Week 1 HW: Principles and Practices

1) Describe a biological engineering application or tool you want to develop and why

At my lab, we came across the problem that, after doing the analysis of the best peptides of interest, we're unable to correctly asses the complete presentation of such peptides in the trained T cells, and we don't know if it is because of the lack of translation (peptide is not being produced), or if it's a lack of presentation (peptide is not going through the ER to an MHC, or not going through the Golgi apparatus to the outside of the cell). Thus, it would be very usefull to be able to use a circuit to express fluorescence when peptide is presented, and also, inside the cell, to know with fluorescence microscopy, the specific location of the peptide if it's inside the cell, or just not at all translated. This is also a general problem we're finding in T Cell Therapy, and could also target the problem where we don't usually have standards for Mass Spec analysis. In views of this problem and, after a little bit of research, I found a good inspiration paper.

I would like to develop a circuit similar to the one described by Ayano, Mohammad et al. (2025), in their paper "High-throughput discovery of MHC class I- and II-restricted T cell epitopes using synthetic cellular circuits". My ideal circuit would use the peptides of interest that have gone through the first filters of utility before being implemented into a cancer vaccine for clinical trials. I could USE APCs as well as we're also interested in understanding MHCII expression and would help to understand better the self regulation and long memory of the immune system.

2) 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.

1 non-malfeasance

2 Responsible use of personalized medicine

3 Access equity

4 Prevention of dual use of the technology

Governance of this technology should prioritize patient safety, responsible handling of biological data, equitable access, and prevention of misuse.

3) Describe at least three different potential governance “actions” by considering the four aspects below (Purpose, Design, Assumptions, Risks of Failure & “Success”)

Action: Regulation requisit for a validation prior to clinical impact

Purpose:

Currently, there's not always a required and standard validation method before results of epitope research gets to clinical decisions. The proposal or getting a multicentric of validation of the peptide prior to clinical impact witll help prioritize will help tackle this problem.

Design:

Main actors would be the FDA, Health Canada or EMA, as well as ethics committees, hospitals and private sector.

Assumptions:

• Regulators have the availability of rapid analysis
• Standars are not stopping innovation
• There’s a consensys about which is “enough evidence” of success for translaiton of epitopes to vaccines.

Risk of failure and success:

• Failure: Too much burocracy.
• Success: Can create barriers of entry that will make it difficult for small research groups to join.

Action: Incentives for open source data and reproducibility proof

Purpose:

Promote data sharing, generate open standards and promote transparent reporting.

Design:

Main actors would be financing agencies (NIH, CIHR), NGOs, Fundations, journals and universities.

Assumptions:

• Researchers have the willing to share
• Openess enhances quality and trust
• Intellectual Property won’t be affected

Risk of failure and success:

• Failure: Some companies might step back or patent aggressively before sharing.
• Success: There could be some actors with bad intentions that could use information to optimize harmful peptides.

Action: Ethics-by-design

Purpose:

Incorporate ethical limits from the design of the project, instead of after the technology has been developed.

Design:

Main actors would be bioengineers, companies, institutional committees, providers.

Assumptions:

• Technical filters really limit misuse of information.
• Users won’t look for alternative access.

Risk of failure and success:

• Failure: Systems might be easy to jump / false security.
• Success: Could limit research.

4) Score governance actions

5) Describe which governance option, or combination of options, you would prioritize, and why.

For the proposed actions mentioned above, I would combine the first and the third, and then implement the second option: 1) require peptide validation prior to the clinical stage and promote experimental design alongside the development of ethical guidelines to be met, and 2) encourage transparency.

To design experiments in the best possible way and ensure compliance with all necessary regulations, it is imperative to develop the research in parallel with ethical requirements. Within these ethical requirements, the importance of peptide validation prior to clinical trials should also be emphasized. If ethical standards and validation are met, moving on to clinical and industry stages should be faster and more appropriate, while also facilitating the bureaucratic steps for acceptance.

Finally, implementing transparency incentives would reduce time in peptide/antigen development, as it would prevent redundancy in research (i.e., repeating tests on antigens already shown to be ineffective) and would also save significant resources. Moreover, making this information publicly available could improve accessibility for laboratories with limited capacity for antigen discovery, enabling the application of already known antigens.

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Homework questions: Professor Jacobson

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

The error rate of DNA polymerase is approximately 1 error per 10⁵ nucleotides. This demonstrates that DNA polymerase is incredibly accurate; however, it does have its limitations.

The human genome contains about 3.2 billion base pairs and, if we consider the polymerase error rate with these numbers in mind, we would realize that it would make around 32,000 errors every time a cell divides. This level of mutation, for a complex organism such as a human being, would be concerning.

However, to prevent this, nature has developed mechanisms to avoid such high levels of error. First, there is base selection, in which only the correct nucleotide fits geometrically into the active site. Second, there is a proofreading system in which a 3′→5′ exonuclease activity detects errors, backs up, removes the incorrect base, and replaces it. Finally, there is also the mismatch repair system, in which specialized proteins scan the newly synthesized DNA after replication to correct errors that may have escaped base selection and proofreading.

2. How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?

A human protein can contain around 400 amino acids. There are 64 possible codons encoding only 20 amino acids, meaning that most amino acids have multiple codon options; on average, there are about three codons per amino acid. The reason not all codons function equivalently is due to codon usage bias, the presence of mRNA secondary structures, critical splice sites, specific folding kinetics, and regulatory signals.

Homework questions: Dr. LeProust

What’s the most commonly used method for oligo synthesis currently?

The most widely used method today is the phosphoramidite method. This approach was developed by Caruthers in the 1980s and, instead of synthesizing DNA in the 5′→3′ direction, it proceeds in the reverse 3′→5′ direction.

Why is it difficult to make oligos longer than 200nt via direct synthesis?

There are several main reasons:

Exponential yield problem. Even with a coupling efficiency of 99.5%, the final yield of the correct product decreases exponentially as the chain grows. For a 20-mer oligonucleotide, the purity is approximately 90%; for a 100-mer, about 60%; and for a 200-mer, around 36% purity. Beyond 200 nucleotides, most of what comes out of the synthesizer is not the desired full-length sequence, but rather a mixture of truncated sequences that are nearly impossible to separate in order to obtain the intended final product.

Depurination. This is an acid-induced form of damage that can accidentally break the bond between the adenine or guanine base and the DNA sugar. Since each synthesis cycle uses acid to remove the DMT protecting group, by the time a strand reaches 200 bases and has been exposed to acid 200 times, the risk that at least one base has undergone depurination becomes high. This can lead to mutations.

Accumulation of mutations from failed coupling events. Some chains that fail to couple properly can become “reactivated” in later cycles. This generates mutations similar to deletions and, in long sequences, purification techniques have greater difficulty separating the affected strands.

Physical limitations of the solid support. The pores in controlled pore glass (CPG) beads used for these reactions can become filled or occupied as the strand grows. This can block the pores and cause entanglement, leading to decreased purity and reduced reaction efficiency.

Why can’t you make a 2000bp gene via direct oligo synthesis?

There are physical and chemical limitations that make this impossible. There is a yield collapse, since each step has approximately 99.5% efficiency. For a 2,000-base chain, the probability that only about 0.00004% of the molecules are synthesized correctly is extremely low, meaning that 99.996% of the product would be a mixture of erroneous sequences.

This leads to the next limitation: purification. At that point, it would be virtually impossible to purify the obtained molecules and isolate the correct full-length product.

Homework Question: George Church

[Using Google & Prof. Church’s slide #4] What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

The 10 essential amino acids in all animals (i.e., those that animals cannot synthesize de novo and must obtain from their diet) are:

• Histidine (His, H)
• Isoleucine (Ile, I)
• Leucine (Leu, L)
• Lysine (Lys, K)
• Methionine (Met, M)
• Phenylalanine (Phe, F)
• Threonine (Thr, T)
• Tryptophan (Trp, W)
• Valine (Val, V)
• Arginine (Arg, R) (essential in many animals and conditionally essential in adults, but required in all animals at some stage)

Taking into account that the Lysine contingency is that making an organism dependent on an amino acid like lysine or a lysine analog for survivalcan work as a biocontainment strategy, this makes the lysine contingency an interesting point of view.

If we use lysine as a contingency factor when doing genetic engineering, lysine becomes metabolically central. Because animals cannot synthesize lysine, its availability is already tightly controlled by diet. So it can be used perfectly as a correct way of avoiding that the lab experiments -cells or organisms- cannot scape the lab as they won't be able to find the intake needed for Leucine if they're not in a lab environment.