Week 1 HW: Final Project Ideas

HTBAA Homework #1 Committed listener | Yunuen Ceron

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

The use of dyes worldwide is fairly widespread; however, in many cases it can be counterproductive and harmful to the environment. Therefore, it is important to seek alternatives that improve dye quality without causing as much environmental impact, while also helping to improve quality of life and increase profitability and competitiveness in the market. [Tiburcio Bernabe, M. M., 2025] My goal is to develop a biological production system for the IndC protein, a protein associated with the production of blue pigments, using engineered E. coli as a microbial cell factory. I am interested in this system because it could provide a biological alternative for producing pigments through microbial biotechnology rather than relying exclusively on conventional chemical synthesis. More broadly, I am interested in designing cells as programmable biological factories that can convert genetic information into useful products at an industrially relevant scale.

(2) What living system would you use?

This project would involve designing an expression vector containing the indC gene, encoding an indigoidine synthetase, and introducing it into engineered Escherichia coli cells for heterologous production of indigoidine. Because indC is a non-ribosomal peptide synthetase, its activity may depend on appropriate phosphopantetheinyl transferase or accessory protein for enzymatic activation. I selected the E.Coli as host because it is well-established for recombinant protein production due to its rapid growth, genetic tractability, and extensively characterized expression systems (Rosano & Ceccarelli, 2014).

Indigoidine is a natural product with antioxidant and antimicrobial activities that can be cultivated in a suitable fermentation medium containing carbon and nitrogen sources. Some papers mentioned some examples of feeding the cultivation, such as L-glutamine as the principal nitrogen source. [Xu, F., Gage, D., & Zhan, J, 2015).] Also, L-glutamine could be supplied as an additional nitrogen-containing precursor or supplemented in the culture medium, depending on the metabolic requirements of the selected E. coli strain and the expression system. (Yu, D., Xu, F., Valiente, J., Wang, S., & Zhan, J. (2013)

During cultivation, the cells would express IndC, and an appropriate phosphopantetheinyl transferase would activate the enzyme if required. The active IndC enzyme would then catalyze the formation of indigoidine from the appropriate amino acid substrates available within the cell. As a result, indigoidine would accumulate in the engineered cells during fermentation and could subsequently be recovered from the biomass. The resulting biological system could serve as a model for investigating how cellular design, genetic engineering, and bioprocess optimization can be integrated to develop scalable microbial biomanufacturing platforms.

(3) How would you use automation?

I would use a cloud laboratory to automate the preparation, cultivation, sampling, and measurement of multiple E. coli cultures producing indigoidine. In this setup, I would computationally design the experimental matrix (testing different induction times, carbon sources, or genetic variants), while the cloud lab’s robotic infrastructure executes the physical protocols remotely.

This approach decouples experimental design from physical execution. It allows for the rapid, standardized testing of vast design spaces without manual intervention, eliminating human pipetting errors, ensuring strict reproducibility, and scaling up the optimization phase much faster than a standard benchtop workflow would allow. To follow this approach I would like to use the next table where I define in detail which stage of automatization gets involved.

(4) How much would you need to make, and how long would it take?

To perform the calculations and estimates in this section, I used an artificial intelligence tool to develop a logical engineering approach and project possible production scenarios. I am committed to conducting a thorough review of the technical literature and the fundamentals of bioprocessing to rigorously validate the reliability and accuracy of these figures in future stages of the project.

A pilot batch with a working volume of 100 L, intended to produce enough pigment for a small collection of textile dyes or to formulate cosmetic prototypes. The result depends primarily on the product dosage and the fermentation titer assumed in the theoretical experiment.

1. Key Assumptions

I will use the following assumptions:

• Product: dried, recovered indigoidine.
• Scale: a small textile collection of 1,000 pieces.
• Pigment consumption per piece: 5 g.
• Losses during recovery and purification: 20%.
• Design fermentation titer: 5 g/L.
• Bioreactor working volume: 100 L.
• Culture time: approximately 28 h, based on a reported process for E. coli.
• Additional time for harvesting, recovery, drying, and cleaning: 12–24 h.

The titer of 5 g/L is a reasonable intermediate assumption. The literature reports 3.93 g/L of indigoidine in E. coli through co-expression of Sc-IndC and Sc-IndB; another study achieved 8.81 g/L through supplementation with L-glutamine. For a preliminary calculation, it is advisable to use 5 g/L rather than the published maximum, because larger scales typically involve transfer losses and differences compared to flasks.

Profit Required Before Considering Losses

Let’s assume:

1,000 pieces x 5g/indigoidine = 5000 g = 5kg

Therefore, 5 kg of usable indigoidine is required in the final product.

2. Adjustment for processing losses

Assuming an overall recovery rate of 80%:

Indigoidine to produce = 5 kg/0.80 = 6.25 kg

Therefore, the fermentation batch should yield approximately 6.25 kg of crude or recoverable indigoidine to end up with 5 kg of usable product.

The 80% efficiency includes hypothetical losses during:

• Cell separation or supernatant removal.
• Washing.
• Extraction or concentration.
• Drying.
• Transfer between equipment.
• Filtration and formulation.

This percentage is not a universal figure; it is a rough estimate that would later need to be replaced by experimental data. Or in this case as a committed listener, the data should be replaced by data drawn from the scientific literature that I could reference in case that is requested.

3. Required volume

   TotalVolume = required mass / Fermentation titer = 6.250g / 5g/L = 1,250 L per Fermentation broth

Approximately 1,250 L of fermentation broth would be required to produce 6.25 kg before accounting for recovery losses.

4. BIorreactor strategy

If a bioreactor with a working volume of 100 L is used:

number of batch = 1,250L / 100 L/batch = 12.5 batches

Since it is not possible to produce a half-batch without explicitly planning for it, the number is rounded to 13 batches of 100 L. The theoretical yield per batch would be:

100 L x 5g/L = 500 g/batch

After an 80% recovery rate:

500g x 0.80 = 400g of usable product per batch

To obtain 5 kg of usable product:

5,000 g400 g/ batch=12.5 batches

Therefore, 13 batches of 100 L are needed.

5. Time Estimate

For the fermentation time, I assume that iIf each batch takes approximately 28 h to ferment: 13 batch x 28 h/batch = 364 h 364h / 24 = 15.2 days

If the batches are processed one after another in the same bioreactor, fermentation would take approximately 15 days.

Of course, after the initial time its necessary to add the pros-processing time. Where would be the next stages:

• Harvesting and separation: 4-8 h
• Recovery, washing, and concentration: 8-16 hours.
• Drying and conditioning: 8–24 hours.
• Cleaning and preparation of the bioreactor between batches: 8–12 hours per cycle.

So, it’s necessary to add 12 additional hours per batch for cleaning and preparation:

13 batches x (28 +12) h = 520h= 520h / 24 = 21.7 days

Then add the final product processing, for example, 1–2 days:

Approximate total time ≈ 23–24 days

Therefore, a reasonable estimate would be:

13 batches of 100 L, approximately 1,250 L of total broth, and 3–4 calendar weeks if a single 100-L bioreactor is used.

After that, if we take into consideration the automation. In an automated platform or cloud lab, the main objective would be to quickly compare many conditions, as example:

• Small volumes, such as milliliters or a few liters.
• Many variants of indC, promoters, and culture conditions.
• Automated measurement of growth and pigmentation.
• Optimization of titer and productivity.
• Generation of data to select the best construct.

Also, a cloud lab would not typically be used to produce several kilograms of product. Its function would be to quickly identify the conditions that would later be transferred to a larger bioreactor. Cloud labs can improve experimental reproducibility and scalability by running remote, standardized protocols. In an industrial plant, stirred-tank bioreactors would be used, possibly ranging from hundreds to thousands of liters. There, the following factors would need to be considered:

• Oxygen transfer and mixing.
• Control of pH, temperature, and dissolved oxygen.
• Carbon and nitrogen feeding.
• Foaming.
• Sterility and contamination control.
• Pigment recovery and drying.
• Cleaning and sterilization time between batches.

For this reason, a titer measured in a flask does not necessarily hold true when scaled up. In an initial calculation, it is more reasonable to use a design titer of 5 g/L, even though the literature reports higher values under optimized conditions.

(5) What don't you know yet?

After evaluating the proposed automated workflow for indigoidine production in E. coli, two major technical and economic bottlenecks emerge:

1. Uncertainty in Critical In-Line Measurement Parameters

I still don’t know how to determine which specific physiological or metabolic indicators should be measured to truly optimize production. While final pigment yield and optical density are standard parameters, secondary markers of metabolic stress, the availability of precursors (such as intracellular L-glutamine levels), or plasmid stability over the course of prolonged automated cycles are less well defined.

2. Economic and Operational Feasibility at Industrial Scale

I would also like to know if adapting a high-throughput automated protocol to large-scale biomanufacturing could be feasible. This is mainly based on the idea that the high costs associated with remote robotic execution, specialized consumables, and cloud infrastructure can be justified by the final value of the pigment, but I’m not sure if it would be a good option considering that the goal is to be cost-effective and less polluting than dyes.

Furthermore, I am still trying to determine whether a technical-economic analysis (TEA) is needed to assess whether the precision and time savings provided by automation offset the large-scale operating expenses (OPEX), or whether the process should ultimately transition to standard stirred-tank bioreactors with simpler, localized control loops in order to remain economically viable for large-scale pigment production.

References:

Rosano, G. L., & Ceccarelli, E. A. (2014). Recombinant protein expression in Escherichia coli: advances and challenges. Frontiers in microbiology, 5, 172.

Tiburcio Bernabe, M. M. (2025). Proceso de tintura y acabados de telas para reducir el impacto ambiental.

Xu, F., Gage, D., & Zhan, J. (2015). Efficient production of indigoidine in Escherichia coli. Journal of Industrial Microbiology and Biotechnology, 42(8), 1149-1155.

Yu, D., Xu, F., Valiente, J., Wang, S., & Zhan, J. (2013). An indigoidine biosynthetic gene cluster from Streptomyces chromofuscus ATCC 49982 contains an unusual IndB homologue. Journal of Industrial Microbiology and Biotechnology, 40(1), 159-168.

Final Project