mario Maker. Well, that’s what I can see; I don’t know what you all think.

In the end, it’s a tool I need to practice more, but I really liked how it works. But let’s leave opinions aside and move on to the rest of HW2.

Part 2: Gel Art - Restriction Digests and Gel Electrophoresis

Perform the lab experiment you designed in Part 1 and outlined in the Gel Art: Restriction Digests and Gel Electrophoresis protocol.

Part 2: Gel Electrophoresis Experiment (Simulation and Analysis)

There was no lab available at my node this week, so I couldn’t complete this part. Instead, I completed a detailed virtual simulation of the protocol using Benchling and theoretical analysis of the expected outcomes. This allowed me to understand the experimental workflow and interpret how restriction digests generate DNA fragment patterns that can be visualized as gel art.

The experiment would begin with designing a restriction digest of Lambda DNA using selected high-fidelity restriction enzymes. By importing the Lambda DNA sequence into Benchling and running virtual digests, I tested different enzyme combinations to predict fragment sizes and design a gel pattern inspired by gel art. This simulation demonstrated how enzyme selection directly influences the final banding pattern.

If performed in a physical laboratory, the next step would involve preparing a 1% agarose gel in TAE buffer and staining it with a fluorescent dye. The digested DNA samples would be mixed with loading dye and pipetted into the gel wells. When an electric field is applied, negatively charged DNA fragments migrate toward the positive electrode. Smaller fragments move faster through the agarose matrix, resulting in size-based separation.

After electrophoresis, the gel would be imaged using a blue light transilluminator. The resulting band pattern would be compared with the virtual digest predictions. Agreement between expected and observed fragment sizes would confirm successful restriction digestion and validate the DNA design used to create the gel art.

Although I did not physically run the gel, performing the simulation reinforced key molecular biology concepts, including restriction enzyme specificity, fragment size prediction, and electrophoretic separation. This exercise highlights how computational tools can effectively model laboratory experiments and support experimental planning in situations where physical lab access is limited.

Part 3: DNA Design Challenge 3.1. Choose your protein.

In recitation, we discussed that you will pick a protein for your homework that you find interesting. Which protein have you chosen and why? Using one of >the tools described in recitation (NCBI, UniProt, google), obtain the protein sequence for the protein you chose.

[Example from our group homework, you may notice the particular format — The example below came from UniProt]

sp|P03609|LYS_BPMS2 Lysis protein OS=Escherichia phage MS2 OX=12022 PE=2 SV=1 METRFPQQSQQTPASTNRRRPFKHEDYPCRRQQRSSTLYVLIFLAIFLSKFTNQLLLSLL EAVIRTVTTLQQLLT

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

The Central Dogma discussed in class and recitation describes the process in which DNA sequence becomes transcribed and translated into protein. The Central Dogma gives us the framework to work backwards from a given protein sequence and infer the DNA sequence that the protein is derived from. Using one of the tools discussed in class, NCBI or online tools (google “reverse translation tools”), determine the nucleotide sequence that corresponds to the protein sequence you chose above.

[Example: Get to the original sequence of phage MS2 L-protein from its genome phage MS2 genome - Nucleotide - NCBI]

Lysis protein DNA sequence atggaaacccgattccctcagcaatcgcagcaaactccggcatctactaatagacgccggccattcaaacatgaggattacccatgtcgaagacaacaaagaagttcaactctttatgtattgatcttcctcgcgatctttctctcgaaatttacca>atcaattgcttctgtcgctactggaagcggtgatccgcacagtgacgactttacagcaattgcttacttaa

3.3. Codon optimization.

Once a nucleotide sequence of your protein is determined, you need to codon optimize your sequence. You may, once again, utilize google for a “codon optimization tool”. In your own words, describe why you need to optimize codon usage. Which organism have you chosen to optimize the codon sequence for and why?

[Example from Codon Optimization Tool | Twist Bioscience while avoiding Type IIs enzyme recognition sites BsaI, BsmBI, and BbsI]

Lysis protein DNA sequence with Codon-Optimization ATGGAAACCCGCTTTCCGCAGCAGAGCCAGCAGACCCCGGCGAGCACCAACCGCCGCCGCCCGTTCAAACATGAAGATTATCCGTGCCGTCGTCAGCAGCGCAGCAGCACCCTGTATGTGCTGATTTTTCTGGCGATTTTTCTGAGCAAATTCACCAACCAGCTGCTGCTGAGCCTGCTGGAAGCGGTGATTCGCACAGTGACGACCCTGCAGCAGCTGCTGACCTAA

3.4. You have a sequence! Now what?

What technologies could be used to produce this protein from your DNA? Describe in your words the DNA sequence can be transcribed and translated into your protein. You may describe either cell-dependent or cell-free methods, or both.

3.5. [Optional] How does it work in nature/biological systems?

Describe how a single gene codes for multiple proteins at the transcriptional level. Try aligning the DNA sequence, the transcribed RNA, and also the resulting translated Protein!!! See example below. [Example shows the biomolecular flow in central dogma from DNA to RNA to Protein] Special note that all “T” were transcribed into “U” and that the 3-nt codon represents 1-AA.

3.1 Choose your protein

For this assignment, I selected the Damage Suppressor protein (Dsup) from tardigrades. Dsup is a remarkable protein that has been shown to protect cellular DNA from radiation and oxidative stress. Tardigrades are microscopic extremophiles capable of surviving severe environmental conditions, including intense radiation, dehydration, extreme temperatures, and even the vacuum of space. Their resilience has attracted significant interest in bioengineering and astrobiology.

I chose Dsup because it represents a compelling intersection between fundamental biology and applied biotechnology. Its protective properties suggest potential applications in radiation protection for human cells, improvement of stress resistance in engineered microorganisms, and future space exploration where biological systems are exposed to harsh environments. Studying and expressing this protein could contribute to the development of more robust biological systems.

Using the UniProt protein database, I obtained the amino acid sequence of the Dsup protein. UniProt provides curated protein information, including functional annotations and sequence data. The protein sequence used for this project is shown below in FASTA format.

Protein sequence (excerpt): I downloaded the sequence to import it into Benchling and be able to view it better.

Anyway, if you want to download the complete sequence, you can find it at NIH.

3.2 Reverse translation (protein to DNA)

To express this protein in a laboratory system, the amino acid sequence must be converted into a DNA sequence. Using reverse translation tools based on the genetic code, I generated a nucleotide sequence corresponding to the Dsup protein.

Reverse translation assigns a codon to each amino acid. Because the genetic code is degenerate, meaning that most amino acids are encoded by multiple codons, there are many possible DNA sequences that can produce the same protein. The reverse-translated sequence represents one valid encoding of the protein.

Reverse-translated DNA sequence (excerpt): For this process I used Reverse Translate, in case you want to try it yourself:

This sequence serves as an initial template that can be further optimized for expression in a specific host organism. The result is much longer; you can verify this for yourself (I didn’t know how to put the entire sequence here 😅).

3.3 Codon optimization

Although many DNA sequences can encode the same protein, not all sequences are expressed equally well in every organism. Different species show preferences for certain codons, a phenomenon known as codon bias. If a gene uses rare codons for the host organism, translation can become inefficient, reducing protein yield.

I optimized the Dsup DNA sequence for expression in Escherichia coli, a widely used host in biotechnology. E. coli is preferred because it grows rapidly, is cost-effective, and has a well-characterized genetic system. Codon optimization improves translation efficiency by matching the codon usage to the host’s tRNA abundance.

This optimization enhances protein production by improving ribosome speed and accuracy, increasing mRNA stability, and reducing the likelihood of translation stalling. The resulting sequence is designed to maximize reliable expression in E. coli.

Analyzing optimization in E. coli sparked my curiosity, and I wanted to test how this would work in humans.

Anyway, there are more cases to analyze different optimizations, which you can see for yourself in IDT (the tool I used for this part).

3.4 You have a sequence. Now what?

Once the codon-optimized DNA sequence is obtained, it can be used to produce the Dsup protein through standard molecular biology techniques.

In a cell-dependent expression system, the DNA is inserted into a plasmid vector and introduced into bacterial cells through transformation. Inside the cell, RNA polymerase transcribes the DNA into messenger RNA. Ribosomes then translate the mRNA into a polypeptide chain, which folds into the functional Dsup protein. This method is commonly used for large-scale protein production in research and industry.

Alternatively, the DNA can be used in a cell-free expression system. These systems contain purified transcription and translation machinery extracted from cells. By adding the DNA template directly to this mixture, proteins can be synthesized rapidly without living cells. Cell-free systems are especially useful for rapid prototyping and synthetic biology applications.

Both approaches follow the central dogma of molecular biology, in which genetic information flows from DNA to RNA and finally to protein.

3.5 Optional: How it works in biological systems

3.5 Optional: How it works in biological systems

In natural biological systems, a single gene can give rise to multiple protein products through several regulatory mechanisms. These include alternative transcription start sites, RNA processing events such as alternative splicing, and post-translational modifications that alter protein function.

A simple example of the central dogma can be illustrated by aligning a short DNA sequence with its RNA transcript and resulting protein.

A short fragment of the Dsup gene illustrates the central dogma of molecular biology. The DNA sequence:

ATG GCA TCC ACA CAC CAA TCA TCC ACA GAA CCC TCT

is transcribed into RNA by replacing thymine with uracil:

AUG GCA UCC ACA CAC CAA UCA UCC ACA GAA CCC UCU

During translation, each codon corresponds to one amino acid, producing the protein fragment:

Met–Ala–Ser–Thr–His–Gln–Ser–Ser–Thr–Glu–Pro–Ser.

Each group of three nucleotides, called a codon, specifies one amino acid. During transcription, thymine is replaced by uracil in RNA. During translation, ribosomes read these codons to assemble the corresponding amino acid sequence, demonstrating how genetic information is converted into functional proteins.

Part 4: Prepare a Twist DNA Synthesis Order This is a practice exercise, not necessarily your real Twist order!

4.1. Create a Twist account and a Benchling account 4.2. Build Your DNA Insert Sequence

For example, let’s make a sequence that will make E. coli glow fluorescent green under UV light by constitutively (always) expressing sfGFP (a green fluorescent protein):

In Benchling, select New DNA/RNA sequence

Give your insert sequence a name and select DNA with a Linear topology (this is a linear sequence that will be inserted into a circular backbone vector of our choosing).

Go through each piece of the given DNA sequences highlighted below (Promoter, RBS, Start Codon, Coding Sequence, His Tag, Stop Codon, Terminator) and paste the sequences into the Benchling file one after the other (replacing the coding sequence with your codon optimized DNA sequence of interest!). Each time you add a new piece of the sequence, make sure to annotate by right clicking over the sequence and creating an annotation that describes what each piece (e.g., Promoter, RBS, etc.) is (see image below).

Promoter (e.g. BBa_J23106): TTTACGGCTAGCTCAGTCCTAGGTATAGTGCTAGC RBS (e.g. BBa_B0034 with spacers for optimal expression): CATTAAAGAGGAGAAAGGTACC Start Codon: ATG Coding Sequence (your codon optimized DNA for a protein of interest, sfGFP for example): AGCAAAGGAGAAGAACTTTTCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCCGTGGAGAGGGTGAAGGTGATGCTACAAACGGAAAACTCACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCGTGGCCAACACTTGTCACTACTCTGACCTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCACATGAAACGGCATGACTTTTTCAAGAGTGCCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGACCTACAAGACGCGTGCTGAAGTCAAGTTTGAAGGTGATACCCTTGTTAATCGTATCGAGTTAAAGGGTATTGATTTTAAAGAAGATGGAAACATTCTTGGACACAAACTCGAGTACAACTTTAACTCACACAATGTATACATCACGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCAAAATTCGCCACAACGTTGAAGATGGTTCCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCCTGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGTCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATGGTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAA 7x His Tag (Let’s add a 7×His tag at the C-terminus of the protein to enable protein purification from E. coli): CATCACCATCACCATCATCAC Stop Codon: TAA Terminator (e.g. BBa_B0015): CCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGTGAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA Once you’ve completed this, click on Linear Map to preview the entire sequence. If you intend to have a TA review a sequence in the future, this is a good way to verify that all sections are annotated!

This is not required for this exercise, but to share your design with others, please ensure that link sharing is turned on!(Optional) Share your final sequence link with a TA for review!

This insert sequence you built is commonly referred to as an expression cassette in molecular biology (a sequence you can drop into any vector and it’ll perform its function). Go ahead and download the FASTA file for the sequence you made.

It’s helpful to visualize DNA designs using SBOL Canvas (Synthetic Biology Open Language) to convey your designs. Here’s an example of what you just annotated in Benchling:

4.3. On Twist, Select The “Genes” Option

4.4. Select “Clonal Genes” option

For this demonstration, we’ll choose Clonal Genes. You’ll select clonal genes or gene fragments depending on your final project.

Historically, HTGAA projects using clonal genes (circular DNA) have reached experimental results 1-2 weeks quicker because they can be transformed directly into E. coli without additional assembly.

Gene fragments (linear DNA) offer greater design flexibility but typically require an assembly or cloning step prior to transformation. An advantage is If designed with the appropriate exonuclease protection, gene fragments can be used directly in cell-free expression.

4.5. Import your sequence

You just took an amino acid sequence of interest and converted it into DNA, codon optimized it, and built an expression cassette around it! Choose the Nucleotide Sequence option and Upload Sequence File to upload your FASTA file.

4.6. Choose Your Vector

Since we’re ordering a clonal gene, you will need to refer to Twist’s Vector Catalog to choose your circular backbone. You can think of this as taking your linear expression cassette for your protein of interest, and completing the rest of the circle!

The backbone confers many special properties like antibiotic resistance, an origin of replication, and more. Discuss with your node to decide on appropriate antibiotic options. At MIT/Harvard, you can use Ampicillin, Chloramphenicol, or Kanamycin resistance.

Twist vectors do not contain restriction sites near the insert fragment, so make sure to flank your design with cut sites if you are intending to extract this DNA insert fragment later.

For this demonstration, choose a Twist cloning vectors like pTwist Amp High Copy.

Click into your sequence and select download construct (GenBank) to get the full plasmid sequence:

Go back to your Benchling account. Inside of a folder, click the import DNA/RNA sequence button and upload the GenBank file you just downloaded.

This is the plasmid you just built with your expression cassette included. Congratulations on building your first plasmid!

Part 4: Preparing a Twist DNA Synthesis Order

This exercise simulates the workflow used in modern synthetic biology to design and order custom DNA. Although this is a practice exercise, it mirrors the real process researchers use to synthesize genes for experimental work.

4.1 Creating Twist and Benchling Accounts

The first step is creating accounts on Twist Bioscience and Benchling. These platforms serve complementary roles in DNA engineering.

Benchling functions as a digital molecular biology workspace where DNA sequences can be designed, edited, and annotated. It allows researchers to simulate genetic constructs before ordering them.

Twist Bioscience is a commercial DNA synthesis provider. Once a sequence is finalized in Benchling, it can be uploaded to Twist for physical synthesis.

Creating these accounts establishes the digital pipeline from design to manufacturing.

I hope you like it :)

4.2 Building the DNA Insert Sequence

The goal of this section is to construct an expression cassette — a functional DNA unit that produces a protein inside a host organism.

In Benchling, a new linear DNA sequence is created. The topology is set to linear because this insert will later be placed inside a circular plasmid vector.

The sequence is built from modular components:

Promoter: initiates transcription. It controls how strongly the gene is expressed.

Ribosome Binding Site (RBS): ensures efficient translation by recruiting ribosomes.

Start Codon (ATG): signals the beginning of protein synthesis.

Coding Sequence: contains the codon-optimized gene of Dsup, in my case.

7× His Tag: adds histidine residues to allow protein purification.

Stop Codon: terminates translation.

Terminator: stops transcription and stabilizes mRNA.

Each component is pasted sequentially and annotated in Benchling. Annotation is critical because it documents the function of each region and makes the design interpretable to collaborators.

The final annotated construct represents a complete gene expression system. Viewing the Linear Map confirms the structural organization and ensures no sections are missing.

Exporting the sequence as a FASTA file prepares it for DNA synthesis.

Expression Cassette Concept

The constructed insert is called an expression cassette because it can function independently once inserted into a plasmid. This modular design allows the same cassette to be reused in different vectors or host organisms.

Visualization with SBOL Canvas helps communicate the design using standardized synthetic biology symbols. I don’t know why, but I like this part. I love the SBOL Canvas interface; I think it’s simply because it’s simple. I would like to use more of this interface.

4.3 Selecting the “Genes” Option in Twist

Inside Twist’s ordering interface, selecting the Genes category specifies that a full gene construct is being synthesized rather than short oligonucleotides.

4.4 Choosing Clonal Genes

Clonal genes are circular plasmids delivered ready for transformation into bacteria. This option accelerates experimentation because no additional cloning is required.

In contrast, gene fragments are linear DNA pieces that require assembly before use. While more flexible, they add extra laboratory steps.

Choosing clonal genes prioritizes speed and simplicity.

4.5 Importing the Sequence

The FASTA file exported from Benchling is uploaded to Twist. This step transfers the digitally designed expression cassette into the manufacturing platform.

At this stage, Twist verifies the sequence for synthesis compatibility.

4.6 Choosing a Vector

A vector is a circular DNA backbone that carries the insert into host cells. It contains essential features such as:

an origin of replication (for plasmid copying),

antibiotic resistance markers (for selection),

cloning regions.

Selecting a vector like pTwist Amp High Copy determines how the plasmid behaves inside E. coli.

Downloading the full plasmid sequence and re-importing it into Benchling allows visualization of the final construct: the insert integrated into the backbone.

This confirms successful plasmid design.

Final Outcome

By the end of this exercise, a fully annotated plasmid construct has been digitally assembled. This workflow demonstrates the complete pipeline of modern DNA engineering:

design → annotation → synthesis preparation → plasmid assembly.

For final projects, both the annotated insert and chosen vector must be clearly documented to ensure reproducibility and successful DNA synthesis.

Part 5: DNA Read/Write/Edit 5.1 DNA Read (i) 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). (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? Also answer the following questions:

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? 5.2 DNA Write (i) What DNA would you want to synthesize (e.g., write) and why? These could be individual genes, clusters of genes or genetic circuits, whole genomes, and beyond. As described in class thus far, applications could range from therapeutics and drug discovery (e.g., mRNA vaccines and therapies) to novel biomaterials (e.g. structural proteins), to sensors (e.g., genetic circuits for sensing and responding to inflammation, environmental stimuli, etc.), to art (DNA origamis). If possible, include the specific genetic sequence(s) of what you would like to synthesize! You will have the opportunity to actually have Twist synthesize these DNA constructs! :)

See some famous examples of DNA design (ii) What technology or technologies would you use to perform this DNA synthesis and why? Also answer the following questions:

What are the essential steps of your chosen sequencing methods? What are the limitations of your sequencing method (if any) in terms of speed, accuracy, scalability? 5.3 DNA Edit (i) What DNA would you want to edit and why? In class, George shared a variety of ways to edit the genes and genomes of humans and other organisms. Such DNA editing technologies have profound implications for human health, development, and even human longevity and human augmentation. DNA editing is also already commonly leveraged for flora and fauna, for example in nature conservation efforts, (animal/plant restoration, de-extinction), or in agriculture (e.g. plant breeding, nitrogen fixation). What kinds of edits might you want to make to DNA (e.g., human genomes and beyond) and why? (ii) What technology or technologies would you use to perform these DNA edits and why? Also answer the following questions:

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?

5.1 DNA Read

For DNA sequencing, I would choose to read DNA used in DNA-based digital data storage. This technology encodes digital information such as images, text, or scientific data into synthetic DNA molecules. I am interested in sequencing this type of DNA because it represents a bridge between biology and computer science, with the potential to create extremely dense, long-term archival storage systems. DNA is far more stable than conventional storage media and could preserve information for thousands of years. Sequencing stored DNA is essential to verify that the encoded information has not degraded and can be accurately retrieved.

To sequence this DNA, I would use next-generation sequencing (NGS), specifically sequencing-by-synthesis technology developed by Illumina. This method is considered a second-generation sequencing technology because it enables massively parallel sequencing of millions of DNA fragments simultaneously, unlike first-generation Sanger sequencing which reads one fragment at a time.

The input for this method is purified DNA containing encoded data. The preparation steps include fragmenting the DNA into short pieces, attaching adapter sequences to both ends, amplifying the fragments using PCR, and immobilizing them on a flow cell. During sequencing, fluorescently labeled nucleotides are added one base at a time. A camera records the fluorescence emitted as each base is incorporated. Specialized software converts these signals into a nucleotide sequence through a process called base calling.

The output of this technology is a large dataset of short DNA reads in digital format. These reads are assembled computationally to reconstruct the original encoded information. This approach provides high accuracy and scalability, which are critical for reliable data retrieval in DNA storage systems.

5.2 DNA Write

For DNA synthesis, I would design a genetic circuit encoding a radiation-protective protein system, inspired by extremophile organisms. Specifically, I would synthesize a codon-optimized gene encoding the Dsup protein along with regulatory elements that allow controlled expression in bacteria. This DNA could be used to study how protective proteins improve cellular resistance to radiation, which has applications in medicine and space exploration.

An example short segment of the synthesized DNA sequence could look like this:

ATGTCCGACCAGTCCCAGAAGCAGGAGAAGCTGAAGGAGGAGCTGAAGGCCAAGAAG

To synthesize this DNA, I would use commercial gene synthesis technology from Twist Bioscience. This technology relies on high-throughput chemical DNA synthesis using phosphoramidite chemistry and microarray-based oligonucleotide assembly.

The essential steps include chemical synthesis of short oligonucleotides, enzymatic assembly into longer fragments, error correction, and cloning into plasmid vectors. These fragments are then amplified and sequence-verified.

The main limitations of this synthesis method include potential synthesis errors in long sequences, cost for very large constructs, and technical limits on maximum fragment length. However, it offers excellent scalability and precision for gene-level synthesis.

5.3 DNA Edit

For DNA editing, I would focus on modifying genes that improve cellular resistance to radiation damage, similar to research being explored by companies such as Colossal Biosciences in the context of advanced genetic engineering. Editing such genes could have applications in protecting human cells during radiation therapy or long-duration space missions.

To perform these edits, I would use CRISPR-Cas9 genome editing technology. CRISPR works by using a guide RNA to direct the Cas9 enzyme to a specific DNA sequence. Cas9 creates a targeted double-strand break, and the cell’s repair machinery introduces modifications during the repair process.

The essential preparation steps include designing a guide RNA that matches the target sequence, constructing a plasmid or delivery system carrying Cas9 and the guide RNA, and introducing these components into cells. The inputs include the DNA template, Cas9 enzyme, guide RNA, and host cells.

The main limitations of CRISPR editing include off-target effects, incomplete editing efficiency, and challenges in delivering the editing machinery into certain cell types. Despite these limitations, CRISPR remains one of the most powerful and precise genome editing tools available.

GammaShroom

I hope you haven’t forgotten about my project proposed in HW1. If you don’t know what I’m talking about, take a look at HW1; it’s above WEEk2. Anyway, I mention this because I’d like to talk about how HW2 could help you better understand how to implement what we saw in HW1. HW2 extends the conceptual ideas introduced in the “gammashroom” proposal from HW1 by translating them into the theoretical and computational foundations of modern genetic engineering, even in the absence of a physical laboratory. While the node did not perform wet-lab experiments, the simulation and design components of HW2 still develop the core competencies required to engineer biological systems like “gammashroom”. By studying how restriction enzymes selectively modify DNA and how virtual gel electrophoresis predicts fragment patterns, we learn how engineered genetic constructs can be analyzed and validated in silico before any real-world implementation. This type of predictive modeling is a critical first step in synthetic biology, where careful planning and verification reduce experimental uncertainty.

More importantly, the DNA read/write/edit framework explored in HW2 directly supports the long-term development of engineered organisms capable of radiation resistance and environmental adaptation. Designing codon-optimized genes, selecting expression systems, and understanding how DNA can be precisely modified provide the technical roadmap for implementing protective genetic features similar to those envisioned in the gammashroom system. Even without executing the laboratory protocol, engaging with these workflows conceptually builds an understanding of how engineered DNA moves from digital design to functional biological systems. In this way, HW2 bridges the gap between speculative bioengineering concepts and the structured methodology required to realize them, reinforcing how computational design and molecular planning underpin any future experimental work.

Prompt used for the task

If you saw my HW1, you’ll have noticed that I also included some of the prompts I used to complete the task. I do this to show that AI is a very useful tool for supporting projects, and it’s something that personally helps me a lot to organize myself much better.

For the homework: “Please organize and synthesize the following information from my assignment (Part 3: DNA Design Challenge and Part 5: DNA Read/Write/Edit) into a clear, structured academic format.

Your goals are:

Group related concepts into logical sections and subsections

Remove redundancy while preserving all important scientific details

Use clear headings and transitions between ideas

Maintain scientific accuracy and an academic tone

Add short explanations that connect concepts when needed

Highlight key technical terms (DNA sequencing, codon optimization, gene expression, etc.)”

“Please rewrite the following scientific text to improve clarity, flow, and academic quality.

Your goals are:

Use more precise scientific vocabulary and appropriate synonyms

Improve sentence structure and transitions

Maintain the original meaning and technical accuracy

Avoid unnecessary repetition

Use a formal academic tone suitable for a university assignment

Keep explanations clear and accessible

Expand brief sections slightly if needed to improve coherence

Do not add new scientific claims — only refine and strengthen the writing.”

For the picture (Gemini):

“Hello, please examine the image I provided. It represents a DNA sequence modified by restriction enzyme digestion, producing a distinct band pattern. Could you generate an artistic image inspired by the visual structure and composition of this pattern?”

Week 3 — Lab Automation

Assignment: Python Script for Opentrons Artwork — DUE BY YOUR LAB TIME! Your task this week is to Create a Python file to run on an Opentrons liquid handling robot.

Review this week’s recitation and this week’s lab for details on the Opentrons and programming it. Generate an artistic design using the GUI at opentrons-art.rcdonovan.com. Using the coordinates from the GUI, follow the instructions in the HTGAA26 Opentrons Colab to write your own Python script which draws your design using the Opentrons. You may use AI assistance for this coding — Google Gemini is integrated into Colab (see the stylized star bottom center); it will do a good job writing functional Python, while you probably need to take charge of the art concept. If you’re a proficient programmer and you’d rather code something mathematical or algorithmic instead of using your GUI coordinates, you may do that instead. Ask for help early! If you are having any trouble with scripting, contact your TAs as soon as possible for help. Do not wait until your scheduled robot time slot or you may not be able to complete this assignment!

If the Python component is proving too problematic even with AI and human assistance, download the full Python script from the GUI website and submit that: Use the download icon pointed to by the red arrow in this diagram. Use the download icon pointed to by the red arrow in this diagram.

If you use AI to help complete this homework or lab, document how you used AI and which models made contributions. Sign up for a robot time slot if you are at MIT/Harvard/Wellesley or at a Node offering Opentrons automation. The Python script you created will be run on the robot to produce your work of art! At MIT/Harvard? Lab times are on Thursday Feb.19 between 10AM and 6PM. At other Nodes? Please coordinate with your Node. Submit your Python file via this form.

Hello again, friend. I hope you’ve been enjoying what I’ve been doing week by week. In this first part of WH3, I’ll be showcasing the art that can be created using both Python code and Opentrons.

First, let’s start with the artwork I created in OpenTrons. I really enjoyed making this piece because it reminds me of pixel art. What I drew is the Pokémon Charizard sleeping with a Luxury Ball beside it. It’s a design I enjoyed creating. If you’d like to see it more clearly and check the coordinates and fonts I used, you can find it under the name SleepingCharizard.

I also tried to do it in Python code so a bot could recreate it in my Node lab. I wrote the code on Google Colab. I used an AI called ChatGPT for help with the code. I know there are better AIs to use, but all I needed were some coordinate points for my variables, so ChatGPT was sufficient for that part of the code. The first block of code is this:

from opentrons import types

metadata = {    # see https://docs.opentrons.com/v2/tutorial.html#tutorial-metadata
    'author': 'Sergio Cuiza',
    'protocolName': 'WH3: Art Laboratory',
    'description': 'Draw a bitmap pattern on the agar plate using different colors for each pixel, leaving everything to your imagination.',
    'source': 'HTGAA 2026 Opentrons Lab',
    'apiLevel': '2.20'
}

##############################################################################
###   Robot deck setup constants - don't change these
##############################################################################

TIP_RACK_DECK_SLOT = 9
COLORS_DECK_SLOT = 6
AGAR_DECK_SLOT = 5
PIPETTE_STARTING_TIP_WELL = 'A1'

well_colors = {
    'A1' : 'Red',
    'B1' : 'Green',
    'C1' : 'Orange'
}


def run(protocol):
  ##############################################################################
  ###   Load labware, modules and pipettes
  ##############################################################################

  # Tips
  tips_20ul = protocol.load_labware('opentrons_96_tiprack_20ul', TIP_RACK_DECK_SLOT, 'Opentrons 20uL Tips')

  # Pipettes
  pipette_20ul = protocol.load_instrument("p20_single_gen2", "right", [tips_20ul])

  # Modules
  temperature_module = protocol.load_module('temperature module gen2', COLORS_DECK_SLOT)

  # Temperature Module Plate
  temperature_plate = temperature_module.load_labware('opentrons_96_aluminumblock_generic_pcr_strip_200ul',
                                                      'Cold Plate')
  # Choose where to take the colors from
  color_plate = temperature_plate

  # Agar Plate
  agar_plate = protocol.load_labware('htgaa_agar_plate', AGAR_DECK_SLOT, 'Agar Plate')  ## TA MUST CALIBRATE EACH PLATE!
  # Get the top-center of the plate, make sure the plate was calibrated before running this
  center_location = agar_plate['A1'].top()

  pipette_20ul.starting_tip = tips_20ul.well(PIPETTE_STARTING_TIP_WELL)

  ##############################################################################
  ###   Patterning
  ##############################################################################

  ###
  ### Helper functions for this lab
  ###

  # pass this e.g. 'Red' and get back a Location which can be passed to aspirate()
  def location_of_color(color_string):
    for well,color in well_colors.items():
      if color.lower() == color_string.lower():
        return color_plate[well]
    raise ValueError(f"No well found with color {color_string}")

  # For this lab, instead of calling pipette.dispense(1, loc) use this: dispense_and_detach(pipette, 1, loc)
  def dispense_and_detach(pipette, volume, location):
      """
      Move laterally 5mm above the plate (to avoid smearing a drop); then drop down to the plate,
      dispense, move back up 5mm to detach drop, and stay high to be ready for next lateral move.
      5mm because a 4uL drop is 2mm diameter; and a 2deg tilt in the agar pour is >3mm difference across a plate.
      """
      assert(isinstance(volume, (int, float)))
      above_location = location.move(types.Point(z=location.point.z + 5))  # 5mm above
      pipette.move_to(above_location)       # Go to 5mm above the dispensing location
      pipette.dispense(volume, location)    # Go straight downwards and dispense
      pipette.move_to(above_location)       # Go straight up to detach drop and stay high

  ###
  ### YOUR CODE HERE to create your design
  ###
  azurite_points = [
    (-8.8, 8.8),
    (-6.6, 6.6),
    (-4.4, 4.4),
    (-2.2, 2.2),
    (0, 0),
    (2.2, -2.2),
    (4.4, -4.4),
    (6.6, -6.6),
    (8.8, -8.8),

    (-8.8, -8.8),
    (-6.6, -6.6),
    (-4.4, -4.4),
    (-2.2, -2.2),
    (2.2, 2.2),
    (4.4, 4.4),
    (6.6, 6.6),
    (8.8, 8.8)
  ]

  mtagbfp2_points = [
    (-2.2, 4.4),
    (2.2, 4.4),
    (-4.4, -2.2),
    (4.4, -2.2)
  ]

  mplum_points = [
    (0, 8.8),
    (2.2, 8.8),
    (-2.2, 8.8)
  ]

  mlychee_tf_points = [
    (-6.6, 4.4),
    (6.6, 4.4),
    (-6.6, -2.2),
    (6.6, -2.2)
  ]

  mruby2_points = [
    (8.8, 2.2),
    (8.8, 0),
    (8.8, -2.2)
  ]

  mko2_points = [
    (-8.8, 2.2),
    (-8.8, 0),
    (-8.8, -2.2)
  ]

  eqfp578_points = [
    (0, 11),
    (-2.2, 11),
    (2.2, 11)
  ]

  mrfp1_points = [
    (4.4, 8.8),
    (6.6, 6.6)
  ]

  mcherry_points = [
    (-4.4, 8.8),
    (-6.6, 6.6)
  ]

  mkate2_points = [
    (0, -8.8),
    (0, -11)
  ]

  # =========================
  # AZURITE (Red)
  # =========================
  pipette_20ul.pick_up_tip()
  pipette_20ul.aspirate(len(azurite_points), location_of_color('Red'))

  for x_coord, y_coord in azurite_points:
    target_location = center_location.move(types.Point(x=x_coord, y=y_coord))
    dispense_and_detach(pipette_20ul, 1, target_location)

  pipette_20ul.drop_tip()

  # =========================
  # mTagBFP2 (Green)
  # =========================
  pipette_20ul.pick_up_tip()
  pipette_20ul.aspirate(len(mtagbfp2_points), location_of_color('Green'))

  for x_coord, y_coord in mtagbfp2_points:
    target_location = center_location.move(types.Point(x=x_coord, y=y_coord))
    dispense_and_detach(pipette_20ul, 1, target_location)

  pipette_20ul.drop_tip()

  # =========================
  # mPlum (Orange)
  # =========================
  pipette_20ul.pick_up_tip()
  pipette_20ul.aspirate(len(mplum_points), location_of_color('Orange'))

  for x_coord, y_coord in mplum_points:
    target_location = center_location.move(types.Point(x=x_coord, y=y_coord))
    dispense_and_detach(pipette_20ul, 1, target_location)

  pipette_20ul.drop_tip()
  # =========================
  # mPlum (Orange)
  # =========================
  pipette_20ul.pick_up_tip()
  pipette_20ul.aspirate(len(mplum_points), location_of_color('Orange'))

  for x_coord, y_coord in mplum_points:
    target_location = center_location.move(types.Point(x=x_coord, y=y_coord))
    dispense_and_detach(pipette_20ul, 1, target_location)

  pipette_20ul.drop_tip()
  # =========================
  # mPlum (Orange)
  # =========================
  pipette_20ul.pick_up_tip()
  pipette_20ul.aspirate(len(mplum_points), location_of_color('Orange'))

  for x_coord, y_coord in mplum_points:
    target_location = center_location.move(types.Point(x=x_coord, y=y_coord))
    dispense_and_detach(pipette_20ul, 1, target_location)

  pipette_20ul.drop_tip()
    # =========================

  # mLychee_tf (Red)

  # =========================

  pipette_20ul.pick_up_tip()

  pipette_20ul.aspirate(len(mlychee_tf_points) * 1, location_of_color('Red'))



  for x_coord, y_coord in mlychee_tf_points:

      target_location = center_location.move(types.Point(x=x_coord, y=y_coord))

      dispense_and_detach(pipette_20ul, 1, target_location)



  pipette_20ul.drop_tip()



  # =========================

  # mRuby2 (Green)

  # =========================

  pipette_20ul.pick_up_tip()

  pipette_20ul.aspirate(len(mruby2_points) * 1, location_of_color('Green'))



  for x_coord, y_coord in mruby2_points:

      target_location = center_location.move(types.Point(x=x_coord, y=y_coord))

      dispense_and_detach(pipette_20ul, 1, target_location)



  pipette_20ul.drop_tip()



  # =========================

  # mKO2 (Orange)

  # =========================

  pipette_20ul.pick_up_tip()

  pipette_20ul.aspirate(len(mko2_points) * 1, location_of_color('Orange'))



  for x_coord, y_coord in mko2_points:

      target_location = center_location.move(types.Point(x=x_coord, y=y_coord))

      dispense_and_detach(pipette_20ul, 1, target_location)



  pipette_20ul.drop_tip()



  # =========================

  # eqFP578 (Red)

  # =========================

  pipette_20ul.pick_up_tip()

  pipette_20ul.aspirate(len(eqfp578_points) * 1, location_of_color('Red'))



  for x_coord, y_coord in eqfp578_points:

      target_location = center_location.move(types.Point(x=x_coord, y=y_coord))

      dispense_and_detach(pipette_20ul, 1, target_location)



  pipette_20ul.drop_tip()



  # =========================

  # mRFP1 (Green)

  # =========================

  pipette_20ul.pick_up_tip()

  pipette_20ul.aspirate(len(mrfp1_points) * 1, location_of_color('Green'))



  for x_coord, y_coord in mrfp1_points:

      target_location = center_location.move(types.Point(x=x_coord, y=y_coord))

      dispense_and_detach(pipette_20ul, 1, target_location)



  pipette_20ul.drop_tip()



  # =========================

  # mCherry (Orange)

  # =========================

  pipette_20ul.pick_up_tip()

  pipette_20ul.aspirate(len(mcherry_points) * 1, location_of_color('Orange'))



  for x_coord, y_coord in mcherry_points:

      target_location = center_location.move(types.Point(x=x_coord, y=y_coord))

      dispense_and_detach(pipette_20ul, 1, target_location)



  pipette_20ul.drop_tip()



  # =========================

  # mKate2 (Red)

  # =========================

  pipette_20ul.pick_up_tip()

  pipette_20ul.aspirate(len(mkate2_points) * 1, location_of_color('Red'))



  for x_coord, y_coord in mkate2_points:

      target_location = center_location.move(types.Point(x=x_coord, y=y_coord))

      dispense_and_detach(pipette_20ul, 1, target_location)



  pipette_20ul.drop_tip()
  # Don't forget to end with a drop_tip()

In the second block the code was already predetermined:

# Execute Simulation / Visualization -- don't change this code block
protocol = OpentronsMock(well_colors)
run(protocol)
protocol.visualize()

The result I got from running the code is this:

=== VOLUME TOTALS BY COLOR ===
	Green:		 aspirated 9	 dispensed 9
	Red:		 aspirated 26	 dispensed 26
	Orange:		 aspirated 14	 dispensed 14
	[all colors]:	[aspirated 49]	[dispensed 49]
=== TIP COUNT ===
	 Used 12 tip(s)  (ideally exactly one per unique color)

I’m not really sure what I wanted to do, because I wanted to try making something like a radiation mask or something similar. I don’t really see much of a resemblance, but I couldn’t try any further due to lack of time. I also wanted to do what I had done in Opentrons, but since the pipette only accepts 20µL, I couldn’t do it. I know that by using more variables it could be achieved, but I didn’t have the time. Here is the link to my Google Collab project.

The code is not yet running on the robot because my node’s labs will only be held this week 4.

Post-Lab Questions — DUE BY START OF FEB 24 LECTURE One of the great parts about having an automated robot is being able to precisely mix, deposit, and run reactions without much intervention, and design and deploy experiments remotely.

For this week, we’d like for you to do the following:

Find and describe a published paper that utilizes the Opentrons or an automation tool to achieve novel biological applications. Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode, Python scripts, 3D printed holders, a plan for how to use Ginkgo Nebula, and more. You may reference this week’s recitation slide deck for lab automation details. While your description/project idea doesn’t need to be set in stone, we would like to see core details of what you would automate. This is due at the start of lecture and does not need to be tested on the Opentrons yet.

Example 1: You are creating a custom fabric, and want to deposit art onto specific parts that need to be intertwined in odd ways. You can design a 3D printed holder to attach this fabric to it, and be able to deposit bio art on top. Check out the Opentrons 3D Printing Directory.

Example 2: You are using the cloud laboratory to screen an array of biosensor constructs that you design, synthesize, and express using cell-free protein synthesis.

Echo transfer biosensor constructs and any required cofactors into specified wells. Bravo stamp in CPFS reagent master mix into all wells of a 96-well / 384-well plate. Multiflo dispense the CFPS lysate to all wells to start protein expression. PlateLoc seal the plate. Inheco incubate the plate at 37°C while the biosensor proteins are synthesized. XPeel remove the seal. PHERAstar measure fluorescence to compare biosensor responses.

1. Published Paper Using Opentrons / Lab Automation

A published paper that utilizes automation tools similar to Opentrons for novel biological applications is “Programming a Low-Cost, Open-Source Robot for High-Throughput Biology”. In this study, researchers implemented an open-source liquid-handling robot to automate repetitive laboratory tasks such as pipetting, reagent mixing, and plate preparation.

The automation system allowed the researchers to conduct high-throughput biological experiments with improved accuracy and reproducibility compared to manual lab work. By programming the robot using Python-based protocols, the team was able to standardize workflows including serial dilutions, reaction setup, and sample transfers across 96-well plates.

A key novel biological application demonstrated in the paper is the scaling of experimental workflows in synthetic biology and molecular biology. Automation reduced human error, increased experimental consistency, and enabled remote experiment execution. This is especially useful for screening large numbers of biological samples, which would otherwise be time-consuming and prone to variability if performed manually.

Overall, the paper shows that open-source automation tools like Opentrons can significantly enhance experimental precision, accessibility, and scalability in modern biological research, making them valuable for applications such as biosensor screening, protein expression experiments, and automated assay development.

2. Description of What I Intend to Automate for My Final Project

For my final project, I intend to use lab automation tools to systematically investigate radiation-protective biological mechanisms inspired by extremophiles, including melanized fungi discovered in high-radiation environments and protective proteins such as Dsup.

The core idea is to automate a comparative experimental workflow that evaluates how biological samples (e.g., protein systems or biomaterial coatings) respond to simulated stress conditions, including oxidative and radiation-like damage proxies.

What I Would Automate

The automation system (e.g., Opentrons OT-2 + cloud lab tools like Ginkgo Nebula) would be used to:

-Precisely prepare reagent mixtures

-Dispense samples into multi-well plates

-Run parallel stress-condition assays

-Standardize incubation and measurement steps

-Collect reproducible quantitative data

This is particularly useful because my node did not perform a physical laboratory experiment, so automation provides a conceptual framework for how the experimental design could be executed remotely and reproducibly.

3. Proposed Automated Workflow (Conceptual)

First, I would design a 3D-printed holder to stabilize specialized sample substrates (such as coated slides or biomaterial samples) so the robot can deposit reagents with spatial precision. This ensures consistent sample positioning and minimizes mechanical variation during automated pipetting.

Then, the automated workflow would proceed as follows:

Transfer prepared biomaterial or protein samples into a 96-well plate using calibrated pipetting protocols.

Dispense controlled concentrations of stress-inducing reagents (e.g., oxidative agents that simulate radiation-induced damage).

Add protective components inspired by extremophile systems (such as melanin analogs or Dsup-related protein constructs).

Seal and incubate the plate under standardized temperature conditions.

Measure fluorescence, absorbance, or structural stability metrics using automated plate readers.

Export and analyze the dataset to compare protective efficiency across conditions.

4. Example Pseudocode (Opentrons-Style Automation Script)

from opentrons import protocol_api

metadata = {
    'protocolName': 'Automated Stress Response Assay',
    'author': 'Student Project',
    'description': 'Automated preparation of stress-response samples',
    'apiLevel': '2.13'
}

def run(protocol: protocol_api.ProtocolContext):

    # Labware setup
    plate = protocol.load_labware('corning_96_wellplate_360ul_flat', '1')
    tiprack = protocol.load_labware('opentrons_96_tiprack_300ul', '2')
    reservoir = protocol.load_labware('nest_12_reservoir_15ml', '3')

    pipette = protocol.load_instrument('p300_single', 'right', tip_racks=[tiprack])

    # Reagent locations
    sample = reservoir.wells()[0]
    stress_agent = reservoir.wells()[1]
    protective_solution = reservoir.wells()[2]

    # Automated distribution loop
    for well in plate.wells()[:24]:
        pipette.pick_up_tip()
        pipette.transfer(50, sample, well)
        pipette.transfer(20, stress_agent, well)
        pipette.transfer(20, protective_solution, well, mix_after=(3, 50))
        pipette.drop_tip()

This script demonstrates how automation ensures precise volume control, repeatability, and scalable experimentation.

5. Role of Cloud Automation (Ginkgo Nebula)

Using a cloud laboratory platform like Ginkgo Nebula would allow remote experiment deployment without needing a physical lab setup. I could upload experimental designs, specify reagent combinations, and run high-throughput assays in parallel. This aligns with the project constraints, since the experimental work in my node was conceptual rather than physically executed.

Cloud automation would also:

Enable large-scale parameter screening

Reduce human error in pipetting and timing

Provide standardized datasets for analysis

Allow iterative experimental optimization based on previous results

6. Why Automation is Critical for This Project

Automation directly supports the scientific objectives by improving experimental precision, reproducibility, and scalability. In projects related to radiation tolerance, stress-response biology, or protective biomolecules, small inconsistencies in reagent handling or incubation can produce misleading results. Automated robotic systems eliminate much of this variability and allow controlled, repeatable experimental design.

Additionally, automation enables remote experimentation and systematic testing of multiple protective conditions, which is especially relevant when investigating biological mechanisms inspired by extremophiles and radiation-resistant systems. This makes the project more rigorous, technically feasible, and aligned with modern synthetic biology and bioengineering workflows.

Final Project Ideas — DUE BY START OF FEB 24 LECTURE As explained in this week’s recitation, add 1-3 slides in your Node’s section of this slide deck with 3 ideas you have for an Individual Final Project. Be sure to put your name, city, and country on your slide!

Slide 1 — Ideas For My Fianl Project

Individual Final Project Ideas – GammaShroom Automation Name: Sergio Cuiza City, Country: Cochabamba, Bolivia Node: SynBio USFQ

My project focuses on GammaShroom, a concept inspired by radiation-resistant fungi and extremophile biology, and how lab automation could optimize experimental design, reproducibility, and remote testing workflows even without direct wet-lab execution.

Slide 2 — Idea 1: Automated Growth Condition Screening for GammaShroom

My first project idea is to design an automated workflow to screen different growth conditions for a radiation-resistant fungal model (GammaShroom concept). Using an Opentrons liquid-handling robot, the system would prepare multiple media compositions, dispense samples into 96-well plates, and standardize experimental setups.

The automation would:

Precisely distribute media with different nutrient concentrations

Control replicates to reduce variability

Enable parallel condition testing

Improve reproducibility of extremophile growth experiments

Even if my node did not perform the wet lab, the protocol design could be deployed remotely in a cloud lab environment, allowing scalable experimentation without manual pipetting errors.

Slide 3— Idea 2: Automated Bio-Pigment Production Screening (Melanin & Radioprotection)

Many radiation-resistant fungi produce melanin-like pigments that may contribute to radiotolerance. My project proposes using automation to screen pigment production efficiency under different environmental conditions.

Automated workflow:

Robotically prepare multiple culture media compositions

Dispense fungal samples into microplates

Incubate under controlled conditions

Measure pigmentation changes using plate reader absorbance

This would connect directly to the GammaShroom concept by exploring the biological mechanisms that could explain radiation resistance in fungal systems.

Slide 4 — Idea 3: Custom 3D-Printed Holder for Non-Standard Fungal Samples

My third idea is to design a custom 3D-printed holder compatible with the Opentrons deck to stabilize unconventional sample containers used for fungal or bio-inspired materials like GammaShroom substrates.

The holder would:

Secure irregular culture containers

Allow precise reagent deposition

Maintain positional accuracy during automated pipetting

Enable consistent spatial experimental layouts

This hardware + automation integration is especially useful for bio-inspired projects where standard lab plates may not match the experimental material format.

Week 1 Lab: Pipetting

Week 2 Lab: Gel Art

Individual Final Project

Group Final Project