Week 1 HW: Principles and Practices

1. First, describe a biological engineering application or tool you want to develop and why.

I’m passionate about both food and biotechnology and seek to combine the two discipline together for my future career. A biological engineering application that I would love to work on is precision fermentation production for alternative sustainable protein sources, specifically β-Lactoglobulin (BLG) protein for recombinant milk production. I was inspired by a PhD student’s publication at Wageningen University & Research while I was searching for a university to earn my Master’s degree. Indonesia’s demand for milk and dairy products has been increasing over the years, and our climate isn’t suited for dairy cattle farming compared to other countries with a more appropriate climate and productive dairy cows. Not to mention the environmental impact of dairy farming, along with the scarcity of arable land, are also factors to be considered to supplement part of the industry with precision fermentation (Hoppenreijs, 2024).

According to our Ministry of Cooperation’s estimation, 80% of Indonesia’s milk is still imported from overseas. With precision fermentation of milk proteins, Indonesia could potentially reduce its reliance on imported milk and redirect that funding to other industries that require it. Precision fermentation is also an emergent technology that’s not widely taught and researched in Indonesia. I would one day like to learn everything about it, so I would be able to bring that knowledge and its application to my motherland.

There have been commercializations of recombinant milk made from non-animal-derived dairy proteins, especially in developed countries with a thriving dairy industry, such as Perfect Day in the USA, Remilk in Israel, Verley Food in France, All G in Australia, and several others. These companies have one thing in common: utilising genetically engineered microorganisms (most often yeast and fungi) as a “cell factory” to produce non-animal dairy proteins for their recombinant milk production. While my idea isn’t novel on a global scale, it is novel in Indonesia and tackles many issues faced by Indonesia’s dairy industries, such as Indonesia’s indigenous dairy cattle, the Balinese Cattle, being low milk producers despite their high fertility rate compared to other breeds of cattle (Suryani et. al., 2017).

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.

My Main Goal: Ensuring non-malfeasance in the production and commercialization of synthetically produced BLG and BLG-derived products such as recombinant milk and dairy products.

The Sub Goals:

• Food safety: Provide pre-market assessment for BLG synthesised through the precision fermentation of genetically modified microorganisms and their derived product to ensure that they are nutritionally identical and as safe and fit for consumption as their conventionally produced counterparts (FAO, 2008). These can be supported by strict monitoring and assessment by Indonesia’s Biosafety Commission of GMOs.

• Environmental safety: Evaluate the biosafety of the production process of synthesised BLG and the finished products along with their derivatives to assess the possible effects on the environment and biodiversity of Indonesia (FAO, 2008). Strict regulations and policies must be adhered to prevent harm to Indonesia’s environment.

• Knowledge sharing and dissemination: Share and disseminate the technical knowledge of the overview on precision fermentation to promote the application of this emergent technology in various industrial applications for Indonesia, such as for pharmaceutical purposes.

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

 Conducting food and environmental safety inspection along with pre-market assessment of BLG and its derivatives, creating standardised labelling, and establishing a clear legal category for precision-fermented proteins within Indonesia’s national food law framework.

1. Purpose: Due to synthetic proteins made through precision fermentation still being inside many regulatory and legal grey areas in Indonesia, a more rigorous safety and risk assessment should be conducted to ensure the health and safety of the manufacturers, the consumers, and the environment.
2. Design: Various government agencies and regulatory bodies, such as the National Agency for Drug and Food Control, the Biosafety Commission of GMO, and the National Standardization Agency of Indonesia. The assessment of alternative proteins is funded by the applicant company, while the regulatory system itself is paid for by the state budget of Indonesia.
3. Assumption: The applicant company would follow all the regulatory and safety assessments.
4. Risk of Failure and “Success”: Corruption and fraud that result in improper testing of a genetically modified product could lead to violations of health and safety standards, potentially jeopardising the health of consumers and the environment.

 Increasing the national budget for research grants and public funding for research into precision fermentation.

1. Purpose: To increase the amount of research into precision fermentation in Indonesia, enabling the country to compete with developed nations in the sphere of biotechnology advancement and incentivising prospective researchers.
2. Design: The Ministry of Finance would be responsible for allocating funds for the increase of research budget from the state budget of Indonesia, while the researchers are responsible for using the allocated budget responsibly within the scope of the research into precision fermentation.
3. Assumption: The Ministry of Finance deems precision fermentation important enough to increase the state funding for research, and for there to be no corruption and mismanagement of said funding by either the government or the applicant company/research institute developing the precision fermentation technology.
4. Risk of Failure and “Success”: Failure to secure the funding or the mismanagement of the fund could lead to the research into precision fermentation in Indonesia being stalled, and the further dissemination of the knowledge of precision fermentation in Indonesia being halted.

 Create public education guidelines for recombinant food proteins.

1. Purpose: Educating the public and managing public perception on precision-fermented proteins and GM products is crucial for building public trust in a product that many Indonesians might not be aware of. It is also an anticipatory act to prevent the spread of misinformation from either the uninformed public or malicious parties by providing scientific FAQ’s backed by influential neutral parties in Indonesia, such as government regulators, religious institutions (which is a big influence in Indonesia), such as the halal certification boards, and other scientific authorities.
2. Design: As said before, there are many neutral parties involved in creating public education guidelines for recombinant food proteins. Government institutions such as the National Agency for Drug and Food Control, the Biosafety Commission of GMO, the Ministry of Health, and the National Research Institute. Due to Indonesia’s major Islamic population, reassurance from halal certification bodies would also help immensely on alleviating public preconception notions towards GM products. Other scientific authorities, such as university researchers and food technologists, would also assist on educating the public due to being perceived as less biased than companies and the government.
3. Assumption: These institutions managed to convey the safety from the health, environmental, ethical, and religious angles of precision-fermented products and their derivatives.
4. Risk of Failure and “Success”: It’s possible that failure in educating the public would lead to rejection of future precision-fermented products, halting the advancement in the research of precision fermentation, not just for food application, but also other novel applications, such as pharmaceutical applications that can save and improve the lives of many others.

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.

While research into precision fermentation of alternative proteins is still in its infancy in Indonesia, I believe that implementing the food and environmental regulatory policies on the subject of precision-fermented products should be prioritised just as much as the research on precision-fermented products as a governance option. Based on the Government Regulation No. 21/2005 on Biosafety of Genetically Engineered Products, Indonesia takes a more cautionary approach towards genetically modified organisms and related products, which will be evaluated by the Biosafety Commission. Due to the novelty of BLG and other precision-fermented proteins in Indonesia, it would be prudent to lay out the legal groundworks first for future GM proteins.

Technically speaking, Indonesia already uses GM products for commercial purposes, such as imported GM soybeans for our tempeh and tofu industry and corn livestock feeds. However, these uses remained in the upstream, which remains fairly invisible to the public eye, rather than branded foods. Commercialization of precision-fermented protein would be a new territory, and thus, new laws and regulations should be enacted for these products first. This is not meant to discourage the proliferation of precision-fermented products, but the opposite. It is for incentivizing research and commercialization of these products. If the laws and regulations of the country are strict and uncertain, then potential investors of the research wouldn’t invest due to the uncertainty if the product could be sold in the first place. The Porter Hypothesis posits that strict, well-designed environmental regulations can trigger innovation, enhancing corporate productivity and competitiveness to outweigh compliance costs (Ambec et. al., 2011). It is better for Indonesia to make laws clear first on precision-fermented products for the interest in the research to increase to the point where the commercialization of this technology is economically viable.

However, this is all with the assumption that precision fermentation technology is already advanced in Indonesia to the point where commercialization becomes the next step. The fact of the matter is, Indonesia is not quite there yet. Indonesia is not scientifically incapable of producing precision-fermented milk proteins; there are plenty of research in Indonesia around industrial fermentation, though mostly on natural microbes. Rather, the absence of regulatory clarity, pilot-scale facilities, and commercialization frameworks prevents translation of existing microbiological research into an alternative protein industry. Because of this, increasing awareness of precision fermentation technology and its myriad of applications would not only increase the research interest among Indonesia’s scientific community, but also help the government realise its significance to justify increasing national research funding.

References:

Ambec, S., Cohen, M. A., Elgie, S., & Lanoie, P. (2011). The Porter hypothesis at 20: Can environmental regulation enhance innovation and competitiveness? Resources for the Future Discussion Paper No. 11-01. Social Science Research Network. https://ssrn.com/abstract=1754674

Food and Agriculture Organization of the United Nations. (2009). GM food safety assessment: Tools for trainers. FAO. https://www.fao.org/4/i0110e/i0110e.pdf

Hoppenreijs, L. (2024). β-lactoglobulin by precision fermentation: Application-driven design and processing (PhD’s thesis, Wageningen University & Research). Wageningen University Repository. https://groenkennisnet.nl/zoeken/resultaat/β-lactoglobulin-by-precision-fermentation-:-application-driven-design-and-processing?id=1413396

Republic of Indonesia. (2005). Government Regulation No. 21 of 2005 on Biosafety of Genetically Engineered Products. https://peraturan.bpk.go.id/Details/49379/pp-no-21-tahun-2005

Suryani, N. N., Suarna, I. W., Sarini, N. P., & Mahardika, I. G. (2017). Increasing energy ration of Bali cattle to improve digestible nutrient, milk yield and milk quality. International Research Journal of Engineering, IT and Scientific Research, 3(1), 8–17. https://sloap.org/journals/index.php/irjeis/article/view/526

Week 1 HW: Week 2 Lecture Prep

Questions from 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?
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?

Answer:

1. The error rate is one in a million. Compared to a human genome, which has approximately 3 billion base pairs. Any mispaired bases in DNA replication are detected by DNA polymerase through a process called proofreading and replaced with the correct base pairs through a process called mismatch repair.
2. Because of the codon usage bias. Codon usage bias is the preferential use of certain synonymous codons in an organism. It occurs because different codons are decoded by tRNAs that exist at different cellular abundances. Codons corresponding to abundant tRNAs are translated faster and more accurately, so they are favored, especially in highly expressed genes. Conversely, genes that contain codons corresponding to rare tRNAs are translated more slowly and are more prone to ribosome pause and translational errors.

Homework Questions from Dr. LeProust:

1. What’s the most commonly used method for oligo synthesis currently?
2. Why is it difficult to make oligos longer than 200nt via direct synthesis?
3. Why can’t you make a 2000bp gene via direct oligo synthesis?

Answer:

1. Solid Phase Synthesis.
2. Direct chemical DNA synthesis is limited by cumulative coupling efficiency. Each nucleotide addition step has about ~99% success, but a 200-nt oligo requires 200 consecutive successful reactions. The probability of obtaining a full-length product is therefore ~0.99²⁰⁰ ≈ 13%, meaning most synthesized molecules are truncated because they failed to extend during earlier cycles. These truncated strands differ from the correct product by only one or a few nucleotides, making purification difficult. Techniques such as HPLC or denaturing PAGE have limited resolution at longer lengths, so separating 200-nt DNA from 199-nt DNA results in low recovery and contamination.
3. The reasoning is similar to my answer to Question 2. During chemical DNA synthesis, each nucleotide addition (coupling step) has about a 99% efficiency. Because the process is stepwise, the probability of obtaining a full-length product decreases multiplicatively with every cycle. For a 2000 bp sequence, the probability of producing a correct molecule is approximately 0.99²⁰⁰⁰ ≈ 0.000000186%. This means essentially none of the synthesized molecules will be the correct full-length gene. Instead, it is more practical to synthesize shorter oligonucleotides (e.g., ~100 nt each) and assemble them into a full gene using methods such as Gibson assembly or overlap-extension PCR, which bypass the chemical synthesis length limitation.

Homework Question from George Church:

1. What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?

Answer:

1. Animals require ten essential amino acids in their diet: arginine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, and valine. These amino acids are termed “essential” because animals cannot synthesize them and must obtain them from dietary protein. Though Arginine can be considered conditionally essential.

   Although I have not watched the Jurassic Park or Jurassic World films, I read about the “lysine contingency” used as a biological containment strategy devised by Dr. Wu. The idea is that the dinosaurs were engineered to be unable to synthesize lysine and would die without lysine supplementation. However, this would not be an effective containment mechanism. Lysine is an essential amino acid for all animals, meaning the dinosaurs (like any other animal, including humans) would naturally obtain it by consuming protein sources in their environment. In fact, this trait would not be unique to the dinosaurs, because all animals already lack the ability to synthesize lysine.

ChatGPT Prompts:

• What is lysine contingency?

Week 2 HW: DNA Read, Write, & Edit

Part 1: Benchling & In-silico Gel Art

For this week’s Part 1 homework, students are tasked with doing several tasks. First, create a Benchling account. Second, simulate restriction enzyme digestion of a predetermined sequence using several enzymes. Third, once students are able to simulate the restriction enzyme digestion, they can create a simple art form named the Latent Figure Protocol.

For the first part of the task, I would need to create an account for Benchling. It was a relatively simple process of using my Gmail account to sign up for Benchling. A few minutes later, after entering my details, I am now the proud owner of a Benchling account.

Next, I would need to import the predetermined sequence into Benchling. For this exercise, we were given a Lambda DNA template from New England Biolabs (NEB). According to the website, the Lambda DNA is a commonly used DNA substrate isolated from bacteriophage lambda (cI857ind 1 Sam 7) with a length of 48,502 base pairs.

I entered the FASTA format of the sequence into the Create DNA/RNA Sequence tab and clicked Create, creating my first (and hopefully not my last) Benchling project. I named the project Lambda_NEB for this exercise, the same name as the product listed in NEB.

Now comes the first challenge, simulating a restriction enzyme digestion using several of the following enzymes:

• EcoRI

• HindIII

• BamHI

• KpnI

• EcoRV

• SacI

• SalI

Bioinformatics was one of my weakest subjects during my university days, years ago. And due to switching to the Fast-moving Consumer Goods industry for several years, I didn’t keep up with the current biotechnology trends as much as I’d liked. For this reason, I needed some time to refresh my memories and get acquainted with the UI of Benchling, a bioinformatics tool I never used before. Luckily, Ice’s YouTube video on Benchling basics helped me start on the task at hand.

After familiarizing myself with the UI of the website, I managed to find the Digest function and start with the next part of this homework. I entered EcoRI as my first restriction enzyme digestion simulation and made my first virtual gel image

After this, I repeat this with the rest of the enzymes listed.

Then I started to think about what pattern I would like to create. Due to my novice nature with Benchling, I listed several criteria to help narrow down a pattern I would create. It has to be simple but distinctive enough to be unique or at least memorable, have some connection to me to give it an identity, and tell a story. After some deliberation, I decided to try making a number 13 for my gel art due to being born at Friday the 13th. For many people, the number 13 is often associated as an omen of bad luck. I used to believe in that too when I was very young. I don’t anymore and am now a firm believer of lucky number 13.

After several tries of experimenting with different restriction enzymes, I chose the image above as my final draft. I used the ladder as the number 1, AjuI as the three overhang strips for the 3, and a combination of BamHI, EcoRI, and EcoRV as the back segments of the 3. Though, as seen above, it isn’t a perfect three with a few segments missing to complete it. While I would love to have a more accurate number 13 picture, finding the right restriction enzyme to fill the last top third of the number 3 segments is already taking too much time. And with my unfamiliarity with Benchling, it would be like trying to find a needle in a haystack to create the perfect number 13.

Unfortunately, due to not having access to a laboratory, I wouldn’t be able to perform a gel electrophoresis and gel visualization and would have to make do with the in-silico visualization.

Part 3

sp|P02754|LACB_BOVIN Beta-lactoglobulin OS=Bos taurus OX=9913 GN=LGB PE=1 SV=3 MKCLLLALALTCGAQALIVTQTMKGLDIQKVAGTWYSLAMAASDISLLDAQSAPLRVYVE ELKPTPEGDLEILLQKWENGECAQKKIIAEKTKIPAVFKIDALNENKVLVLDTDYKKYLL FCMENSAEPEQSLACQCLVRTPEVDDEALEKFDKALKALPMHIRLSFNPTQLEEQCHI

The amino acid sequence above is for the protein Beta-lactoglobulin (BLG). BLG is the most abundant whey protein in milk, responsible for most of dairy milk’s emulsion stability, viscosity, and gelling properties (Hoppenreijs, 2024). It can be equated that BLG is responsible for dairy milk’s taste and texture, leaving plant-based milk often with inferior palatability compared to its conventional counterpart. As stated in my previous homework, Indonesia faces a problem of increasing dairy demand and its inability to meet domestic demand. The precision fermentation of BLG would be a step towards resolving this problem without overly relying on dairy imports and further burdening the national budget.

Using the reverse translation tools of Bioinformatics.org, I managed to reverse translate the BLG protein into the most statistically likely used DNA sequence and the degenerate DNA sequence that uses IUPAC degenerate nucleotide codes to show the different nucleotide possibilities.

The image above shows the sequence of most likely codons.

While this one shows the sequence of consensus codons. Notice how there are letters other than atgc? That is the IUPAC degenerate nucleotide codes. Due to codon degeneracy, in which multiple codons code for the same amino acids. R could either code for A or G, Y for C and T, and so on and so forth.

Next, I run the sequence through a codon optimizer tool and selected Saccharomyces cerevisiae or commonly known as Baker’s Yeast, as the intended organism modified with the BLG sequence. I specifically chose Baker’s Yeast over the easier-to-grow bacteria such as Escherichia coli due to one reason. Using bacteria as the host organism can lead to several post-translational modification issues, such as the generation of inclusion bodies due to their prokaryotic nature and the complexity of eukaryotic proteins and other more complex bioactive compounds, such as bovine BLG (Gao et. al., 2025). Yeasts and fungi have advantages over bacteria as cell factories due to their eukaryotic nature capable of post-translational modification and high yield (Deng et. al., 2026).

As previously stated, I intend to use precision fermentation to produce BLG protein, hopefully on an industrial scale in the future. To do this, I would need a genetically modified microorganism able to synthesize BLG. With precision fermentation, I could selectively ferment BLG protein in a bioreactor and recover the protein using different recovery methods, whether the particular protein is intracellular or extracellular (Deng et. al., 2026).

optimised reverse translation of sp|P02754|LACB_BOVIN Beta-lactoglobulin ATGAAATGCTTGTTGTTGGCGTTAGCATTGACATGTGGTGCTCAAGCTTTGATTGTTACCCAAACAATGAAAGGTTTGGATATTCAAAAAGTTGCTGGTACTTGGTACTCCTTGGCAATGGCTGCCTCTGACATTTCTTTATTGGATGCCCAGTCTGCACCATTGAGAGTATATGTCGAAGAATTGAAGCCAACTCCTGAGGGTGATTTAGAAATCCTTTTGCAAAAATGGGAAAATGGTGAATGCGCCCAAAAAAAAATTATTGCCGAAAAGACAAAAATCCCAGCAGTCTTTAAAATTGACGCATTGAACGAAAATAAGGTATTAGTTTTGGATACTGATTACAAGAAATACTTGTTGTTTTGTATGGAAAATTCAGCTGAACCAGAACAATCATTGGCCTGTCAATGCCTTGTTAGAACCCCAGAAGTGGACGATGAAGCTTTAGAAAAGTTTGATAAAGCCTTAAAAGCACTACCAATGCATATTAGATTATCTTTTAACCCAACCCAACTTGAAGAACAATGTCATATT

As stated in the previous lectures and homework, each organism has a preferential use of certain codons for synonymous amino acids, known as codon usage bias. This is due to the fact that different codons are decoded by tRNAs that exist at different cellular abundances. The tRNAs abundant in a mammal, such as a cow, would be very different from those in yeasts, such as Baker’s Yeast. Without this optimization would be slower translation speed, produce ribosome stallings, be more prone to protein misfolding, and have lower yields, not ideal for industrial scale-up.

4.1. Create a Twist account and a Benchling account

Week 1 Lab: Pipetting

Individual Final Project

Group Final Project