Biological Engineering Application & Research Proposal

Agnostic Biosurveillance in the Age of Generative AI

The Landscape of Risk

As AI capabilities grow, concerns about biosecurity risks posed by frontier large language models (LLMs) have grown significantly. Current reports based on surveys of experts in biosecurity and biology, indicate that if AI reaches certain performance levels, the annual risk of a human-caused epidemic (with more than 100,000 deaths) could increase from 0.3% to 1.5%.

Given this landscape, my proposal is to develop a detection system capable of identifying any genetically modified organism, even if it is completely new and does not exist in any database.

Focus on how it was made, not on what it is

As I understand, the problem with current biosecurity is that it functions reactively, similar to a computer “antivirus” that only recognizes threats it has seen before. If someone creates a new pathogen from scratch using AI, our current tools might not detect it because they would not recognize the specific sequence.

My proposal, instead does not focus on the identity of the organism but on its creation process.

The “Human Accent” Hypothesis

The central idea is that human-manufactured DNA possesses an “accent” or statistical pattern distinct from that of natural DNA. This occurs because humans use specific tools to design, optimize, and assemble genetic code. My idea proposes to use AI models (specifically genomic language models) to learn this “human accent” systematically.

Mechanism and Vision

Once the AI identifies an artificially designed sequence, it would be coupled with an enhanced CRISPR-Cas13 system to trigger a biological alarm. Ultimately, I seek to create a tool that makes any genetic engineering intervention “visible,” ensuring that we can anticipate and mitigate the growing risks posed by generative AI applied to biology.

References of Interest

• Linder, J., et al. (2024) | Nature Machine Intelligence Validates the use of AI architectures to identify statistical biases in synthetic DNA.
• Gootenberg, J. S., et al. (2023) | Science Establishes the technical viability of CRISPR-Cas13 sensors for PCR-free biodefense applications.
• Zhou et al. (2024) | Eliyon Confirms that CRISPR-Cas13a systems achieve exceptional sensitivity with the capacity for single-copy detection.
• Forecasting LLM-enabled biorisk and the efficacy of safeguards (2025) Forecast Research Institute.

Goal 1: Preventing Misuse of the System (Security)

Since this tool can have a wide reach, so would its implications; therefore, we must prevent anyone from using it to learn how to manufacture threats that “deceive” detection.

• Controlled Access (API): Do not release the AI code openly. Instead, allow it to be used under supervision so that no one can use it to “train” new invisible pathogens.
• Attack Simulations (Red-Teaming): Assemble a panel of experts to constantly try to break or deceive the system. In this way, the goal is to stay ahead of those who wish to use AI to cause harm.

Goal 2: Fair Access for Everyone (Equity)

Biological security should not be a luxury for rich countries; it must protect everyone.

• Accessible CRISPR Technology: Although the AI remains private for security reasons, the instructions to manufacture the biological sensor (CRISPR) must be free and low-cost so that any country can use them.
• Solidarity-based Global Alert: Create a network where, if a country detects something unusual, it can warn the rest of the world efficiently.

Goal 3: Protecting Privacy and Legitimate Science (Ethics)

The system must be capable of distinguishing between a real threat and a medical breakthrough or a natural mutation.

• Clear Boundaries: Create rules that separate what is a dangerous pathogen from what is medicine (such as gene therapy), in order to avoid slowing down science that actually helps people.
• Human Oversight: Design safeguards and the corresponding alignment so that the AI does not make extreme decisions on its own. If the system detects something, a group of experts must review the situation before launching a panic-inducing alarm.

Additional References

• International AI Safety Report (2026) | A Comprehensive review of latest scientific research on the capabilities and risks of general-purpose AI systems.

Action 1: An International “Alert System”

Technical Strategy

• Purpose: Currently, threat detection is isolated and slow. This proposal could eventually encourage the creation of a global network of shared sensors that use AI to report genetic anomalies in real-time.
• Design: Academic researchers and hospitals install low-cost CRISPR biosensors. Data is sent to a centralized platform (similar to the financial system that detects bank fraud).
• Assumptions: We assume that countries will be willing to share health data for the common good.
• Risks: If the system is “successful,” we could have too many alerts (false positives). If it fails, a real threat could go unnoticed if sensors are not well distributed geographically.

Action 2: “Black Box” Licenses for AI

Rule / Requirement

• Purpose: To prevent the AI that detects the “human accent” from being used by criminals to learn how to erase that trace.
• Design: Restrictions must be established so that technology companies do not release the model’s source code openly. Only verified users (governments and certified scientists) will be able to access the tool using special keys.
• Assumptions: We believe we can maintain technological secrecy and that no one will manage to “hack” or replicate the AI privately.
• Risks: The “success” of this rule could slow down scientific innovation if researchers cannot freely access the tool. Failure would be the emergence of a black market for “unrestricted” versions of this AI.

Action 3: “Clean DNA” Certification

Incentive

• Purpose: Today, DNA synthesis companies cannot always detect if an order is dangerous if it is a new design.
• Design: DNA synthesis companies receive a certification or security seal if they run their orders through this AI filter before sending them to the customer. A regulatory entity validates that these companies comply with the standard.
• Assumptions: We assume that companies will seek certification and that customers will not go to illegal providers.
• Risks: If successful, designing biopathogens becomes almost impossible to print legally. A risk of success is that biotechnology becomes slower or more expensive. A failure would be that malicious actors simply buy their own “DNA printers” to bypass control.

🧭 POLICY GOVERNANCE | EVALUATION MATRIX

SCORING KEY ● ● ● High Alignment (1) | ● ● ○ Medium Alignment (2) | ● ○ ○ Low Alignment (3)

Strategic Priority: Academic-Industry Alliance

I recommend that MIT lead a joint task force with the International Gene Synthesis Consortium (IGSC) to implement a combination of Action 1 (Alert System) and Action 3 (Clean DNA Certification).

Why this combination? This alliance allows MIT’s cutting-edge research to become an industrial standard. While MIT provides the AI to detect the “human accent,” IGSC companies act as the first global filter, ensuring that no suspicious sequence is synthesized without oversight. This maximizes Security and Efficacy without relying solely on slow-moving government regulations.

Trade-offs

• Security vs. Industrial Speed: Implementing an AI filter on every DNA order might make synthesis slightly slower. However, the cost of not doing so (an artificial pandemic) is much higher.
• Transparency vs. Intellectual Property: Companies must allow an external AI to analyze their orders. To protect their trade secrets, this proposal points toward a system where the AI processes data in an encrypted manner (Privacy-Preserving AI).

Assumptions and Uncertainties

• Universal Adoption: We assume that companies outside the consortium will not offer “no-questions-asked” services to attract malicious actors.
• False Positives: There is uncertainty regarding whether the AI will halt legitimate synthetic biology experiments, which would require a fast and expert human review process.

• The Duality of Detection: The same system that detects engineering could be used to teach others how to hide it.
  • Proposed Action: Establish a “Black Box” protocol where the source code for detection is only accessible to certified biosecurity auditors.
• Technological Justice: We do not want this technology to create a gap where only MIT and large companies are protected.
  • Proposed Action: Create an Open Biosurveillance fund to donate low-cost CRISPR sensors to laboratories in countries of the Global South.

Conclusion for MIT and the IGSC

Biological security in the age of AI should not be solved by a single institution. By combining MIT’s intelligence with the IGSC’s infrastructure, the proposal aims to create an ecosystem where genetic engineering could be visible and accountable.

🧬 Q&A: Professor Jacobson

Polymerase Error Rate

• 10^{-4 to 10}-5 (1 error per 10,000–100,000 bases).

Genome Comparison

• With 3.2 x 10^9 bp, lack of repair would cause ~100,000 mutations per division, making life impossible.

Biological Solution

• A three-tier system (Selectivity – Proofreading – Mismatch Repair) reduces error to < 1 mutation per genome.

Coding Possibilities

• ~400 amino acids can be encoded by 10^191 different DNA sequences.

Functional Constraints

• Not all codes work due to:
  1. tRNA availability
  2. Folding kinetics
  3. mRNA stability
  4. Splicing signals

Reference: Alberts et al. (2022). Molecular Biology of the Cell.

Q&A: Professor LeProust

Oligo Synthesis: Methods and Limits

1. Most Common Method The standard method is Phosphoramidite synthesis. It is a chemical, solid-phase process that adds nucleotides one by one to a growing chain.

2. Why is it hard to exceed 200nt? It is a matter of cumulative yield. Even with a 99% efficiency per step, errors add up exponentially:

• At 100nt, ~36% of the final molecules are correct.
• At 200nt, only ~13% are correct. Beyond 200nt, the mixture contains too many “short” or damaged sequences, making it nearly impossible to isolate the right one.

3. Why not a 2,000bp gene directly?

• Zero Yield: At 2,000 bases, the math (0.99^2000) results in essentially zero correct molecules.
• DNA Damage: The chemicals used in each cycle eventually degrade the DNA chain before it can reach that length.

As a solution, to make a 2,000bp gene, companies synthesize multiple small 60-100nt fragments (oligos) and then “stitch” them together using biological methods like Assembly PCR or Gibson Assembly.

Q&A: Professor Church

The 10 dietary essential amino acids are:

The 10 dietary essential amino acids are:

• Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, Val
• The “lysine contingency” from Jurassic Park doesn’t work well because animals already can’t make lysine on their own. They normally get it from food. So if one escaped, it could still find lysine in nature, which makes this a weak safety control.

Part 3: DNA Design Challenge

3.1 Choose Your Protein

• Protein: Envelope small membrane protein (Gene: E)
• Organism: Severe acute respiratory syndrome coronavirus 2 (2019-nCoV) (SARS-CoV-2)
• Metadata:
  • Length: 75 amino acids
  • Mass: 8,365 Da

Amino Acid Sequence:

MYSFVSEETGTLIVNSVLLFLAFVVFLLVTLAILTALRLCAYCCNIVNVSLVKPSFYVYSRVKNLNSSRVPDLLV

Reason for Selection:

I am interested in the SARS-CoV-2 Envelope (E) protein from a biosurveillance standpoint. This protein is essential for viral assembly and release; consequently, it cannot undergo significant mutations without halting these vital functions. Given this, primers for the E gene should tend to be more stable over time, unlike those for the Spike (S) protein, which constantly mutates to evade vaccines and immune responses. Therefore, the E protein can be considered a more reliable and “secure” target for viral detection and monitoring.

3.2 Reverse Translation

Conversion of Protein (amino acid) sequence to DNA (nucleotide) sequence.

Description: Reverse translation of sample sequence to a 226-base sequence of most likely codons.

DNA Sequence:

atgtatagctttgtgagcgaagaaaccggcaccctgattgtgaacagcgtgctgctgtttctggcgtttgtggtgtttctgctggtgaccctggcgattctgaccgcgctgcgcctgtgcgcgtattgctgcaacattgtgaacgtgagcctggtgaaaccgagcttttatgtgtatagccgcgtgaaaaacctgaacagcagccgcgtgccggatctgctggtg. 

3.3 Codon Optimization

Optimized DNA Sequence:

ATGTATAGCTTTGTGAGCGAAGAAACGGGCACCCTGATTGTGAACAGCGTGCTGCTGTTCCTGGCATTTGTGGTGTTTCTGCTGGTGACCCTGGCGATTCTGACCGCGCTGCGCCTGTGCGCCTATTGTTGCAACATTGTGAACGTGAGCCTGGTGAAACCGAGCTTTTACGTTTATAGCCGCGTGAAAAATCTGAACAGCAGCCGCGTGCCGGATCTGCTGGTG.

Codon optimization is basically a synthetic biology hack to get the most out of gene expression in a specific organism. Since the genetic code is degenerate, different species have their own “favorite” codons to code for the same amino acid. This technique tweaks the genetic sequence to match the tRNA availability of the host, clearing out any bottlenecks during translation. By swapping “rare” codons for “frequent” ones, we keep the ribosome from stalling, ensuring we get a steady and efficient protein production.

3.4. You have a sequence! Now what?

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

To produce this Protein E, we can use different technologies. For example, bacterial expression systems, mammalian cell cultures, or insect cell systems (infected with baculoviruses). Cell-free protein synthesis technologies also exist.

For the case of Protein E, I have decided to use bacterial systems technology, as it is the fastest and lowest in cost. Although it does not include post-translational modifications (PTMs), for the objectives of this production, this does not represent an issue.

Workflow for Protein E Production: To produce my Protein E, I need the following workflow. I will use E. coli for production, which requires two major components: 1) a plasmid and 2) the host cell. The Plasmid: This is the vehicle that allows us to integrate our gene of interest into an expression system. A recommended model is the pET plasmid system. It primarily consists of a promotor (T7) that is activated in a regulated manner (by adding IPTG). I will use restriction enzymes for cloning or Gibson Assembly technology to insert my sequence into the plasmid. To purify this protein, specific labels are used, such as a His-tag. The Cell (E. coli): For my gene to be converted into Protein E within E. coli, it first needs to be transcribed into mRNA. This process utilizes a T7 RNA polymerase, which reads the sequences and generates a complementary strand (mRNA). Codon optimization will ensure that the message structure is stable and efficient. Next is the translation of mRNA into protein, which occurs in the ribosomes. The ribosome attaches to the mRNA and reads its optimized codons. tRNAs (transfer RNA) bring the corresponding amino acids (aa), and the ribosome links them together, and then builds the amino acid chain of Protein E. Finally: Culture and Purification: Once the bacteria has produced the protein, it must be extracted. Using bioreactors or shake flasks, the bacteria are grown in nutrient-rich media such as LB. To release the protein, we must sonicate to disrupt the bacterial cells and use detergents to extract it from the membrane and keep it stable. Finally, using affinity chromatography techniques, we can purify it by using the His-tag.

Optimized DNA Sequence:

ATGTATAGCTTTGTGAGCGAAGAAACGGGCACCCTGATTGTGAACAGCGTGCTGCTGTTCCTGGCATTTGTGGTGTTTCTGCTGGTGACCCTGGCGATTCTGACCGCGCTGCGCCTGTGCGCCTATTGTTGCAACATTGTGAACGTGAGCCTGGTGAAACCGAGCTTTTACGTTTATAGCCGCGTGAAAAATCTGAACAGCAGCCGCGTGCCGGATCTGCTGGTG.

Part 4: Prepare a Twist DNA Synthesis Order

This part (from 4.1 to 4.6) was done succesfully and the plasmid is shown below.

Part 5: DNA Read/Write/Edit

5.1 DNA Read

(i) What DNA would you want to sequence (e.g., read) and why?

I want to sequence the E gene in hospitalized patients for more reliable biosurveillance. Unlike the Spike (S) protein, which mutates rapidly to evade immunity, the E protein is structurally essential and highly conserved. Its lower mutation rate makes it a more stable anchor for detection, ensuring consistent monitoring and fewer false negatives over time.

(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?

To monitor the E protein gene, I’ve chosen Illumina technology due to its high accuracy and ability to process multiple patient samples simultaneously. It would allow us not only to detect its presence but also to identify specific mutations, track variants, and confirm that our “anchor” (the E gene) remains stable.

1. Generation This is a second-generation technology (Next-Generation Sequencing ), as it utilizes massive amplification of fragments on a solid surface and detection via fluorescence.

2. Input and Preparation

The input is viral genomic DNA. Since SARS-CoV-2 is an RNA virus, we must first perform Reverse Transcription to convert its RNA into complementary DNA (cDNA), as sequencing technologies like Illumina only work with DNA. Regarding the samples, we use nasopharyngeal swabs (where the viral load is highest) or blood samples from hospitalized patients to monitor systemic presence.

Essential Preparation Steps:

Fragmentation: Cutting the DNA into small pieces. Adapter Ligation: Attaching sequences to the ends to fix them to the flow cell. Indexing: Adding “barcodes” to identify each individual patient. Enrichment PCR: Amplifying the library to ensure a detectable signal.

3. Essential Steps and Base Calling Bridge PCR (Amplification): Creates “clusters” of identical copies on the flow cell. Sequencing by Synthesis: Nucleotides with fluorescent terminators are incorporated one by one. Base Calling: A camera captures the color of each cluster; the software translates that color (e.g., Blue = C) into a nitrogenous base and assigns a quality score.

4. Output The final results are FASTQ files, which contain the sequences or reads and their respective quality values (Q-scores) for subsequent bioinformatic analysis.

5.2 DNA Write

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

I want to synthesize the codon-optimized gene for the SARS-CoV-2 Envelope (E) protein. While reading or sequencing allows us to monitor the virus, writing the DNA enables us to develop tools for detection.

Why synthesize the E gene? Synthesizing this specific sequence is critical for biosurveillance for three main reasons:

Safe Diagnostics: It allows for the creation of synthetic positive controls to validate PCR kits and biosensors without the need to handle the live, infectious virus.

Immune Response Monitoring: The synthesized protein serves as a stable antigen for serological assays, helping identify if patients are developing antibodies against the most conserved regions of the virus

Analysis of Structural Stability: Having the pure protein enables structural studies (such as cryo-EM) to predict how potential mutations might impact viral integrity.

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

To produce the Protein E gene, I would use silicon microchip-based DNA synthesis, mainly because this technology allows for the simultaneous writing of thousands of genes with high precision.

Its essential Steps are:

Digital Design: Fragmentation of the gene E sequence into shorter oligonucleotides.

Chemical Printing: Deposition of phosphoramidites via inkjet printing onto the silicon chip.

Elongation Cycle: Base-by-base construction (Coupling, Oxidation, Deprotection).

Assembly: Joining short fragments using PCR or Gibson Assembly to form the full-length gene.

Verification: Final quality control using NGS to guarantee the sequence is 100% accurate.

Limitations:

Speed: Delivery times can extend to weeks due to logistics and rigorous quality control steps.

Accuracy: The risk of errors increases proportionally with sequence length, requiring costly error-correction processes.

Scalability: Excellent for massive projects, but less efficient in terms of cost and time if only a single, isolated gene is required.

5.3 DNA Edit

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

I would like to edit the gene encoding the Cas13a protein to optimize its affinity for the guide RNA (gRNA), thereby creating a hypersensitive variant with enhanced detection capabilities.

gRNA Source: The gRNAs used in this system will be derived from a preliminary scan of suspicious sequences, specifically those exhibiting signatures of genetic engineering or a “human accent” (such as the use of artificially optimized codons). This allows the detection to be targeted toward synthetic threats created by malicious actors.

Why: In biosecurity, it is important to detect these sequences at extremely low concentrations. By editing Cas13a, it would improve its binding capacity and the collateral cleavage signal, enabling rapid detection upon gRNA recognition.

Application: This would facilitate the development of a rapid diagnostic kit to identify modified pathogens or synthetic threats in the field, eliminating the need for complex laboratory infrastructure.

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

I would use Prime Editing to perform these DNA edits. This technology is often referred to as a “genetic word processor” because it allows for high-precision search-and-replace capabilities without requiring double-strand breaks.

How it works and Essential Steps:

Prime Editing uses a specialized protein complex and a unique guide RNA to rewrite the target sequence:

Targeting: The pegRNA (prime editing guide RNA) directs the nCas9-RT (a fusion of nickase Cas9 and Reverse Transcriptase) to the specific site in the Cas13a gene.

Nicking: The nCas9 nicks only one strand of the DNA.

Reverse Transcription: The Prime Editor uses the "tail" of the pegRNA as a template to synthesize new DNA (containing the desired mutation) directly into the target site.

Flap Resolution: The cell’s natural repair machinery removes the old DNA strand and incorporates the new edited sequence, which permanently changes the Cas13a gene.

Preparation and Input:

Design Steps: The process involves designing a specific pegRNA that contains both the targeting sequence and the search-and-replace template for the desired Cas13a mutations. A secondary nick-gRNA is also designed to increase editing efficiency.

Input:

    Plasmid/mRNA: Encoding the Prime Editor protein (nCas9-RT fusion).

    pegRNA & nick-gRNA: Synthesized RNA or DNA templates for their expression.

    Host Cells: For example, a production strain of E. coli or a yeast system where the modified Cas13a gene will be hosted and expressed.

Limitations:

Efficiency: Prime Editing can be less efficient (lower percentage of successfully edited cells) than traditional CRISPR-Cas9, particularly in certain cell types.

Complexity: Designing effective pegRNAs is more complex than designing standard gRNAs and often requires multiple rounds of optimization to achieve the desired hypersensitivity in the protein.