Week 1 HW: Principles and Practices

I project to genetically modify cyanobacteria to enhance the production of proteins in bacteria. Cynobacteria are easy to grow in bioreactors and produce a high yield while being more water-efficient than other plant-based proteins.

Food autonomy is an important goal for sovereignty; therefore the governments should have an interest in heightening nutritional yield. However, genetically modified organisms can disrupt existing eco-systems. These existing eco-systems may play a key role in ecological balance, on which human lives depend. Existing eco-systems must be protected from genetically modified organisms.

Governance policy goals may be:

A. Prevent leakage into existing ecosystems B. Have society trust food containing Genetically Modified Microorganisms (GMM) C. Promote resource-efficiency above scaling production

Governmental actions to A. prevent leakage

A.1. Safe containers

Actors: Cyanobacteria farming company, industrial designers & bioreactor manufacturers, Health and Safety Executive UK (HSE) • Purpose: Build an environment from which the bacteria containing engineered genetics cannot escape. According to U.S.A.’s National Institute of Health’s guideline, a genetic modified organism escape rate below 1 in 108 cells is safe, which several biocontainment systems provide. (Varma et al., 2024) • Design: Bioreactor with maximal safety standard. • Assumptions: Bioreactors can fully prevent leakage. It is possible to clean the bioreactors without risking leakage. • Failure: Engineered DNA could be exchanged in the natural environment, rendering the natural environment more saturated with the substance that was initially developed for human mining. • Success:
GEOs present no threat to indigenous ecologies.

A.2. Synthetic Self-destruction

Actors: Scientists, collaboration across academic and companies conducting research

• Purpose: Engineer the GMM to self-destruct when escaping the built environment: When bacteria escape the bioreactor, the change of environmental features initiates the production of toxic proteins killing the bacteria. Another possibility is to initiate the knockout of genes necessary for competing with indigenous bacteria. Research in this direction is gaining momentum, even though most GMMs are cultivated in especially safe containers and labs.

• Assumption:

When exiting the built environment, the environmental conditions change significantly (light, ph, temperature, etc).

• Failure: If self-destruction fails, the genetic pool can contaminate indigenous ecologies. One downside, which is not yet a failure, is that the environmental needs of GMMs with inbuilt auxotrophy become more specific and might require more effort and energy.
• Success: Even when spilling into indigenous ecologies, GMMs are no threat,

A.3 & B.1: Environmental Impact Assessment

Actors: Scientists and their institutions, Health and Safety Executive UK (HSE), the public

• Purpose: It is required to submit a risk assessment to the government, which, with some reservations about competitive information, is available to the public. Safety measures to prevent threats to the environment are required by the HSE.
• Design: Risk assessment about potential risks for the human and for the environment and premises are registered.
• Assumptions: Scientists and companies follow the regulations. If followed, the regulations prevent threats to the environment and to the human.
• Failure: Breeches of the regulations go unnoticed. If an event occurs that was not considered in risk assessment, it may still cause GMMs to contaminate indigenous ecosystems.
• Success: By being demanding in the risk assessment, the probability for a spread reduces. B.1. Collaborate with a super-market to set up focus groups with diverse customers. Actors: Market-chain, scientists and customers
• Purpose: There are customer reviews of food products.
• Design: Explain the concept first, then ask how they feel about it and what concerns they might have. Assumptions: Scientists and companies follow the regulations. If followed, the regulations prevent threats to the environment and to the human.
• Failure: Breeches of the regulations go unnoticed. If an event occurs that was not considered in risk assessment, it may still cause GMMs to contaminate indigenous ecosystems.
• Success: Leakages are prevented because safety precautions were followed. B.2. Label the packaging with a campaign highlighting GMOs as resource efficient. Actors: Campaign-Designers, Packaging Designer, End-product’s company, producing company
• Purpose: Transparency: give customers the choice while influencing their choice with the campaign.
• Design: Communicate the presence of GMOs as a beneficial aspect with no tradeoffs. Assumptions: The bad reputation of synthetic food can be reversed if the benefits are well communicated.
• Failure: If the campaign is not well received, the reaction against the campaign can threaten the acceptance of GMOs in general and the public image of companies using GMOs.
• Success: The label on the packaging makes the product more desirable. C.1. Corelate the height of taxes to lower resource-consumption per Ton produced. Actors: Ministry of Finance, Company growing cyanobacteria
• Purpose: Scale production to a size that is resource efficient rather than outdoing competitors.
• Design: Reward resource efficiency Assumptions: The reward can be sufficient to guide choices by the company’s management
• Failure: The measure lowers tax-intake but has no consequence on the resource efficiency of Cyanobacteria
• Success: Nutrients are produced at maximum efficiency.

B.2. and C.1. Label the packaging with a campaign highlighting GMOs as resource efficient.

Actors: Campaign-Designers, Packaging Designer, End-product’s company, producing company

• Purpose: Transparency: give customers the choice while influencing their choice with the campaign.
• Design: Communicate the presence of GMOs as a beneficial aspect with no tradeoffs. Assumptions: The bad reputation of synthetic food can be reversed if the benefits are well communicated.
• Failure: If the campaign is not well received, the reaction against the campaign can threaten the acceptance of GMOs in general and the public image of companies using GMOs.
• Success: Users trust the prodcut and prefer GMOs for the resource efficiency.

In conclusion, measures A1, A2, A3/B1 should be implemented in parallel. Environmental Impact Assessment is directing laboratories to build the environment that can prevent the GMM to contaminate the gene pool of indigenous microorganisms. Simultaneously, bioreactors may not be 100% safe. Adding genetical self-destruction is a good backup. Lastly, labelling food comtaining GMOs might be counter-productive. I would therefore not implement that.

Homework Questions

Prof. Jacobson’s questions:

Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase?

1:(10 to the power of 6)

How does this compare to the length of the human genome?

Chat GPT prompt “what is the outcome of 10 to the power of 6 error rate in the 3.2Gbp during one replication with no repair mechanisms?” (3.2 x 10 to the power of 9) x 10 to the power of 6 = 3200 That are 3200 errors in each natural replication of DNA

How does biology deal with that discrepancy?

• According to the lecture slides, the human genome is ~3.2Gbp is long. The reference Lamers et al. Nature 407:711 (2000) on the slide with the title “Error Connection” describes a repair mechanism. Accordingly, in E. coli:
• the dimeric MutS protein recognizes mismatched pairs as well as small insertion or deletion loops in newly synthesized strands
• which are then repaired by ATP hydrolysis dependent binding of a homodimer of the ATPase MutL,
• which scans the strands until a strand-discrimination signal is encountered. In E.Coli, this signal is nicked (meaning there is no phosphodiester bond on the unmethylated strand) by MutH that binds to MutL.
• Helicase II then unwinds the DNA towards the mismatch
• and the according exonucleases removes the unmethylated strand until a ‘discrete site’ just beyond the mismatch
• In presence of single-stranded binding protein, DNA polymerase III forms a novel strand
• In presence of single-stranded binding protein, DNA ligase connects the novel strand

How many different ways are there to code (DNA nucleotide code) for an average human protein?

On the slide “How Many Base Pairs Do We Need To Synthesize?“, the standard human protein is 1036 bp long. There are 20 amino acids in the canonical genetic code, which are encoded with 64 possible triplet codons.

This same slide “Fabrication Complexity” gives the equation:

W= N! / (N/Q)! to the power of Q

Let’s assume each amino acid is encoded by codon triplets only (which is not the case naturally). Then N = 1036 bp : 3 = 345 Q is the number of different ways to code an amino acid, which can possibly range to 64 different codon triplets. Q is therefore 64. W=345!/(345/64)!to the power of 64 I have entered the equasion above in online calculators and could not find the correct result.

In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?

In the scientific article “Genomically Recoded Organisms Expand Biological Functions” quoted in the lecture’s slides explains an experiment that genetically recode organisms (GRO). In this experiment they redefine the stop-codon UAG, by removing the defining sequence RF1 and reassigning it to a nonstandard amino acid (NSAA). They found that the absence of RF1 had a negative effect on E.Coli’s fitness, while the NSAA had positive impact on the bacteria’s fitness able to outweigh the negative effect. Firstly, certain combinations of triplet codons affect each other in the use of resources for the production of non-essencial codon translation. Secondly, when several triplet codons are synonymous, it reduces the risk for disruptive mutations: consistency in genetic encoding is necessary to avoid unfortunate incorporation of amino-acids. If all 64 possible tripplets have different effects, then mutation in tripplets would cause more proteome-scale misfolding instead of going unnoticed. (Lajoie et al., 2013)

Dr. LeProust’s questions:

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

From Dr. LeProust slides, I could not tell for sure which is the most commonly used oligo synthesis. The slides suggest that Twist Bioscience is not yet the leading firm in synthetic DNA synthesis, however this firm does have superior processes. Transferring information from Prof. Jacobson’s slides I would suggest that Phosphoramidite DNA Synthesis Cycle is the most commonly used DNA synthesis. Phosphoramidite DNA Synthesis is also often mentioned in the timeline of innovation in DNA’s synthesis on Dr. LeProust’s slides and is the mentioned on Oligonucleotide Synthesis slide right at the start.

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

On the slide “We Continue to Push the Boundaries of Oligo Synthesis”, it is said that with enhanced chemistry, direct synthesis of 700mer was demonstrated. However, the next slide “Sometimes The Limitation is PCR, Not Synthesis”, inferring that direct synthesis would be able to make longer oligos more frequently if it were not for PCRs. The slide names chimeras as a major problem, which describes DNA sequences generated by the PCR with wrongly matched sequences. These errors can be reduced through later processing. Another issue described on the slide is a slower amplification of GC nucleotides, which results in an overrepresentation of oligos with more AT saturation. These limitations of PCR reduce the efficiency of direct synthesis.

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

Direct oligo synthesis only generates short DNA-sequences. When longer sequences are needed other processing steps are needed to join together sequences from earlier oligo synthesis. One example of such a process is roughly described on the slide “Got Extremely Difficult Sequences?”.

George Church’s questions:

What are the 10 essential amino acids in all animals?

Aspartate, Histidine, Cysteine, Methionine, Asparagine, Lysine, Arginine, Tyrosine, Threonine, Serine

How does this affect your view of the “Lysine Contingency”?

The Lysine Contingency is a fictive element to Jurassic Park, a series about Dinosaures on a island. The dinosaurs were genetically modified to lack the ability to synthesize lysine, therefore being dependent on eating lysine-rich food. However, Lysine being an amino acid means it is not synthesized by animals, instead it is a building block for proteins. Animals do absorb lysine from nutrients and there are lysine rich nutrition in the world outside the dinosaurs’ island.

What code would you suggest for AA:AA interactions?

As far as I understand AA stands for amino acid and NA for nucleic acid. I guess I would suggest using the letters assigned in the graph visualizing the genetic code on slide #3. As I do not understand how it relates to slides #2 & 4, I wonder whether the answer relates to the complexity of the 10 essential amino acids for animals, which may be harder to synthesize than Polymers. In this case I do see that the little black cubes represent the AA main chain.

Given the one paragraph abstracts for these real 2026 grant programs sketch a response to one of them or devise one of your own:

(Apologies, I could not respond to this question, I am not an advanced student and ran out of time.)

Sources:

Varma, S., Gulati, K.A., Sriramakrishnan, J., Ganla, R.K. and Raval, R. (2024). Environment signal dependent biocontainment systems for engineered organisms: Leveraging triggered responses and combinatorial systems. Synthetic and Systems Biotechnology, [online] 10(2), pp.356–364. doi:https://doi.org/10.1016/j.synbio.2024.12.005. https://www.sciencedirect.com/science/article/pii/S2405805X24001571

https://www.science.org/doi/epdf/10.1126/science.1241459 Lajoie, M.J., Rovner, A.J., Goodman, D.B., Aerni, H.-R., Haimovich, A.D., Kuznetsov, G., Mercer, J.A., Wang, H.H., Carr, P.A., Mosberg, J.A., Rohland, N., Schultz, P.G., Jacobson, J.M., Rinehart, J., Church, G.M. and Isaacs, F.J. (2013). Genomically Recoded Organisms Expand Biological Functions. Science, 342(6156), pp.357–360. doi:https://doi.org/10.1126/science.1241459.

https://kb.ezbiocloud.net/home/science-blogs/basics/chimeras

Week-02-Homework-dna-read-write-and-edit

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project