Week 1 HW: Final Project Ideas

Extremophile-Inspired Seed Pods (XTremePods)

Dynamic Metabolic Tuning of PHA & Ectoine in Halomonas bluephagenesis for Phytoremediation of Degraded Mining Soils

1️⃣ What I Would Like to Biomanufacture and Why?

Target Product

“Extremophile Seed Pods”, biodegradable seed capsules made from a biocomposite of:

The Problem & Motivation

Climate change and unregulated mining, particularly in the Peruvian Andes, leave behind vast expanses of acidic, saline, metal-contaminated tailings where traditional seeds simply cannot germinate.

My current bachelor’s thesis at UNMSM evaluates the osmotic tolerance and ectoine production of the extreme acidophile Leptospirillum ferriphilum under chloride and sulfate stress. This homework is my opportunity to translate that fundamental discovery into a tangible and scalable biomanufacturing solution.

⚠️ Critical Literature Gap

While halophilic bacteria exhibit Plant Growth Promoting (PGP) traits under salt stress (Srivastava, 2022), no direct experimental evidence currently exists on the formal application of formulated ectoine or PHA biopolymers as seed coatings for the phytoremediation of mine tailings.

2️⃣ What Living System Would I Use?

Proposed Chassis: Halomonas bluephagenesis

I propose using Halomonas bluephagenesis, a halophilic microbial manufacturing chassis renowned for Next-Generation Industrial Biotechnology (NGIB). It is capable of open, non-sterile fermentation at high salinity, which drastically reduces CAPEX and OPEX.

The Metabolic Challenge & Biological Process

H. bluephagenesis naturally co-produces both PHA and ectoine, but these pathways compete for central carbon. Ectoine biosynthesis diverts oxaloacetate away from the TCA cycle, suppressing oxidative phosphorylation to prioritize precursor availability over ATP generation (Enuh, 2024; Shu, 2023).

graph TD
    A["Carbon Source<br/>(Crude Glycerol)"] --> B["Entner-Doudoroff Pathway"]
    B --> C["Oxaloacetate"]
    C --> D["Aspartate Semialdehyde"]
    D --> E["ectABC Operon<br/>→ Ectoine"]
    D --> F["Amino Acid Synthesis"]
    C --> G["TCA Cycle"]
    G --> H["ATP Generation"]
    G --> I["phaCAB Operon<br/>→ PHA"]

    style E stroke:#228B22,stroke-width:2px
    style I stroke:#4169E1,stroke-width:2px
    style H stroke:#DAA520,stroke-width:2px
    style A stroke:#8B4513,stroke-width:2px

Rather than static overexpression, I propose exploring a a toggle switch circuit to balance co-production:

• Stage 1 (Growth & PHA): Low osmotic stress favors biomass accumulation and native PHB synthesis.
• Stage 2 (Ectoine Burst): A targeted osmotic shock (like a specific Cl⁻/SO₄²⁻ pulse, inspired by my thesis) flips the circuit, derepressing ectABC for hyper-accumulation.

What goes in: Crude glycerol (agricultural waste) + NaCl/metal sulfates. What comes out: High-density microbial biomass heavily loaded with intracellular PHA granules and ectoine.

3️⃣ How Would I Use Automation?

To connect the living system to the target product, I propose using automation in three distinct phases of the biomanufacturing workflow: Strain Optimization, Product Separation, and Artifact Creation.

1. Upstream Automation: Desktop Liquid-Handling Robot

Technology: Opentrons OT-2 integrated with open-source platforms. Workflow Automated: The Design-Build-Test-Learn (DBTL) cycle for genetic circuit assembly.

Balancing two competing metabolic pathways requires screening hundreds of genetic circuit variants (Promoter × RBS × Terminator). The Opentrons will automate the Golden Gate assembly of the toggle switch library and inoculate 96-well plates with precise Cl⁻/SO₄²⁻ gradients. This accelerates the primary DBTL cycle from months to weeks (Carbonell, 2018).

2. Downstream Automation: Automated Microbial Milking & Separation

Technology: Automated continuous-culture system with robotic valves and centrifugation modules. Workflow Automated: Non-lethal Product Separation.

Extracting intracellular products is traditionally labor-intensive, but the physical state of the molecules allows for an elegant automated separation:

• Ectoine (Dissolved solute): Lives dissolved in the cell’s cytoplasm.
• PHB (Solid polymer): Lives as insoluble, solid intracellular granules.

The Automated Milking Process:

1. Automated Osmotic Shock: Robotic valves rapidly cycle the bioreactor between high and low salinity. The cell membrane temporarily becomes permeable. The dissolved ectoine “sweats” out into the liquid, while the solid PHB granules remain trapped inside the cell.
2. Automated Centrifugation: The system automatically separates the streams where the liquid supernatant (rich in ectoine) is routed to crystallization, while the cell pellet (rich in PHB) is routed to solvent extraction.

3. Formulation Automation: From Biopolymers to “Seed Pods”

Technology: Automated liquid mixing and precision casting modules. Workflow Automated: Formulation and Artifact Molding.

The raw PHB extracted from the cells comes out as solid pellets after solvent evaporation. To create the final artifact:

1. Automated Blending: A robotic mixing module dissolves the purified PHB pellets and the crystallized ectoine powder together in a solvent, creating a homogeneous biocomposite resin.
2. Precision Casting: The liquid resin is automatically dispensed into 3D-printed molds shaped like seed pods. A physical plant seed is placed in the center of each mold before the resin solidifies, encapsulating the seed in the protective, moisture-retaining PHA-Ectoine matrix.

4️⃣ How Much Would I Need to Make, and How Long Would It Take?

Meaningful Scale Definition

As a reasonable engineering estimate for a pilot project, I define the scale as the reforestation of 1 hectare of degraded, acidic mining tailings in the Peruvian Andes.

(a) How Much?

graph TD
    A["10,000 m²<br/>Mining Tailings"] --> B["10,000 Seed Pods<br/>1 pod / m²"]
    B --> C["20 kg Biocomposite<br/>2 g / pod"]
    C --> D["2 kg Pure Ectoine<br/>10% of matrix"]
    D --> E["100 L Fermentation<br/>at 20 g/L titer"]

    style A stroke:#8B4513,stroke-width:2px,color:#fff
    style B stroke:#228B22,stroke-width:2px
    style C stroke:#8B4513,stroke-width:2px
    style D stroke:#DAA520,stroke-width:2px
    style E stroke:#4169E1,stroke-width:2px

(b) How Long?

Phase 1 — Prototyping (Opentrons OT-2)

Phase 2 — Manufacturing at Scale (100L Bioreactor + Automated DSP)

Total Time to Product: Around 4 days of manufacturing to yield the 100L batch (2 kg ectoine + 18 kg PHA) required for the 1-hectare pilot, following the 11-week automated strain optimization.

5️⃣ What Don’t I Know Yet?

AI Citation

AI Client: Qwen (developed by Alibaba Cloud)

Prompt Used: “Format the final proposal as a visually appealing Markdown page with Mermaid diagrams, emojis, and tables”

References

• Bryant, J. A., Jr, Kellinger, M., Longmire, C., Miller, R., & Wright, R. C. (2023). AssemblyTron: flexible automation of DNA assembly with Opentrons OT-2 lab robots. Synthetic Biology (Oxford, England), 8(1), ysac032. https://doi.org/10.1093/synbio/ysac032
• Carbonell, P., Jervis, A. J., Robinson, C. J., Yan, C., Dunstan, M., Swainston, N., Vinaixa, M., Hollywood, K. A., Currin, A., Rattray, N. J. W., Taylor, S., Spiess, R., Sung, R., Williams, A. R., Fellows, D., Stanford, N. J., Mulherin, P., Le Feuvre, R., Barran, P., … Scrutton, N. S. (2018). An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals. Communications Biology, 1(1), 66. https://doi.org/10.1038/s42003-018-0076-9
• Enuh, B. M., Aytar Çelik, P., & Angione, C. (2024). Genome-scale metabolic modeling of Halomonas elongata 153B explains polyhydroxyalkanoate and ectoine biosynthesis in hypersaline environments. Biotechnology Journal, 19(10), e202400267. https://doi.org/10.1002/biot.202400267
• Hong, M., Zhao, Y., Huang, W., Zhang, L., Wu, F., Ye, J., & Chen, G.-Q. (2020). Rational flux-tuning of Halomonas bluephagenesis for co-production of bioplastic PHB and ectoine. Nature Communications, 11(1), 3313. https://doi.org/10.1038/s41467-020-17223-3
• Hu, Q., Sun, S., Zhang, Z., Liu, W., Yi, X., He, H., Scrutton, N. S., & Chen, G.-Q. (2024). Ectoine hyperproduction by engineered Halomonas bluephagenesis. Metabolic Engineering, 82, 238–249. https://doi.org/10.1016/j.ymben.2024.02.010
• Shu, Z., Zhang, X., Wang, R., Xing, J., Li, Y., Zhu, D., & Shen, G. (2023). Metabolic engineering of Halomonas campaniensis strain XH26 to remove competing pathways to enhance ectoine production. Scientific Reports, 13(1), 9732. https://doi.org/10.1038/s41598-023-36975-8
• Srivastava, A. K., Srivastava, R., Sharma, A., Bharati, A. P., Yadav, J., Singh, A. K., Tiwari, P. K., Srivatava, A. K., Chakdar, H., Kashyap, P. L., & Saxena, A. K. (2022). Transcriptome analysis to understand salt stress regulation mechanism of Chromohalobacter salexigens ANJ207. Frontiers in Microbiology, 13, 909276. https://doi.org/10.3389/fmicb.2022.909276

Week 2 HW: DNA Design

Part 0: Set up in Kernel Repository: Cesar_Alcantara-SeedPods

Part 1: Define your expression target Molecules expressed: Ectoine biosynthesis enzymes (EctA, EctB, EctC) Sequences: The ectABC operon sequence derived from Leptospirillum ferriphilum YR01, retrieved from NCBI

• EctA:
  1 gtggacagca aaaatatttc tccggcctgt ctggatttcc agaccccgac ccgcacagaa 61 gcggaacgtt tcttccggtt ccggacgccg acagaccttg atggccggtc tgttcatcgc 121 ctgatctcgc gctgtccgcc gctggatgcc aactccgtct atgccaatct cctccagtgt 181 ctccattttt ccggaacatc catccttgca gaaacaggag acggacagct ggcagcattc 241 atctccggat atgtgcttcc ggaaaacccg gacacgcttt tcatctggca ggtcgcggtc 301 gatcccgttc accgggggaa gggagtcgcc ctgtccatgc ttgacgcgct cctcgaccgg 361 acgattcctg cgggcgtccg ctatctggaa accacgatct ccccggaaaa cggtccctcc 421 cggaccttgt tccagaagct tttctcccgg aggaaagcgc cgttttccaa acgtcctctc 481 ttcggcagag agacacattt cgggggaggg cacgacgacg aggtcctgta tcgcggaggg 541 cccttttccg ccgtcggccc atga

• EctB: 1 atgaagattt ttgaagaaaa cgaatccgtc gtccgcagtt attgtcggtc ttttcccgcc 61 attttccggg aagcccgcgg cgccgaactg atttcgacgg aaggggacca ctatctcgac 121 tttctcgcgg gagcgggcac cctgaactac ggacacaacc accctgtcct caagaaagcc 181 ctgatcaact acatccagga agacgggatt acccacagcc tggacctcta tacaacggcc 241 aaggagcgat ttatcgagac gttcaaccgg ttgatcctga atccccgcgg tatgggaaat 301 taccgcatgc agtttacggg accgacggga gccaacgcag tggaagccgc actcaagctt 361 gcacgaaaga tcacaggacg aaccaacatc ctgagcttta ccaacggctt tcatggctgc 421 tccatcgggg cccttgccgc gacaggaaac cgtcatcacc gtggcggggc aggcgttccg 481 ctgtctcatg tcagtcgcat gccctatgcc aactacttcg gcgaacaggt caataccatc 541 gccatgatgg acaagatgct caacgaccct tcgagcggcg tcgacaaacc ggccgcagcc 601 atcgtcgagg ttgtccaggg agagggggga ctgaataccg cttcccccga ctggatgcga 661 aagcttgaaa aactctgtcg aaagcacggc atgcttctga tcctggacga tatccaggcc 721 ggatgcggcc ggacaggtca ttttttcagt tttgaggaca tggggatccg tcctgacatt 781 gtcaccctgt ccaagtccct gagcggattc ggactgccgc ttgcgatcgt gctgatgaaa 841 gaggatctcg accaatggaa agccggcgag cacaacggca cttttcgcgg gaacaatcac 901 gctttcgtca cggccaccgt ggctcttgag catttctgga gcgatgatgc cttttcccgg 961 agcatccggg gaaaaagccg gcttctttcc gaacggttaa accgtatcgt ccttcgtcac 1021 ggcccccatt ccctcaaggt gaaagggcgg gggatgatgc agggcattgc ctgtccggac 1081 ggggaaacgg ccgaagccat ctgcacgcgc gcctttgata acggactgat tcttgaaacg 1141 tgcggctcca acgccgaagt cgtgaaatgc ctcagtccgc tgaccatcac caaagaacag 1201 atcgaccggg ccatggatat cctcgaagag gcgttttccg ccgtcctctc cgaacccgtt 1261 tccagccggt cttcctga

• EctC: 1 atgattgtcc gaacattgga agaagccgaa caaagcgagc gaagggtggt gacgcctacc 61 tgggaaagca cacgcctgct cctgaaaaat gacaggatgg gcttttcatt ccatatcacg 121 accatctacg ccaataccga aacccacatc tgctaccaga accacctgga agccgtttat 181 tgcatcagcg gagaaggaga ggtcgagaca ctggccgacg gcgtggtcca taaaattcgg 241 cctggaacgc tctatgttct cgatcggcac gacgaccacc tgttaagagg catttcggag 301 atgaagctcg cctgtgtctt caaccctccc ctgagcggaa aagaagtgca tgacgaaaag 361 ggggtctacc cgctggaggc cgaacccatc tcctga

Chassis: Halomonas bluephagenesis

Destination of products: Cytoplasm

Part 2: Design your genetic parts Promoter: From Leptospirillum ferriphilum YR01 ectA AAAAATCCTTCAAAAACAAAGGGCATTTTTGTCATTGGAAACAAAAAAATTGGATTTCAGGTTTGACATT CCGGCGTTCACTTGCAAAACTAGAAAACATGCACAAACGGTTTGAGGGGTTTTAAACGTTGCCCTGCCGG CGTGTCGCTGGCAGGGGGGGGAAGCTGTTTTTGAAACGAGAGGGAGGCATGACGTTCGACGCGTCATGCA GGAAGGAGGAGAAGAAGCCCGAAAAACAACACGATTTCTGACTTTCCGGAAGAGAATTTCCGGACAGAAA AAGGTCATCTCTAAACCGCCCGTTTTACCCGACATCGGACCCCGAAAAAGTTCACCCCCGCAACCCCTGA ACAAAGGCACCACAACAAGAGTTTCTCTCCGGACCGGCCAGATGGCCAATTGTTTTTTCTCCGGTGACGC GGTTCTCATAAATGCATGTTGCCCGTTCAGCATCCTGACCGGACGCTCGTTTTATGACATATGCCAAAGG AGGGACGCCC

RBS: RBS (Elowitz 1999) - B0034 AAAGAGGAGAAA

CDS: EctABC

Terminator: Double terminator - B0015 CCAGGCATCAAATAAAACGAAAGGCTCAGTCGAAAGACTGGGCCTTTCGTTTTATCTGTTGTTTGTCGGT GAACGCTCTCTACTAGAGTCACACTGGCTCACCTTCGGGTGGGCCTTTCTGCGTTTATA

Vector context:

Part 3: Assemble your construct

Part 4: Optimize and quality-check a short paragraph per tool covering what you ran, what it returned, and what you concluded. Screenshots welcome.

Part 5: From design to build How would you build it? What does synthesis constrain? Synthesis has error rates and struggles with certain sequences. Does your design contain anything hard to synthesize? How would error correction or fragment-based assembly change your approach? Prototype vs. scale. Mirroring HW1: the way you’d build one construct to test your idea may differ from how you’d produce DNA at the scale your product needs. Briefly describe both if they differ.

Part 6: What don’t you know yet?

Final Project