Week 1 HW: Principles and Practices

Class Assignment — DUE BY START OF FEB 10 LECTURE

(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.

By leveraging biological engineering tools, such as CRISPR systems, I would like to develop highly specific nucleic acid biosensors and synthetic circuits to detect M. tuberculosis and resistance mutations with high precision and speed. The inspiration for this comes from working on my MSc project, where I studied the genomic epidemiology of multi-drug-resistant tuberculosis (MDR-TB) using WGS data. My work focused on downstream analyses (phylogenetics, transmission clustering, regression, and machine learning), with particular attention to population structure and epidemiological interpretation. However, when working on my project, I found that genomic data of MDR-TB is geographically imbalanced, limiting the representativeness of global MDR-TB patterns and, ultimately, timely detection and treatment. This is especially true in high burden countries. As a result, I would like to explore the application of biosensors and genetic circuitry to add an additional layer of surveillance alongside traditional methods; biosensor or genetic circuit engineered to detect specific MDR-TB resistance markers or lineage-specific sequences, potentially using luminescence as a real-time readout to provide rapid, high-throughput signals.

Brief on the biology and possible mechanism for the tool: 🛠️ 🧬

Unlike many other bacteria that can share drug‑resistance genes with each other through horizontal gene transfer, Mycobacterium tuberculosis mainly becomes drug resistant through mutations in its own DNA (Single Nucleotide Polymorphisms (SNPs), insertions/deletions (indels)) [1]. Simultaneously, the ability of M. tuberculosis to persist within human hosts exposes it to prolonged immune pressure, driving adaptive changes in virulence‑associated loci such as phoR, mymA and the mce1 operon that can influence how different lineages transmit or interact with particular human populations [2¬–4]. As a result, the proposed bio-engineering tool could take the form of a bio-sensor, where CRISPR-based device could be programmed to recognise TB resistance mutations or an engineered genetic circuit that only produces a light or electrical signal when multiple resistance signatures are present. Such a device would convert the presence of specific mutations into a measurable output that can be rapidly read and fed into surveillance models.

(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.

Governance Goal 1: Prevent harm or misuse

As genomic data can be geo-located and time-stamped, there are risks for community stigmatization and political duress. Therefore, to mitigate against these risks, the governance goal should implement frameworks that: (i) Require ethical review and oversight of bio-sensor data and its secondary uses (ii) Establish strict guidelines on the limits of how precise location data can be shared or publicized (iii) Establish clear accountability mechanisms for state and private actors

Governance Goal 2: Promote equity in data collection, analysis and development

To prevent further exacerbation of inequities biological data collection and usage, the framework will implement mechanisms that ensure: (i) Control of locally generated data by implementing country (ii) Inclusion of implementing country as equal partners in analysis and interpretation (iii) Prioritization of under-sampled regions to improve representativeness and combining outputs with timely access to treatment and care.

(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.).

1. Purpose: What is done now and what changes are you proposing?
2. Design: What is needed to make it “work”? (including the actor(s) involved - who must opt-in, fund, approve, or implement, etc)
3. Assumptions: What could you have wrong (incorrect assumptions, uncertainties)?
4. Risks of Failure & “Success”: How might this fail, including any unintended consequences of the “success” of your proposed actions?

Governance Action 1: Regulation and creation of standards for early-stage bio-sensor development

Purpose: Early-stage bio-sensor development research is guided by bio/genetic engineering but requires safety and bio-security risks. I am proposing specific standards and regulatory requirements for early-stage biosensor design, ensuring safety, transparency, and responsible innovation before deployment. This could be in the form of new regulatory support or reference diagnostics.

Design: Actors may include public health agencies, national regulators in science, and diagnostic developers. Establish validation criteria, accuracy thresholds, metadata standards, and geolocation safeguards. In addition, embed standards into existing public health TB surveillance programmes.

Assumptions: This initiative assumes that regulators will be quick to evaluate bio-sensor technologies. Also assumes public health surveillance will be quick to agree and implement technology across the existing surveillance system.

Risk of failure: Bureaucracy may hinder technological innovation and deployment. Unintended consequences include a premature reliance on bio-sensor technology which could lead to false positive cases and mis-directed public health strategies.

Governance Action 2: Pre-sequencing rapid signal regulatory pathways

Purpose: Currently, bio-sensor outputs such as CRISPR signals and genomic data are not integrated in low to middle-income countries Therefore, I would like to propose the creation of formal pathways that enable rapid biosensor signals to feed into surveillance systems before whole genome sequencing (WGS), with defined quality, privacy, and data use standards.

Design: Actors include public health agencies, national regulators in science, and diagnostic developers. Actors may also include international bodies such as the WHO. There may be potential to expand the WHO’s ‘attributes and principles on genomic data-sharing platforms supporting surveillance of pathogens’ [5–7].

Assumptions: This assumes developers implement required standards and metadata. Also assumes public health agencies can incorporate new signal streams effectively.

Risk of failure: Disagreements about implementation into existing surveillance pathways. State agencies may lack technical expertise to train workers to evaluate, interpret and act on rapid biosensor signals. This could lead to misinterpretation and/or delayed action

Governance Action 3: Ethical data access and sharing standards (with local and community engagement requirements)

Purpose: Many genomic and bio-engineering projects lack consistent standards for privacy, consent, equity, and local engagement. A proposed change could be the mandatory implementation of ethical standards for data access combined with mandatory local/community engagement, ensuring transparency, and equitable benefit-sharing.

Design: Develop standardised model data agreements which specify permissible uses, benefit-sharing obligations, and consent mechanisms. Furthermore, advisory boards and steering committees can be established to ensure engagement, feedback, and regular assessment of processes.

Assumptions: This assumes that communities where the technology is planned to be implemented will agree to engage meaningfully. It also assumes that cross-country coordination on ethical standards will be possible.

Risk of failure: Strict data provisions may slow down implementation, collection and action. There may be failure to engage communities as they may view the initiative to engage them as superficial.

(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.

Based on the inputs and ranking in the matrix above, I would prioritize the following:

(i) Regulation and creation of standards for early-stage bio-sensor development (ii) Ethical data access and sharing standards with local community engagement

Together both these actions would address both the technical and social foundations required for responsible deployment of biosensors. Standards would ensure that biosensors are developed safely, setting incentive structures to develop lab safety protocols and enforce biosecurity. Local community engagement, training, and capacity building will help build trust, protect rights, and enable effective use of surveillance data across settings.

References

1. Richard M. Jones, Kristin N. Adams, Hassan E. Eldesouky, and David R. Sherman “The evolving biology of mycobacterium tuberculosis drug resistance.” Frontiers in Cellular and Infection Microbiology 2022.

2. Sebastien Gagneux “Ecology and evolution of mycobacterium tuberculosis.” Nature Reviews Microbiology 2018.

3. Qingyun Liu, Jianhao Wei, Yawei Li, Mei Wang, Jun Su, et al. “Mycobacterium tuberculosis clinical isolates carry mutational signatures of host immune environments.” Science Advances 2020.

4. Á. Chiner-Oms, L. Sánchez-Busó, J. Corander, S. Gagneux, S. R. Harris, et al. “Genomic determinants of speciation and spread of the mycobacterium tuberculosis complex.” Science Advances 2019.

5. World Health Organization. Attributes and principles of genomic data-sharing platforms supporting surveillance of pathogens with epidemic and pandemic potential. World Health Organization; 2025.

6. Carter L, Yu MA, Sacks J, Barnadas C, Pereyaslov D, Cognat S, et al. Global genomic surveillance strategy for pathogens with pandemic and epidemic potential 2022–2032. Bulletin of the World Health Organization. 2022 Apr 1;100(04):239–9A.

7. Trump BD, Florin MV, Perkins E, et al. Biosecurity for Synthetic Biology and Emerging Biotechnologies: Critical Challenges for Governance. 2021 Sep 8. In: Trump BD, Florin MV, Perkins E, et al., editors. Emerging Threats of Synthetic Biology and Biotechnology: Addressing Security and Resilience Issues [Internet]. Dordrecht (DE): Springer; 2021. Chapter 1. Available from: https://www.ncbi.nlm.nih.gov/books/NBK584259/ doi: 10.1007/978-94-024-2086-9_

Assignment (Week 2 Lecture Prep)

Homework Questions from Professor Jacobson

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 polymerase is 1 error per 10⁶ nucelotides, where this can range from expected error frequency from 1 error per 10⁴ to approximately 10⁶ [1]. The human genome has 3 x 10⁹ base pairs, this is around 3 billion nucleotides. This is much larger (approx. 3000 times) than 10⁶-nucleotide error rate of polymerase. Biology deals with this through a process of proofreading; cells use polymerase proofreading and mismatch repair to reduce errors to just a few per genome per replication [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?

Average Human Protein: 1036 bp As 1 codon = 3 nucleotides

∴ Total amino acids = 1036/3 ~ 345

Given 3 nuclotide-codons and 1 codon codes for 1 amino acid, there are 3³⁴⁵ different ways to code for an average human protein.

Given 3³⁴⁵ DNA sequences code for the same protein, only some of it works due to codon preferences and bias, repetitive or unstable sequences, and mRNA folding [3].

References

1. Kunkel TA, Bebenek K. DNA replication fidelity. In: Brenner S, Miller JH, editors. DNA Replication and Human Disease. Bethesda (MD): National Center for Biotechnology Information (US); 2002. Available from: [https://www.ncbi.nlm.nih.gov/books/NBK9940/](https://www.ncbi.nlm.nih.gov/books/NBK9940/]

2. Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P. Molecular Biology of the Cell. 4th ed. New York: Garland Science; 2002. ISBN: 0-8153-3218-1, 0-8153-4072-9.

3. Lin J, Chen Y, Zhang Y, Lin H, Ouyang Z, et al. Deciphering the role of RNA structure in translation efficiency. BMC Bioinformatics. 2022;23:559

Homework Questions from Dr. LeProust

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

Oligonucleotide synthesis is the chemical process of making short fragments of DNA or RNA with a defined sequence, typically using step‑by‑step addition of nucleotide building blocks on a solid support [1]. For enzyme-free synthesis, the process involves sequentially adding nucleotide units to a growing chain, typically using solid- or liquid-phase synthesis [2]. The most common method is solid phase oligo phosphoramidite synthesis. As it is now automated and uses high quality short sequences, it is widely used in biotech companies around the world [3–4].

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

As length is increased, chemical synthesis becomes less efficient. As a result, there is a loss in product yield, greater rate of error accumulation (higher substitution or deletion rates), and an increased difficulty in purifying the final product due to the introduction of truncated and mis-incorporated oligos [5].

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

As oligosynthesis adds one nucleotide at a time, increasing length will lead to a greater accumulation of errors (substitutions/deletions). The truncated or defective sequences become increasingly difficult to purify [6]. Therefore, direct synthesis of a 2000bp gene is not practical despite surface-based methods and capture-based purification [7].

References

1. Beaucage SL, Caruthers MH. Deoxynucleoside phosphoramidites—A new class of key intermediates for deoxypolynucleotide synthesis. Tetrahedron Letters. 1981;22(20):1859–62. doi:10.1016/S0040-4039(01)90461-7.

2. Bachem. What is oligonucleotide synthesis & how does it work? [Internet]. Bubendorf: Bachem; 2024 Aug 26 [cited 2026 Feb 10]. Available from: https://www.bachem.com/articles/oligonucleotides/how-does-oligonucleotide-synthesis-work/

3. ScienceDirect. Oligonucleotide synthesis [Internet]. Amsterdam: Elsevier; 2024 [cited 2026 Feb 10]. Available from: https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/oligonucleotidesynthesis

4. ATDBio. Solid-phase oligonucleotide synthesis: The Phosphoramidite method [Internet]. Southampton: ATDBio; 2024 [cited 2026 Feb 10]. Available from: https://atdbio.com/nucleic-acids-book/Solid-phase-oligonucleotide-synthesis#The-Phosphoramidite-method

5. Kosuri S, Church GM. Large-scale de novo DNA synthesis: technologies and applications. Nat Methods. 2014;11:499–507. doi:10.1038/nmeth.2918.

6. Pichon M, Hollenstein M. Controlled enzymatic synthesis of oligonucleotides. Commun Chem. 2024;7:138. doi:10.1038/s42004-024-01216-0.

7. Yin Y, Arneson R, Yuan Y, Fang S. Long oligos: direct chemical synthesis of genes with up to 1728 nucleotides. Chem Sci. 2025;16:1966–73. doi:10.1039/D4SC06958G.

Homework Question from George Church

Choose ONE of the following three questions to answer; and please cite AI prompts or paper citations used, if any.

1. [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”?

Essential amino acids are defined as the amino acids that the animal body cannot synthesize, and therefore must obtain from diet. The essential amino acids in animals are are: isoleucine, leucine, lysine, threonine, tryptophan, methionine, histidine, valine, and phenylalanine. In addition, cysteine and tyrosine are often described as conditionally essential because they cannot be synthesized de novo in animals and are instead produced from methionine and phenylalanine, respectively [1].

Given lysine is one of essential amino acids that is universal for all animals, the “Lysine Contingency” is not an exclusive real control mechanism. Even if it hypothetically existed and could be removed, animals could easily source it from food, either meats, beans, or grains.

References

1. Hou Y, Wu G. Nutritionally essential amino acids. Adv Nutr. 2018;9(6):849–851. doi:10.1093/advances/nmy054