Week 1 HW: Final Project Ideas

HTBAA Homework #1 Due: Tuesday September 22, by 2:30p ET

For your first homework, propose and describe one idea for something you would like to biomanufacture. This could be medicine, material, food, energy, an art, design, or architectural artifact, or beyond. Draw from your own background and interests for inspiration.

The goal of this assignment is not to develop a complete or technically optimized manufacturing process. Instead, we want you to begin connecting four fundamental questions: What do you want to make? → What living system could make it? → How could automation help you make it? → What would it take to make it at a meaningful scale? Include in detail:

(1) What you would like to biomanufacture and why?

Carminic Acid. Three reasons: Growing demand for high-performance natural red colorants. Food, beverage, and cosmetics companies are increasingly interested in natural colorants, while carmine remains commercially valuable because it has unusually good heat, light, and pH stability compared with many plant-derived red pigments. It is already used across dairy, confectionery, beverages, cosmetics, and pharmaceuticals. The current supply chain is concentrated and difficult to scale. Conventional carminic acid depends on farming cochineal insects, with much of the raw-material supply concentrated in Peru and other parts of Latin America. That geographic and biological dependence can create supply, price, and consistency risks. Fermentation could potentially create a more predictable supply chain using standardized bioreactors located closer to customers. An insect-free version could open additional customer segments. Traditional carmine’s insect origin prevents its use in many vegan products and creates religious, ethical, labeling, and allergen-related complications. In the U.S., for example, foods and cosmetics containing conventional carmine must specifically disclose it on the label. If fermentation-derived carminic acid could be produced with equivalent performance and gain the appropriate regulatory status, it could potentially appeal to manufacturers seeking an insect-free red colorant I also want to produce carminic acid because I want to help bring bold red lipstick back into fashion. Carmine has a long history as a vivid red pigment in cosmetics, and a biomanufactured version could make that color more scalable, consistent, and potentially more appealing to modern consumers. (2) What living system would you use? What living system would I use? I would use engineered Saccharomyces cerevisiae, commonly known as baker’s yeast, as the living-system manufacturing chassis for carminic acid. I chose S. cerevisiae because my intended end uses include food and cosmetics, so I want a production organism that already has a strong history of use in food manufacturing. Yeast is widely used in baking, brewing, and other food fermentations, making it a more appropriate starting point for a food-grade production process than a conventional laboratory strain of E. coli. The system would work by genetically engineering the yeast with the enzymes required to construct carminic acid. Glucose would be supplied as the main carbon source, along with food-grade nutrients, water, minerals, and oxygen. The yeast would metabolize the glucose and generate metabolic precursors such as acetyl-CoA and malonyl-CoA. Engineered enzymes would then convert these precursors through a series of polyketide synthesis, cyclization, hydroxylation, and glycosylation reactions to form carminic acid. A simplified process would be: Food-grade glucose + nutrients + oxygen → engineered baker’s yeast → metabolic precursors → carminic acid → separation and purification → food/cosmetic-grade pigment This is also experimentally plausible. Researchers have already reconstructed the carminic-acid pathway in S. cerevisiae and reported approximately 7.58 mg/L in a 5 L bioreactor. For manufacturing, I would use a food-grade fermentation process with sanitary stainless-steel bioreactors, food-compatible nutrients, validated cleaning procedures, and downstream purification designed to remove yeast cells, residual media components, and process impurities. An important assumption is that using a food-associated organism does not automatically make the final genetically engineered product regulatory-approved for food use. The specific engineered strain, manufacturing process, purity specification, and intended use would still require appropriate safety and regulatory review. However, S. cerevisiae gives me a much stronger food-manufacturing starting point than a chassis chosen only for laboratory convenience. (3) How would you use automation? How would I use automation? I would use automation at two different stages of the project: first to optimize the engineered yeast and fermentation process at laboratory scale, and later to control reproducible batch production at manufacturing scale. Because my intended product could be used in food and cosmetics, I would use engineered Saccharomyces cerevisiae as the production organism rather than E. coli. The main technical challenge is not simply proving that yeast can make carminic acid, but increasing the amount of carminic acid produced per liter while maintaining a process compatible with food-grade manufacturing. Closed-loop laboratory automation During pathway development, I would build a closed-loop Design-Build-Test-Learn system that combines an Opentrons Flex liquid-handling robot, automated incubators, analytical instruments, and optimization software. The workflow would operate as follows:

1. Design experiments Software would select combinations of biological and fermentation conditions to test. These could include: promoter strengths controlling pathway enzymes; enzyme variants; gene copy number; expression levels of different pathway modules; glucose concentration; food-compatible media composition; fermentation temperature; oxygen availability; feeding strategy; cultivation time; strategies for increasing acetyl-CoA and malonyl-CoA availability. Rather than testing one condition at a time, I could test dozens or hundreds of combinations in parallel.
2. Prepare and inoculate cultures An Opentrons Flex liquid-handling robot would prepare multiwell plates containing different yeast strains and growth conditions. The robot could automate: media preparation; nutrient dispensing; serial dilutions; inoculation; addition of pathway inducers when required; addition of glucose and other feed components; transfer of cultures between plates; preparation of technical replicates. This would make experiments more reproducible and allow many more conditions to be tested than would be practical manually.
3. Automated cultivation The culture plates would then be transferred into an automated incubator or plate hotel. The incubator would maintain controlled conditions such as: temperature; shaking speed; humidity; cultivation time; oxygen availability. A robotic system could automatically move plates between the incubator, liquid handler, plate reader, and sampling station. This would allow me to grow many yeast cultures simultaneously while tightly controlling the conditions experienced by each culture.
4. Automated feeding and sampling During cultivation, the liquid handler could automatically add glucose or nutrients at programmed times. This is especially useful because industrial carminic acid production would likely use a fed-batch process rather than supplying all of the glucose at the beginning. The robot could also remove samples at predefined time points, allowing me to determine how carminic acid production changes throughout fermentation.
5. Measure growth and carminic acid production After sampling, automated analytical tools would evaluate each culture. A plate reader could measure optical density to estimate yeast growth. More importantly, samples would be analyzed using HPLC or LC-MS to quantify: carminic acid concentration; pathway intermediates; unwanted by-products. The main performance metric would be carminic acid titer, measured in mg/L. I would also calculate yield and productivity so that the system does not simply select cultures that grow quickly but produce very little pigment.
6. Data integration and closed-loop decision making All experimental results would be stored in a laboratory information management system. The database would connect each measurement with the exact strain, media formulation, temperature, feed strategy, cultivation time, and other experimental conditions used. Optimization software could then analyze the results and determine which combinations produced the highest carminic acid concentrations. For example, the system might learn that a particular combination of promoter strength, glucose-feed rate, and temperature produces significantly more carminic acid. Instead of requiring me to manually design the next experiment, the algorithm could automatically generate the next group of conditions to test. The full closed loop would therefore be: Design conditions → Liquid handler prepares cultures → Automated incubator grows yeast → Robot feeds and samples cultures → Plate reader and HPLC/LC-MS measure performance → Software analyzes the results → Algorithm selects improved conditions → Next experimental batch begins This creates a self-improving experimental system in which each batch of experiments provides information for designing the next one. The goal would be to progressively identify combinations that increase carminic acid titer, yield, and productivity. Automation at manufacturing scale The automation used for laboratory optimization would be different from the automation used for actual production. I would not use hundreds of microplates or an Opentrons robot to manufacture commercial quantities of carminic acid. Once an optimized food-grade yeast strain and fermentation process had been identified, I would transfer the process into sanitary stirred-tank bioreactors. At manufacturing scale, the production system would automatically monitor: temperature; pH; dissolved oxygen; agitation rate; aeration; glucose concentration; nutrient concentration; biomass; fermentation time. Sensors would continuously send data to a process-control system. The controller could automatically adjust variables such as: glucose-feed rate; nutrient-feed rate; agitation speed; airflow; oxygen supplementation; heating and cooling; acid or base addition. The objective would be to keep the yeast within the fermentation conditions that the laboratory optimization system had identified as producing the most carminic acid. For example, if dissolved oxygen dropped below the desired level, the system could automatically increase agitation or airflow. If glucose became depleted, the controller could increase the feed rate. Automated batch production The full manufacturing system could therefore operate as: Food-grade media preparation Automated seed culture preparation Large-scale yeast fermentation Real-time sensor monitoring Automatic adjustment of fermentation conditions Automated sampling and quality testing Cell separation Carminic acid extraction and purification Final food- or cosmetic-grade product Each completed batch would also generate process data that could be compared with previous batches. If certain conditions consistently produced higher yields, that information could feed back into both laboratory optimization and future manufacturing runs. Therefore, the overall system contains two related automation loops: Prototype loop: Automated liquid handling + incubation + sampling + analytical measurement + algorithmic optimization. Manufacturing loop: Automated bioreactor sensing + feedback control + sampling + purification + quality control. The important advantage of this strategy is that automation does more than reduce manual labor. It allows me to search a very large experimental space at laboratory scale and then reproduce the best-performing biological conditions consistently during food-grade batch manufacturing

Identify at least one automation technology that you would combine with your living system. This could include a large-scale Cloud Lab, a desktop liquid-handling robot such as an Opentrons, a programmable microfluidics platform such as Nuclera’s, or another automation technology. Describe what part of the biomanufacturing workflow you would automate and why. For example, you might automate liquid handling, culturing, assembly, screening, sampling, measurement, optimization, or another step. (4) How much would you need to make, and how long would it take? 4. How much would I need to make, and how long would it take? (a) How much? For my project, I will define a meaningful initial manufacturing scale as 1 kilogram of purified carminic acid. 1 kg = 1,000 g = 1,000,000 mg The reported carminic acid titer in engineered Saccharomyces cerevisiae is approximately 7.58 mg/L. If recovery were 100%: Required fermentation volume = Target amount / Titer Required fermentation volume = 1,000,000 mg / 7.58 mg/L Required fermentation volume = approximately 131,900 L So, with perfect recovery, I would need approximately 132,000 L of fermentation broth. However, I will assume only 70% of the carminic acid is recovered during downstream purification. Required biological production = Final target / Recovery fraction Required biological production = 1,000 g / 0.70 Required biological production = 1,429 g 1,429 g = 1,429,000 mg Required fermentation volume = 1,429,000 mg / 7.58 mg/L Required fermentation volume = approximately 188,500 L Therefore, at the currently demonstrated yeast titer, I would need approximately 188,500 L of fermentation broth to obtain 1 kg of purified carminic acid. Improved production target I would use closed-loop automation to try to increase the carminic acid titer to 100 mg/L. This is an engineering target, not a currently demonstrated value. At 100 mg/L: Required fermentation volume = 1,429,000 mg / 100 mg/L Required fermentation volume = 14,290 L Therefore, increasing the titer from 7.58 mg/L to 100 mg/L would reduce the required fermentation volume from approximately 188,500 L to 14,290 L. Fold reduction = 188,500 L / 14,290 L Fold reduction = approximately 13.2 So the improved strain would require about 13 times less fermentation volume. (b) How long? For my manufacturing estimate, I will assume: Fermentation time per batch = 5 days Bioreactor size = 10,000 L Number of bioreactors = 10 Total fermentation capacity per cycle = 10 bioreactors x 10,000 L Total fermentation capacity per cycle = 100,000 L Current demonstrated yeast titer Required fermentation volume = 188,500 L Number of cycles = 188,500 L / 100,000 L per cycle Number of cycles = 1.885 cycles Because I cannot run part of a complete manufacturing cycle, I would round this up to 2 cycles. Total fermentation time = 2 cycles x 5 days per cycle Total fermentation time = approximately 10 days Therefore, at the current demonstrated yeast titer, it would take approximately 10 days of fermentation using ten 10,000-L bioreactors operating in parallel. Improved titer of 100 mg/L Required fermentation volume = 14,290 L Two 10,000-L reactors provide: 2 x 10,000 L = 20,000 L Since 20,000 L is greater than the required 14,290 L, the full amount could be produced in one fermentation cycle. Total fermentation time = 1 cycle x 5 days Total fermentation time = approximately 5 days Therefore: Current titer: 7.58 mg/L -> approximately 188,500 L -> approximately 10 fermentation days Improved target: 100 mg/L -> approximately 14,290 L -> approximately 5 fermentation days

(a) How much? Define a scale relevant to your project and calculate approximately how much of your target product you would need to manufacture. For example, if you are producing a medicine, you might estimate the amount required to treat 1,000 people, everyone in Massachusetts, or everyone in the world. If you are producing a building material, you might calculate the amount required for one structure or an entire neighborhood. If you are producing an art or design artifact, define an appropriate scale yourself. Clearly show your calculation, including units, assumptions, and sources for important numbers. (b) How long? Using your proposed living system and automation technology, estimate how long it would take to manufacture that amount. Consider quantities such as production rate, yield, volume, number of parallel experiments or reactors, automation throughput, or whatever parameters are most appropriate for your system. You may discover that the automation technology you would use to prototype your system is very different from what you would use to manufacture at scale. If so, describe both. Again, we are looking for a reasonable engineering estimate. Show your assumptions and calculations. 5. What don’t you know yet?

1. Can I increase carminic acid titer enough for commercial production? The biggest unknown is whether engineered Saccharomyces cerevisiae can be improved from the currently reported low titers to something much closer to an industrially useful level. I would need to determine which parts of the pathway are limiting production and whether closed-loop strain optimization can substantially increase titer, yield, and productivity.
2. Is there an alternative biosynthetic pathway that would be easier to scale? I do not yet know whether the currently demonstrated carminic acid pathway is the best route for industrial production. There may be alternative enzyme combinations or metabolic routes that use more abundant precursors, require fewer steps, generate fewer unwanted intermediates, or place less metabolic burden on the yeast. I could use pathway-design and enzyme-selection tools such as KEGG and Selenzyme to search for alternative reaction routes and candidate enzymes. I would then compare these pathways based on factors such as number of enzymatic steps, predicted enzyme activity, precursor availability in Saccharomyces cerevisiae, cofactor requirements, by-product formation, and likely scalability.
3. Which pathway step is the main metabolic bottleneck? Carminic acid synthesis requires several enzymatic steps, and I do not yet know which one most strongly limits flux through the pathway. Possible bottlenecks could include precursor availability, poor enzyme activity, imbalanced expression, or accumulation of pathway intermediates. I would need to measure intermediate concentrations and systematically vary enzyme expression to identify the limiting step.
4. How efficiently can I extract and purify the carminic acid? My calculations currently assume 70% downstream recovery, but this is only an assumption. I would need to determine how much carminic acid remains inside the cells versus in the fermentation broth, how easily it can be separated, and what purification process can produce a high-purity food- or cosmetic-grade pigment without excessive cost.
5. Will the process remain stable and consistent when scaled up? A strain that performs well in a 96-well plate may behave differently in a 10,000-L bioreactor. Large reactors can develop gradients in oxygen, nutrients, temperature, and mixing. I would need to determine whether the engineered yeast remains genetically stable and whether carminic acid production stays consistent as the process moves from laboratory scale to pilot scale and then to manufacturing scale. AI

Feel free to use AI and our learning tools to be shared in recitation to assist you, but remember that you will present in class, so make sure you have a solid understanding of your responses.

AI client: OpenAI ChatGPT, GPT-5.6 Sol. Prompts used: I asked ChatGPT to help me develop a biomanufacturing proposal for carminic acid, including choosing a food-grade production organism, designing a closed-loop automation workflow, estimating production scale and time, identifying technical unknowns, and exploring alternative biosynthetic pathways using tools such as KEGG and Selenzyme.

Readings:

Asin-Garcia, E., Fawcett, J. D., Batianis, C., & Martins dos Santos, V. A. P. (2025). A snapshot of biomanufacturing and the need for enabling research infrastructure. Trends in Biotechnology, 43(5), 1000–1014. https://doi.org/10.1016/j.tibtech.2024.10.014 Averesch, N. J. H., Berliner, A. J., Nangle, S. N., Zezulka, S., Vengerova, G. L., Ho, D., Casale, C. A., Lehner, B. A. E., Snyder, J. E., Clark, K. B., Dartnell, L. R., Criddle, C. S., & Arkin, A. P. (2023). Microbial biomanufacturing for space-exploration—what to take and when to make. Nature Communications, 14, 2311. https://doi.org/10.1038/s41467-023-37910-1 Karim, A. S. & Jewett, M. C. (2021). “Toward sustainable, cell-free biomanufacturing.” Current Opinion in Biotechnology 69, 136–144. DOI: 10.1016/j.copbio.2020.12.012. Petzold, C. J., & Mukhopadhyay, A. (2025). From bench to biofactory: High-throughput technologies and automated workflows to accelerate biomanufacturing. Current Opinion in Biotechnology, 94, 103320. https://doi.org/10.1016/j.copbio.2025.103320 Yap, W. S., Choudhury, D., & Suntornnond, R. (2023). Towards biomanufacturing of cultured meat. Trends in Biotechnology, 41(3), 292–294. https://doi.org/10.1016/j.tibtech.2023.01.014 Ye, J.-W., Lin, Y.-N., Yi, X.-Q., Yu, Z.-X., Liu, X., & Chen, G.-Q. (2023). Synthetic biology of extremophiles: A new wave of biomanufacturing. Trends in Biotechnology, 41(3), 342–357. https://doi.org/10.1016/j.tibtech.2022.11.010 Zhang, Y.-H. P., Sun, J., & Ma, Y. (2017). Biomanufacturing: History and perspective. Journal of Industrial Microbiology & Biotechnology, 44, 773–784. https://doi.org/10.1007/s10295-016-1863-2 Readings Folder: https://drive.google.com/drive/folders/1wX9w_MaJ3TFwHMyPp51b2vJ4mxkUNibf?usp=sharing

Final Project