Week 1 HW: Final Project Ideas

Predictive Biomanufacturing of Functional Mycelium–Nanoparticle Materials

Why this matters: Mycelium-based materials offer a renewable and biodegradable platform for functional materials, but their performance depends strongly on the biological state of the fungus and the conditions used to process it. My project explores whether Ganoderma lucidum can act not only as the material itself, but also as a living manufacturing system that helps form antimicrobial silver nanostructures within the mycelial network. I want to combine this biological system with automated cultivation and characterization to understand how process conditions influence nanoparticle formation, structure, and function. In the longer term, the goal is to move from small-scale experiments toward a scalable, data-driven and predictive biomanufacturing platform where desired material properties can guide how the biological process is designed and controlled.

                    WHY THIS MATTERS
                    ----------------

                 Mycelium-based materials
                 ------------------------
              +---------------------------+
              | Renewable & biodegradable |
              | material platform         |
              +-------------+-------------+
                            |
                            v
              +---------------------------+
              | Can be vulnerable to      |
              | microbial growth and      |
              | degradation               |
              +-------------+-------------+
                            |
                            v
               Need antimicrobial function
                            |
             +--------------+--------------+
             |                             |
             v                             v

    CONVENTIONAL APPROACH              IN-SITU APPROACH
    ---------------------              ----------------

+---------------------------+      +---------------------------+
| Synthesize antimicrobial |      | Use the living fungal     |
| agent separately         |      | system as part of the     |
| e.g. silver nanoparticles|      | manufacturing process     |
+-------------+-------------+      +-------------+-------------+
              |                                  |
              v                                  v
+---------------------------+      +---------------------------+
| Chemical synthesis may   |      | Fungal-associated        |
| require reductants,      |      | biomolecules interact    |
| stabilizers and energy   |      | with silver ions         |
+-------------+-------------+      +-------------+-------------+
              |                                  |
              v                                  v
+---------------------------+      +---------------------------+
| Purify / stabilize the   |      | In-situ formation of     |
| antimicrobial agent      |      | silver nanostructures    |
+-------------+-------------+      | within the mycelium      |
              |                    +-------------+-------------+
              v                                  |
+---------------------------+                    v
| Incorporate into the     |      +---------------------------+
| material                 |      | Nanoparticle formation    |
|                          |      | and material formation    |
| • Coating                |      | become more integrated    |
| • Impregnation           |      +-------------+-------------+
+-------------+-------------+                    |
              |                                  v
              v                    +---------------------------+
+---------------------------+      | Functional mycelium      |
| Possible limitations     |      | nanocomposite             |
|                          |      |                           |
| • Additional processing |      | Antimicrobial function   |
| • Additional energy use |      | integrated within the    |
| • Possible uneven       |      | material structure       |
|   distribution          |      +-------------+-------------+
| • Possible leaching or  |                    |
|   weak integration      |                    v
+---------------------------+      +---------------------------+
                                   | Streamlined              |
                                   | biomanufacturing         |
                                   +-------------+-------------+
                                                 |
                                                 v
                                   +---------------------------+
                                   | Fewer separate           |
                                   | manufacturing steps      |
                                   | and better opportunity   |
                                   | for structural           |
                                   | integration              |
                                   +-------------+-------------+
                                                 |
                                                 v
                                   +---------------------------+
                                   | Antimicrobial bio-based  |
                                   | functional material      |
                                   +---------------------------+

Where I want to take this:

Fungal physiological state + Process conditions
                      |
                      v
            Nanoparticle formation
                      |
                      v
             Structure + Function
                      |
                      v
        Automation + Systematic Data
                      |
                      v
              Predictive Model
                      |
                      v
      Scalable & Controllable Biomanufacturing         

1. What would I like to biomanufacture and why?

I would like to biomanufacture a mycelium-based antimicrobial material containing in-situ synthesized silver nanoparticles (AgNPs). My interest comes from my current research on Ganoderma lucidum, where I study how fungal biomass can participate in nanoparticle formation rather than acting only as a passive scaffold.

The conventional route is usually to synthesize an antimicrobial agent separately and then incorporate it into a material through methods such as coating or impregnation. This can require additional chemicals, purification, processing steps, and energy, while the antimicrobial agent may also be less uniformly distributed within the final material. My idea is to explore whether these steps can be streamlined through in-situ synthesis, where the fungal system itself helps form silver nanostructures directly within the mycelial network.

The target is therefore not simply a mycelium material with silver added to it, but a bio-integrated functional material in which the living system contributes to both material formation and antimicrobial function. Possible applications include biodegradable protective packaging and other temporary materials where microbial control is useful.

What interests me most is understanding how fungal physiological state, processing conditions, nanostructure, and final function are connected. In the long term, I would like to move from trial-and-error fabrication toward a process where the desired material properties can be predicted and controlled.

2. What living system would I use?

I would use the filamentous fungus Ganoderma lucidum as the living manufacturing chassis. Its mycelium forms an interconnected network and provides a biologically active environment containing biomolecules that can interact with metal ions. In my proposed system, the fungus would first be grown to produce active mycelial biomass. The biomass would then interact with a silver precursor, allowing fungal-associated biomolecules to participate in silver-ion transformation and nanoparticle formation. The resulting output would be functionalized mycelium containing biologically formed silver nanostructures, which could later become part of a larger bio-based material.

Why Ganoderma lucidum?

Where does mycelium fit in the fungal life cycle?

My focus is the mycelial network, rather than the fruiting body. In this system, the mycelium serves both as a living manufacturing environment and as part of the material scaffold.

The main assumptions are that sufficient fungal viability can be retained for subsequent material formation and that nanoparticle loading and distribution can eventually be controlled reproducibly.

3. How would I use automation?

I would use automated cultivation, sampling, characterization, and data analysis to make the biomanufacturing workflow more reproducible and data-rich. The main goal would be to run multiple fungal conditions in parallel, collect samples at defined time points, and connect biological state and process conditions with nanoparticle formation and final material performance.

Automation could be used for liquid handling, parallel culturing, scheduled sampling, UV–Vis measurements, SEM/EDX image analysis, and antimicrobial testing. A parallel microbioreactor platform such as the Ambr 15 could be one example for small-scale process development, but the broader idea is to build an automated workflow rather than depend on one specific instrument.

Automated workflow

Experimental design
        |
        v
Parallel fungal cultivation
        |
        v
Controlled process conditions
        |
        v
Scheduled sampling
        |
        v
+-----------------------------+
| Characterization            |
|                             |
| Ag+ depletion               |
| UV-Vis                      |
| SEM / EDX                   |
| Image analysis              |
| Antimicrobial testing       |
+-------------+---------------+
              |
              v
      Structured dataset
              |
              v
 Mechanistic + statistical
      modelling
              |
              v
   Predictive biomanufacturing

4. How much would I need to make, and how long would it take?

For a first meaningful scale, I would target a pilot demonstration of 1,000 antimicrobial protective-packaging inserts. The values below are preliminary engineering assumptions for this homework rather than measured production parameters.

                     PILOT-SCALE TARGET
                     ------------------

              +---------------------------+
              |      1,000 usable        |
              |    packaging inserts      |
              +-------------+-------------+
                            |
                            v
              +---------------------------+
              |   ~50 g material / piece  |
              +-------------+-------------+
                            |
                            v
              +---------------------------+
              | 1,000 × 50 g = 50 kg     |
              | finished material         |
              +-------------+-------------+
                            |
                            v
              +---------------------------+
              | Allow ~10% manufacturing |
              | loss / rejection          |
              +-------------+-------------+
                            |
                            v
              +---------------------------+
              | ~1,111 pieces required    |
              | ~56 kg material           |
              +-------------+-------------+
                            |
                            v
              +---------------------------+
              | 12 parallel modules       |
              | × 100 pieces / module     |
              +-------------+-------------+
                            |
                            v
              +---------------------------+
              | ~1,200 pieces / batch     |
              +-------------+-------------+
                            |
                            v
        +-----------------------------------------+
        |          ESTIMATED PRODUCTION TIME      |
        |                                         |
        | Material growth / formation   ~5 days   |
        | Drying / stabilization        ~2 days   |
        | Handling + quality control    ~1 day    |
        |                               --------  |
        | Total                         ~8 days   |
        +--------------------+--------------------+
                             |
                             v
              +---------------------------+
              | ~1,000 usable products    |
              | every ~8-day pilot batch  |
              +---------------------------+


         PROTOTYPING                    MANUFACTURING
         ------------                   -------------

 Automated small-scale                 Larger biomass
 cultivation & testing                 production
          |                                  |
          v                                  v
 Identify reproducible       --->    Parallel material-
 process conditions                  forming units
                                             |
                                             v
                                    Drying + Quality Control

Estimated pilot target: ~56 kg of material → 1,000 usable products → ~8 days per production cycle

Actual manufacturing capacity would need to be determined experimentally from biomass yield, material density, growth time, drying time, production capacity, and rejection rate.

5. What do I still need to understand?

The main challenge is not simply whether the fungus can form silver nanoparticles, but whether the process can become predictable, reproducible, and scalable. I see three major unknowns that need to be solved before this could become a reliable biomanufacturing process.

                KEY UNKNOWNS
                ------------

+----------------------------------+
| 1. Biological variability       |
|                                  |
| Which aspects of fungal          |
| physiological state most strongly|
| control nanoparticle formation?  |
+----------------+-----------------+
                 |
                 v
+----------------------------------+
| 2. Process → Structure → Function|
|                                  |
| How do processing conditions     |
| affect nanoparticle formation,   |
| distribution, and antimicrobial  |
| performance?                     |
+----------------+-----------------+
                 |
                 v
+----------------------------------+
| 3. Scale-up                      |
|                                  |
| Will relationships observed at   |
| small scale remain predictable   |
| in larger manufacturing systems? |
+----------------------------------+

5.1 What do I still need to understand and learn?

My current background gives me experience in materials science, biomaterials, fungal cultivation, nanoparticle synthesis, and materials characterization. Through my current research, I have learned how to grow mycelium, investigate biologically mediated silver nanoparticle formation, use techniques such as UV–Vis and SEM/EDX, perform antimicrobial testing, and design experiments to compare processing conditions.

However, moving from an experimental material to a predictable biomanufacturing process requires skills that go beyond what I currently know. One reason I am interested in HTBAA is to begin connecting my existing materials research with automation, bioprocessing, and scalable manufacturing.

             WHAT I ALREADY KNOW
             -------------------

        Materials Science
               |
               v
     Biomaterials + Mycelium
               |
               v
     Nanoparticle Formation
               |
               v
   Materials Characterization
      UV-Vis | SEM | EDX
               |
               v
     Antimicrobial Testing
               |
               v
       Experimental Data


                    ↓
            WHAT I NEED TO BUILD
                    ↓


      Bioprocess Engineering
               |
     +---------+---------+
     |                   |
     v                   v
Bioreactor control    Scale-up
and monitoring        principles
     |
     v
Lab + Process Automation
     |
     +--> Automated liquid handling
     +--> Sensors and process monitoring
     +--> Automated sampling
     +--> Reproducible workflows
     |
     v
Data Integration
     |
     +--> Experimental design
     +--> Reaction kinetics
     +--> Statistics
     +--> Automated image analysis
     +--> Programming / data pipelines
     |
     v
Predictive Modelling
     |
     +--> Machine learning
     +--> Process prediction
     +--> Optimization
     |
     v
Scalable & Controllable
Biomanufacturing

What I hope to develop through HTBAA

For me, HTBAA is an opportunity to begin filling the gap between making a functional biomaterial in the laboratory and engineering a reproducible biomanufacturing system. I particularly want to develop practical skills in laboratory automation, automated experimental workflows, bioprocess design, process monitoring, data integration, and scale-up thinking. I also want to become more comfortable connecting hardware, biological experiments, and computational tools within the same workflow.

Some areas, especially advanced fungal physiology, detailed kinetic modelling, and machine learning, will require continued training beyond a single course. However, HTBAA can help me build the biomanufacturing and automation foundation needed to connect these areas together.

The direction I want to move toward

WHAT I CAN DO NOW

Biology + Materials + Characterization
                 |
                 v
         Observe what happens

                 +

WHAT I WANT TO LEARN

Automation + Bioprocessing + Data + Modelling
                 |
                 v
       Understand why it happens
                 |
                 v
       Predict what will happen
                 |
                 v
        Control how it happens

Ultimately, I want to move from observing nanoparticle formation in a biological material toward being able to measure, model, predict, and eventually control the process.

6. How do I use AI in my research workflow?

I use ChatGPT (GPT-5.6 Sol) as a research-support tool, not as a replacement for experiments, scientific judgement, or my own interpretation of the data. I mainly use it to help organize ideas, explore possible experimental approaches, clarify concepts, improve scientific writing, perform preliminary calculations, troubleshoot analysis workflows, and connect different parts of my research more clearly.

              MY AI-ASSISTED WORKFLOW
              -----------------------

Research question / Experimental problem
                 |
                 v
        Discuss with ChatGPT
                 |
        +--------+---------+
        |                  |
        v                  v
 Clarify concepts      Explore options
 Generate questions    Compare approaches
        |                  |
        +--------+---------+
                 |
                 v
      Design / refine experiment
                 |
                 v
           Analyze data
                 |
                 v
      Interpret the results
                 |
                 v
   Improve communication and
     planning for next steps

What I use ChatGPT for

• Experimental thinking — discussing controls, possible sources of error, alternative methods, and questions that should be tested.
• Data analysis — checking calculations, statistical approaches, visualization ideas, and analysis workflows.
• Scientific communication — improving the structure and clarity of reports, presentations, webpages, and research explanations.
• Learning — helping me understand unfamiliar topics in biology, bioprocessing, automation, modelling, and programming.
• Research planning — helping me connect my current experiments with longer-term questions in mechanistic understanding, automation, and predictive biomanufacturing.

For this HTBAA project, I also used ChatGPT to help me organize the project concept, simplify diagrams, develop back-of-the-envelope calculations, and translate my research direction into a biomanufacturing framework, while keeping unpublished experimental details out of the public webpage.

Ultimately, I use AI as a thinking and communication assistant, while experimental decisions, interpretation, and scientific conclusions remain my responsibility.

References

1. Osorio-Echavarría J, Ossa-Orozco CP, Gómez-Vanegas NA. Synthesis of silver nanoparticles using white-rot fungus Anamorphous Bjerkandera sp. R1: influence of silver nitrate concentration and fungus growth time. Scientific Reports. 2021;11(1):3842. doi:10.1038/s41598-021-82514-8.

2. Mukherjee P, Ahmad A, Mandal D, Senapati S, Sainkar SR, Khan MI, Parishcha R, Ajaykumar PV, Alam M, Kumar R, Sastry M. Fungus-mediated synthesis of silver nanoparticles and their immobilization in the mycelial matrix: a novel biological approach to nanoparticle synthesis. Nano Letters. 2001;1(10):515–519. doi:10.1021/nl0155274.

3. Karwa A, Gaikwad S, Rai MK. Mycosynthesis of silver nanoparticles using Lingzhi or Reishi medicinal mushroom, Ganoderma lucidum (W. Curt.:Fr.) P. Karst. and their role as antimicrobials and antibiotic activity enhancers. International Journal of Medicinal Mushrooms. 2011;13(5):483–491. doi:10.1615/IntJMedMushr.v13.i5.80.

4. Ahmed S, Ahmad M, Swami BL, Ikram S. A review on plants extract mediated synthesis of silver nanoparticles for antimicrobial applications: a green expertise. Journal of Advanced Research. 2016;7(1):17–28.

5. Stamets P. Mycelium Running: How Mushrooms Can Help Save the World. Ten Speed Press; 2005.

6. Stamets P. Earth’s natural internet. Whole Earth Review. 1999;Fall:74–77.

7. Pramila K, Sushil K. Microbes mediated synthesis of metal nanoparticles: current status and future prospects. International Journal of Nanomaterials and Biostructures. 2016;6(1):1–24.

8. Wong-Pinto LS, Menzies A, Ordóñez JI. Bionanomining: biotechnological synthesis of metal nanoparticles from mining waste—opportunity for sustainable management of mining environmental liabilities. Applied Microbiology and Biotechnology. 2020;104:1859–1869. doi:10.1007/s00253-020-10353-0.

9. Bahrulolum H, Nooraei S, Javanshir N, et al. Green synthesis of metal nanoparticles using microorganisms and their application in the agrifood sector. Journal of Nanobiotechnology. 2021;19:86. doi:10.1186/s12951-021-00834-3.

Week 2 HW: DNA Design

Part 1: Define the Expression Target

What molecule am I expressing?

I will express nitrate reductase (NR) from Ganoderma lucidum. NR is a multidomain redox enzyme involved in nitrate metabolism. The published G. lucidum NR contains regions associated with molybdopterin/Moco, cytochrome b5/heme, FAD, and NAD binding, giving the protein an internal electron-transfer architecture.

Figure 1. Motif/domain schematic of Ganoderma lucidum nitrate reductase (NR).
Redrawn from the motif analysis reported by Liu et al. (2018). The predicted regions are associated with molybdenum cofactor, cytochrome b5/heme, FAD, and NAD-dependent electron transfer.

NR can transfer reducing equivalents through these cofactors, which is why I selected it as a mechanistically plausible candidate for testing whether increased reductase activity can influence Ag⁺ reduction. However, a direct role of NR in Ag⁺ reduction in my G. lucidum system has not yet been demonstrated.

NAD(P)H-derived electrons
          ↓
         FAD
          ↓
   Heme / Cyt-b5
          ↓
 Moco / molybdopterin
          ↓
   Nitrate reduction

What sequence will I use?

I will use the complete coding sequence of the Ganoderma lucidum nitrate reductase gene, GenBank accession MG601724.1.

The sequence I will use is the spliced mRNA/CDS, rather than the intron-containing genomic sequence. The supplemental material also provides primers designed to amplify the NR ORF and detect NR expression.

I will keep the NR protein untagged so that the first construct introduces as few additional variables as possible when testing the effect of NR overexpression on Ag⁺ reduction.

What host or chassis will I use?

The host will be Ganoderma lucidum, the same fungal chassis used in my HW1 project.

My experimental fungus is a commercial G. lucidum strain without a sequence-defined strain ID. Therefore, MG601724.1 is being used as the reference nitrate-reductase sequence for this design.

Before laboratory implementation, I would verify the endogenous nr sequence in my strain to confirm that it is sufficiently similar to the reference sequence used in the construct.

I chose to keep the host and target gene within the same species to make the design more biologically compatible and to reduce uncertainty associated with expressing a heterologous protein.

Where does the expressed protein need to go?

For this design, NR will remain intracellular, and I will not add a secretion signal or other targeting peptide.

The goal is to keep the engineered NR as close as possible to its native cellular form while testing whether increased NR expression changes Ag⁺ reduction.

Pnr induction
     ↓
NR expression
     ↓
Intracellular NR activity
     ↓
Test effect on Ag+ reduction

Why nitrate reductase?

I selected nitrate reductase because it is a redox-active enzyme with an internal electron-transfer system involving NAD(P)H-associated, FAD, heme/cytochrome b5, and molybdenum-cofactor regions.

My hypothesis is that increasing NR expression may alter the reducing capacity of the fungal system and therefore influence Ag⁺ reduction and AgNP formation.

Inducible NR overexpression
          ↓
   ↑ NR abundance/activity
          ↓
Change in cellular reducing capacity
          ↓
      Ag+ → Ag0
       HYPOTHESIS
          ↓
AgNP nucleation / growth
          ↓
Functional mycelium–AgNP material

How would I test this hypothesis?

I would compare the engineered NR-overexpression strain with appropriate controls and measure whether increased NR expression produces a measurable change in silver reduction.

NR expression
      ↓
NR enzyme activity
      ↓
Ag+ depletion kinetics
      ↓
AgNP formation
      ↓
UV–Vis + SEM/EDX
      ↓
Antimicrobial performance

If NR overexpression changes Ag⁺ depletion or AgNP formation relative to the controls, this would support a role for NR-associated redox activity in the process. If no clear change is observed, other fungal enzymes or metabolites may be more important contributors.

Part 2: Design the Genetic Parts

1. Transcription start — inducible promoter

For transcriptional control, I will use a candidate nitrate-reductase promoter (Pnr) from Ganoderma lucidum.

I selected the 1 kb region immediately upstream of the native nr locus as a candidate Pnr sequence. The goal is to use nitrogen-responsive regulation so that NR expression can be increased after the mycelium has already developed, rather than continuously overexpressing NR throughout fungal growth.

Mycelial growth
      ↓
Nitrate induction
      ↓
Pnr activation
      ↓
NR expression

Justification: An inducible promoter gives better temporal control than constitutive expression and should make it easier to distinguish the effects of NR overexpression on Ag⁺ reduction from effects on normal fungal growth.

Important limitation: This 1 kb sequence is a candidate regulatory region from a reference G. lucidum strain, not a promoter that has been experimentally validated in my commercial strain. Its inducibility would therefore need to be tested experimentally.

2. Translation initiation — native start context

For the first construct, I will not introduce a synthetic Kozak sequence. Instead, I will preserve the native start of the G. lucidum nr coding sequence and use its natural ATG start codon.

Justification: Translation-initiation preferences can vary between fungal species, so I prefer to retain a host- and gene-matched start context rather than introduce a generic mammalian Kozak sequence.

Important limitation: The exact transcription start site and native 5′ UTR boundary of nr have not yet been experimentally defined for the reference sequence used here. This region would need to be experimentally mapped or verified before a laboratory-ready construct is finalized.

3. Coding sequence — nitrate reductase (NR)

The coding sequence will be the complete Ganoderma lucidum nitrate reductase CDS from GenBank accession MG601724.1.

I will use the spliced mRNA/CDS sequence rather than the intron-containing genomic gene. This gives a continuous coding region suitable for assembly and later sequence optimization.

Justification: NR is the central test gene in this design because it is a redox-active enzyme and provides a direct way to test whether increasing reductase activity alters Ag⁺ reduction and AgNP formation.

Important limitation: MG601724.1 is used as a reference sequence, because my commercial G. lucidum strain is not sequence-defined. Before laboratory implementation, I would verify the endogenous nr sequence in my strain.

Protein tag: None. I am keeping NR untagged to avoid introducing another variable that could affect folding, catalytic activity, or interpretation of Ag⁺ reduction.

4. Terminator — A. nidulans trpC terminator

For transcription termination, I will use the Aspergillus nidulans trpC terminator (TtrpC).

Justification: I selected TtrpC because it has been used successfully in fungal expression systems, including G. lucidum, and it avoids duplicating the native TsdhB sequence already present in the cbxR selectable-marker cassette.

Important limitation: TtrpC is a heterologous terminator, so although it has precedent in fungal expression, its exact termination efficiency in my commercial G. lucidum strain would still need to be confirmed experimentally.

5. Selectable marker — cbxR / sdhB-H216L

For fungal selection, I will use the Ganoderma lucidum sdhB-based carboxin-resistance marker (cbxR).

Justification: I selected cbxR because it is a homologous G. lucidum selection system, making it more biologically compatible with my fungal chassis than using an unrelated heterologous resistance marker.

Design choice: I will keep the cbxR cassette intact, including its native regulatory regions, rather than modifying its terminator or promoter. This minimizes changes to an established fungal selection system.

Important limitation: My commercial G. lucidum strain is not genetically characterized, so its baseline sensitivity to carboxin would need to be tested before using cbxR experimentally.

6. Vector backbone — pJW-EXP-derived context

For the vector context, I will use a pJW-EXP-derived Ganoderma lucidum expression backbone.

The original pJW-EXP system was developed for genetic manipulation of G. lucidum. In this design, I will keep the backbone and fungal selection strategy, but replace the original constitutive expression cassette with my nitrate-responsive NR cassette.

pJW-EXP-derived backbone
        |
        +--> bacterial propagation / selection
        |
        +--> cbxR fungal selection cassette
        |
        +--> Pnr → NR → TtrpC

Justification: I selected a pJW-EXP-derived backbone because it was developed for G. lucidum and provides a host-compatible starting point for fungal transformation and selection.

Important limitation: I am using the published pJW-EXP architecture as the vector context, but I do not yet have a complete sequence-verified copy of the exact backbone. The final plasmid sequence would need to be confirmed before synthesis or assembly.

Proposed Genetic Architecture

The final design combines the NR expression cassette with the fungal selection cassette and a pJW-EXP-derived vector context.

Figure 2. Genetic architecture of the proposed NR overexpression system.
The main expression cassette contains the candidate nitrate-responsive promoter (Pnr), the G. lucidum nitrate reductase CDS (MG601724.1), and the A. nidulans trpC terminator (TtrpC). A separate G. lucidum cbxR / sdhB-H216L cassette provides fungal selection within a pJW-EXP-derived vector context.

Main expression cassette
Pnr → NR CDS → TtrpC

          +

Selection cassette
native sdhB regulatory region
          ↓
   sdhB-H216L (cbxR)
          ↓
      native TsdhB

          +

pJW-EXP-derived vector context

Part 3: Assemble the Construct in Kernel

I assembled the proposed nitrate reductase expression cassette in Asimov Kernel using three sequence-defined parts:

Pnr → NR (MG601724.1) → TtrpC

The final cassette is 4,185 bp long:

• Pnr_Glucidum_candidate: 1–1000 bp
• NR_MG601724: 1001–3409 bp
• TtrpC_Anidulans: 3410–4185 bp

Kernel Part Match identified and linked all three components in the expected 5′→3′ orientation.

Figure 3. Kernel linear map of the proposed inducible nitrate reductase expression cassette.

The 4,185-bp construct contains the candidate G. lucidum nitrate-responsive promoter, the nitrate reductase CDS (MG601724.1), and the A. nidulans trpC terminator.

The sequence viewer displayed translation across the NR coding region and reported no lintable errors.

The current Kernel construct represents the expression cassette only. The complete pJW-EXP-derived plasmid, including the fungal selection cassette and bacterial backbone, remains pending sequence-level backbone verification.

Selection Cassette

The complete transformation system also requires a separate fungal selection cassette:

native sdhB regulatory region → sdhB-H216L (cbxR) → native TsdhB

The cbxR cassette provides carboxin resistance, allowing transformed Ganoderma lucidum cells to be selected.

I have not yet assembled this cassette as a sequence-defined Kernel construct because the exact regulatory-region boundaries and final engineered sdhB-H216L sequence still require sequence-level verification.

Therefore, the current Kernel construct represents the NR expression cassette, while the cbxR cassette is retained as part of the proposed pJW-EXP-derived vector architecture.

Part 4: Optimize and Quality-Check

4.1 Codon Optimization

I evaluated the Ganoderma lucidum nitrate reductase CDS (MG601724.1) using Kernel’s coding-sequence optimization workflow.

Because the designed NR protein is intended to remain intracellular, I disabled the automatically suggested signal peptide so that the native protein localization would not be changed.

Kernel did not provide Ganoderma lucidum as an available expression organism. The available options were:

• Cricetulus griseus
• Homo sapiens
• Escherichia coli
• Saccharomyces cerevisiae

Figure 4. Kernel expression-organism options for codon optimization.
Ganoderma lucidum was not available as a host in the Kernel coding-sequence optimizer. Therefore, Saccharomyces cerevisiae was selected only as a fungal surrogate to demonstrate the optimization workflow.

To demonstrate the codon-optimization workflow, I used the following settings:

• Input: NR_MG601724
• Algorithm: Simple Codon Optimizer v0.3
• Asimov preset: None
• Signal peptide: Skipped
• Expression organism: Saccharomyces cerevisiae

The S. cerevisiae optimization was treated only as a comparative workflow exercise and was not intended to replace the native G. lucidum coding sequence.

However, the optimization run returned an internal server error, so no optimized sequence was generated for interpretation.

Figure 5. Kernel codon-optimization run error.
The surrogate S. cerevisiae optimization run returned an internal server error; therefore, no optimized sequence was generated or incorporated into the proposed construct.

Because MG601724.1 is already a native G. lucidum coding sequence and the intended expression host is also G. lucidum, I did not substitute a sequence optimized using codon preferences from another organism.

Final design decision: retain the native MG601724.1 NR CDS in the proposed construct.

Limitation: A host-specific codon-optimization analysis for G. lucidum could not be completed because the organism was not available in Kernel’s expression-host options, and the surrogate optimization run did not complete successfully.

4.2 Sequence Search / BLAST

To verify the identity of the nitrate reductase coding sequence used in the construct, I performed a nucleotide BLAST (BLASTN) search using the 2,409-bp NR_MG601724 CDS.

The search was performed against the NCBI Core Nucleotide (core_nt) database using the highly similar sequence search (megablast).

The strongest match was:

The query sequence showed 100% coverage and 100% nucleotide identity with the G. lucidum nitrate reductase reference sequence MG601724.1, confirming that the CDS incorporated into the Kernel construct matches the intended reference sequence.

A second significant match was detected with a nitrate reductase gene from Ganoderma boninense. This hit showed 81.96% identity over 35% of the query, supporting sequence conservation of nitrate reductase among related Ganoderma species.

Conclusion: The BLAST analysis confirms the identity of the selected NR coding sequence and supports its annotation as a Ganoderma nitrate reductase.

Figure 6. NCBI BLASTN results for the NR_MG601724 coding sequence.
The 2,409-bp query matched the Ganoderma lucidum nitrate reductase reference sequence MG601724.1 with 100% query coverage and 100% sequence identity. A partial homologous match was also observed with nitrate reductase from Ganoderma boninense.

4.3 Biosecurity Sequence Screening

I screened the NR_MG601724 nucleotide sequence using Kernel’s integrated SecureDNA Biosecurity Sequence Scanner.

SecureDNA compares the input nucleotide sequence against a database of sequences associated with potential biosecurity concerns.

The scan returned:

No flagged sequences detected.

Figure 7. SecureDNA biosecurity screening of the NR_MG601724 coding sequence.
Kernel’s integrated SecureDNA scanner did not identify any flagged sequences in the nitrate reductase sequence used in the proposed construct.

This result indicates that the NR coding sequence did not trigger the biosecurity screening criteria used by SecureDNA.

However, this result should not be interpreted as a general statement that the complete experiment is biologically safe or exempt from institutional oversight. It only indicates that the submitted nucleotide sequence was not flagged by this sequence-screening system.

Conclusion: No sequence-level biosecurity concerns were identified for the NR_MG601724 CDS by the SecureDNA screening tool.

Part 5: From Design to Build

For the first prototype, I would use commercial DNA synthesis + seamless assembly.

Build strategy

The sequence-defined expression cassette is:

Pnr           NR CDS            TtrpC
1,000 bp  +  2,409 bp   +      776 bp
                    ↓
                 4,185 bp

I would order the cassette as one or several synthetic DNA fragments and assemble it into the pJW-EXP-derived vector context using a method such as Gibson assembly.

Gibson et al. (2009) showed that overlapping DNA fragments can be assembled seamlessly in a single isothermal reaction.

Kernel design
     ↓
DNA synthesis
     ↓
Fragment assembly
     ↓
Sequence verification
     ↓
G. lucidum transformation

What synthesis constrains DNA synthesis can be affected by:

• repeats
• extreme GC content
• homopolymers
• difficult secondary structures
• synthesis errors If the full 4.2-kb cassette is difficult to synthesize, I would split it into smaller overlapping fragments.

Pnr
 +
NR
 +
TtrpC
 +
vector
 ↓
assembly

Because synthetic DNA can contain sequence errors, the final construct should be sequence-verified before transformation. Carr et al. (2004) showed the importance of error correction in de novo DNA synthesis.

Prototype vs scale

FIRST PROTOTYPE

1 construct
   ↓
commercial synthesis
   ↓
assembly
   ↓
sequence verification
   ↓
test in G. lucidum

Current limitation

Pnr → NR → TtrpC
      ✅ sequence-defined

cbxR cassette
      ⚠ still needs sequence verification

pJW-EXP-derived backbone
      ⚠ still needs complete sequence verification

Build decision: use commercial DNA synthesis + seamless assembly + sequence verification for the first prototype.

Part 6: What Don’t I Know Yet?

The main uncertainties in this design are still biological rather than sequence-level.

The first question is whether the candidate Pnr promoter will provide nitrate-responsive expression in my commercial Ganoderma lucidum strain. The promoter sequence was selected from a reference genome and has not yet been experimentally validated in my strain. I would test this by measuring NR transcript level and NR enzyme activity before and after nitrate induction.

The second and most important uncertainty is whether NR overexpression actually changes Ag+ reduction and AgNP formation. This remains the central hypothesis of the design:

↑ NR expression
      ↓
change in reducing activity
      ↓
change in Ag+ reduction
      ↓
change in AgNP formation

I would test this by comparing wild type, vector control, and NR-overexpression strains, followed by measurements of NR activity, Ag+ depletion kinetics, UV–Vis response, and SEM/EDX characterization.

The third uncertainty is whether the engineered strain will be stable and reproducible. Different transformants may have different integration sites, copy numbers, or expression levels. I would therefore compare several independent transformants for construct presence, NR expression/activity, Ag+ reduction phenotype, and stability during subsequent growth.

Main remaining bet: whether changing NR expression produces a measurable change in Ag+ reduction and AgNP formation.

Part 7: AI Usage

I used OpenAI ChatGPT (GPT-5.6 Sol) as a learning companion and design-support tool throughout this homework.

My background is primarily in materials science and biomaterials, so I used AI to help me understand unfamiliar concepts in fungal genetics and synthetic biology, compare design alternatives, interpret sequence information, and work through the Kernel workflow step by step.

AI was used for:

• explaining promoters, CDSs, terminators, selectable markers, and vector architecture
• comparing constitutive vs. inducible expression
• comparing promoter, terminator, and tagging strategies
• interpreting GenBank and BLAST results
• organizing the Pnr → NR → TtrpC construct
• guiding construct assembly and quality checks in Kernel
• discussing codon optimization, synthesis constraints, and experimental risks
• improving figures, Markdown organization, and scientific writing

Representative prompts included:

Why choose nitrate reductase as the expression target?

Should NR expression be inducible or constitutive?

How do the main expression cassette and selection cassette differ?

How should I represent a pJW-EXP-derived vector context?

Help me assemble Pnr → NR → TtrpC in Kernel.

Should I codon-optimize a native G. lucidum gene if G. lucidum is not available in Kernel?

Help me interpret the BLAST and SecureDNA results.

What are the biggest remaining biological risks in this construct?

I did not treat AI output as a scientific reference by itself. Sequence identities, accession numbers, construct components, and literature-based claims were checked against NCBI records, Kernel documentation, primary literature, and course readings where possible.

AI client: OpenAI ChatGPT
Model: GPT-5.6 Sol
Role: Learning companion, research/design support, and scientific communication assistance
Accessed: September 2026

Part 8: References

Nitrate reductase and Ganoderma lucidum

1. Liu R, Shi L, Zhu T, Yang T, Ren A, Zhu J, Zhao MW. Cross Talk between Nitric Oxide and Calcium-Calmodulin Regulates Ganoderic Acid Biosynthesis in Ganoderma lucidum under Heat Stress. Applied and Environmental Microbiology. 2018;84(10):e00043-18.
   https://doi.org/10.1128/AEM.00043-18

2. NCBI GenBank. Ganoderma lucidum nitrate reductase mRNA, complete CDS. Accession MG601724.1.
   https://www.ncbi.nlm.nih.gov/nuccore/MG601724.1

3. NCBI GenBank. Ganoderma lucidum strain G.260125-1 genomic scaffold. Accession AGAX01000028.1.
   https://www.ncbi.nlm.nih.gov/nuccore/AGAX01000028.1

Vector and selectable-marker design

4. Yu X, Ji SL, He YL, Ren MF, Xu JW. Development of an expression plasmid and its use in genetic manipulation of Lingzhi or Reishi medicinal mushroom, Ganoderma lucidum. International Journal of Medicinal Mushrooms. 2014;16(2):161–168.
   https://doi.org/10.1615/IntJMedMushr.v16.i2.60

5. Xu JW, Xu YN, Zhong JJ. Enhancement of ganoderic acid accumulation by overexpression of an N-terminally truncated 3-hydroxy-3-methylglutaryl coenzyme A reductase gene in the basidiomycete Ganoderma lucidum. Applied and Environmental Microbiology. 2012;78(22):7968–7976.
   https://doi.org/10.1128/AEM.01263-12

6. NCBI GenBank. Ganoderma lucidum sdhB genomic sequence. Accession JN377411.1.
   https://www.ncbi.nlm.nih.gov/nuccore/JN377411.1

Terminator sequence

7. NCBI GenBank. Aspergillus nidulans trpC gene. Accession X02390.1.
   https://www.ncbi.nlm.nih.gov/nuccore/X02390.1

Design-to-Build Readings

8. Gibson, D. G., Young, L., Chuang, R.-Y., Venter, J. C., Hutchison, C. A., & Smith, H. O. (2009).
   Enzymatic assembly of DNA molecules up to several hundred kilobases.
   Nature Methods, 6, 343–345.
   https://doi.org/10.1038/nmeth.1318

9. Carr, P. A., Park, J. S., Lee, Y.-J., Yu, T., Zhang, S., & Jacobson, J. M. (2004).
   Protein-mediated error correction for de novo DNA synthesis.
   Nucleic Acids Research, 32(20), e162.
   https://doi.org/10.1093/nar/gnh160

10. Tian, J., Gong, H., Sheng, N., Zhou, X., Gulari, E., Gao, X., & Church, G. (2004).
    Accurate multiplex gene synthesis from programmable DNA microchips.
    Nature, 432, 1050–1054.
    https://doi.org/10.1038/nature03151

11. Kosuri, S., Eroshenko, N., LeProust, E. M., Super, M., Way, J., Li, J. B., & Church, G. M. (2010).
    Scalable gene synthesis by selective amplification of DNA pools from high-fidelity microchips.
    Nature Biotechnology, 28, 1295–1299.
    https://doi.org/10.1038/nbt.1716

Software and Online Resources

12. Asimov. Kernel Documentation.
    Used for construct creation, part annotation, coding-sequence optimization, and sequence-design workflow.
    Accessed September 2026.

13. NCBI BLAST.
    Used to verify the identity of NR_MG601724. The query matched MG601724.1 with 100% query coverage and 100% nucleotide identity.
    Accessed September 2026.

14. SecureDNA Biosecurity Sequence Scanner — Kernel Documentation.
    Used for sequence-level biosecurity screening of the NR sequence through Kernel.
    Accessed September 2026.

15. OpenAI ChatGPT.
    GPT-5.6 Sol was used as a learning companion and design-support tool.
    Accessed September 2026.

Week 3 HW: Cell-Free Protocols

Introduction

This homework focuses on designing cell-free protein synthesis (CFPS) reaction compositions for sfGFP, PETase, and Reteplase, with the goal of improving functional protein production while reducing cost per gram. In CFPS, transcription and translation occur outside living cells, which makes the reaction highly tunable and well suited to rapid, automated screening.

This connects closely with my current research on mycelium–AgNP biomanufacturing. Although the products are different, both systems involve the same general challenge: understanding how reaction or process conditions influence kinetics, structure, function, and overall process efficiency.

For me, HW3 is therefore not only about optimizing a CFPS recipe. It is also a useful model for thinking about how to design and automate multivariable biomanufacturing experiments, which is directly relevant to my longer-term goal of building predictive workflows for mycelium–nanoparticle systems.

The Shared Research Logic is as follows.

INPUT CONDITIONS
      ↓
PROCESS KINETICS
      ↓
STRUCTURE / STATE
      ↓
FUNCTION
      ↓
OPTIMIZATION

The assignment also uses a staged 384-well → 96-well → 2 mL screening strategy and an automated platform to compare many reaction compositions efficiently. This is especially relevant to my research direction because I want to apply similar design-build-test-learn and automation strategies to study how fungal state and process conditions govern Ag⁺ reduction, AgNP formation, and material function.

A.1 Potassium Glutamate vs. Magnesium Glutamate

Both salts help establish the ionic environment required for CFPS. The homework treats potassium and magnesium salts as variable components of the reaction composition.

Their optimal concentrations differ between formulations because other reaction components, especially nucleotides and energy-system components, interact with Mg²⁺ and change how much is functionally available.

Which is more sensitive?
Mg²⁺ is generally the more sensitive variable, while K⁺ is also important but usually acts over a broader operating range.

Connection to My Research

The same principle also appears in my mycelium–AgNP system: the amount of an ion added to a reaction is not necessarily the same as the amount that is chemically or functionally available.

Key connection: in both systems, performance depends not only on the total concentration added, but also on how that component interacts with the surrounding biochemical environment.

What I can learn: In my research, this means I should consider how medium composition, biomolecules, and fungal metabolism influence Ag⁺ availability, rather than treating Ag⁺ concentration as an isolated variable.

A.2 PEP + NTP vs. Ribose + NMP

Key difference: PEP + NTP provides more of the required chemistry directly, while Ribose + NMP relies more on residual lysate metabolism and can reduce reagent cost.

Connection Back to A.1

This also explains why the optimal Mg²⁺ concentration can change between CFPS formulations.

Different nucleotide / energy system
                ↓
Different Mg²⁺ binding and availability
                ↓
Different free Mg²⁺ concentration
                ↓
Different optimal Mg-Glu level

Connection to A.1: This reinforces why Mg²⁺ is a sensitive tuning variable: its functionally available concentration depends on interactions with nucleotides and other reaction components, so changing the energy/nucleotide system can also shift the optimal Mg²⁺ level.

Connection to My Research

A similar interaction occurs in my mycelium–AgNP system.

What I can learn: in both systems, the amount initially added is not necessarily the amount functionally available. Therefore, I should consider interactions between process variables and ion availability over time, rather than optimizing Ag⁺ concentration as an isolated variable.

A.3 Comparison of Two Literature CFPS Formulations

I selected PANOx-SP from Jewett & Swartz (2004) and the RFopt formulation from Olsen et al. (2026). These represent two different approaches to supporting cell-free protein synthesis.

Composition Comparison

*Reported as the amino-acid component in the RFopt formulation.

The two formulations therefore differ not only in individual concentrations, but also in their overall strategy for supplying ions, energy, nucleotides, and cofactors.

Overall Formulation Strategy

Comparison: PANOx-SP uses PEP and ready-to-use NTPs, together with several added cofactors. In contrast, RFopt uses glucose, ribose, and NMPs, relying more on metabolic activity in the lysate to regenerate energy and nucleotides.

Main difference: PANOx-SP represents a more direct-supply formulation, while RFopt is a more simplified, metabolism-dependent, and cost-optimized formulation.

Connection to My Research

The PANOx-SP vs. RFopt comparison is similar to how I think about my mycelium–AgNP system.

What I can learn: a good formulation does not necessarily need more components; it needs the right combination of inputs and biological activity. For my research, this supports studying interactions between medium composition, fungal physiological state, Ag⁺ availability, and process conditions, rather than optimizing each factor independently.

Part B: First-Round CFPS Formulations

The following were my first-round 20 µL CFPS formulations submitted to the HTBAA coopetition. Values are final concentrations in the complete reaction, including contributions from the fixed base buffer.

Fixed Components

Each 20 µL reaction also contained:

• 5 µL lysate
• 2 µL DNA template
• 2 µL base buffer

sfGFP and PETase used the standard reducing lysate, while Reteplase used the designated Δgor oxidizing/DSB lysate with IAM treatment and cysteine quenching.

Design Rationale

All three formulations use the same low-cost glucose + ribose + NMP metabolic backbone so that differences in functional protein production can be interpreted more clearly.

• sfGFP: simple reducing baseline with no additional folding reagents.
• PETase: kept in the assigned reducing regime, with increased HEPES as an exploratory pH-buffering adjustment.
• Reteplase: uses GSSG/GSH + DsbC to support formation and correction of its multiple disulfide bonds.

These are first-round hypotheses rather than optimized formulations and will be refined using experimental results.

AI Use

I used ChatGPT (OpenAI, GPT-5.6 Sol) as a learning and design-support tool for this homework. I used it to:

• explain CFPS concepts that were new to me,
• compare literature reaction formulations,
• identify differences between PEP + NTP and Ribose + NMP strategies,
• connect CFPS formulation principles to my own biomanufacturing research,
• review literature beyond the papers provided in the assignment,
• brainstorm first-round reaction compositions for sfGFP, PETase, and Reteplase, and
• suggest a possible community reagent for improving disulfide-bond formation.

AI-generated ideas were treated as design hypotheses, not established results, and were checked against the assignment materials and published literature before being included.

Representative AI Prompts

1. Understanding reaction composition

   Why do potassium glutamate and magnesium glutamate concentrations differ between CFPS formulations, and which is more sensitive?

2. Energy and nucleotide strategy

   Explain the difference between a PEP + NTP reaction formulation and a Ribose + NMP formulation.

3. Literature comparison

   Compare PANOx-SP from Jewett & Swartz (2004) with the RFopt formulation from Olsen et al. (2026), focusing on reagent choice, concentration, energy regeneration, nucleotide strategy, and cost.

4. Broader literature review

   Review additional CFPS literature beyond the papers provided in the homework and identify important developments in energy regeneration, nucleotide recycling, reaction simplification, DOE, active learning, and AI-guided optimization.

5. Reaction design

   Using the homework constraints and published CFPS literature, propose first-round 20 µL reaction designs for sfGFP, PETase, and Reteplase. Explain the reasoning for salts, energy sources, building blocks, cofactors, and redox components.

6. Community reagent

   Suggest a reagent that could be added to the Community Reagents inventory, especially one that may improve functional folding of Reteplase, and support the suggestion with literature.

AI client: OpenAI ChatGPT Model: GPT-5.6 Sol Role: Learning companion, research/design support, and scientific communication assistance Accessed: October 2026

References

Cell-Free Protein Synthesis Formulations

1. Jewett MC, Swartz JR. Mimicking the Escherichia coli Cytoplasmic Environment Activates Long-Lived and Efficient Cell-Free Protein Synthesis. Biotechnology and Bioengineering. 2004;86(1):19–26.
   https://doi.org/10.1002/bit.20026

2. Calhoun KA, Swartz JR. Energizing Cell-Free Protein Synthesis with Glucose Metabolism. Biotechnology and Bioengineering. 2005;90(5):606–613.
   https://doi.org/10.1002/bit.20449

3. Olsen ML, Copeland CE, Sundberg CA, Aw R, Shaver ZM, Rao G, Swartz JR, Karim AS, Jewett MC. Design-Driven Optimization of Low-Cost Reagent Formulations for Reproducible and High-Yielding Cell-Free Gene Expression. Nature Communications. 2026;17:3478.
   https://doi.org/10.1038/s41467-026-69605-8

Cell-Free Biomanufacturing and Optimization

4. Silverman AD, Karim AS, Jewett MC. Cell-Free Gene Expression: An Expanded Repertoire of Applications. Nature Reviews Genetics. 2020;21:151–170.
   https://doi.org/10.1038/s41576-019-0186-3

5. Rasor BJ, Vögeli B, Landwehr GM, Bogart JW, Karim AS, Jewett MC. Toward Sustainable, Cell-Free Biomanufacturing. Current Opinion in Biotechnology. 2021;69:136–144.
   https://doi.org/10.1016/j.copbio.2020.12.012

6. Borkowski O, Koch M, Zettor A, et al. Large Scale Active-Learning-Guided Exploration for In Vitro Protein Production Optimization. Nature Communications. 2020;11:1872.
   https://doi.org/10.1038/s41467-020-15798-5

7. Zhu J, Meng Y, Gao W, et al. AI-Driven High-Throughput Droplet Screening of Cell-Free Gene Expression. Nature Communications. 2025;16:2720.
   https://doi.org/10.1038/s41467-025-58139-0

AI-Guided CFPS Design

8. Smith AA, Wong EL, Donovan RC, et al. Using a GPT-5-Driven Autonomous Lab to Optimize the Cost and Titer of Cell-Free Protein Synthesis. bioRxiv. 2026.
   https://doi.org/10.64898/2026.02.05.703998

Reteplase and Disulfide-Bond Folding

9. Zhuo XF, Zhang YY, Guan YX, Yao SJ. Co-Expression of Disulfide Oxidoreductases DsbA/DsbC Markedly Enhanced Soluble and Functional Expression of Reteplase in Escherichia coli. Journal of Biotechnology. 2014;192:197–203.
   https://doi.org/10.1016/j.jbiotec.2014.10.028

Software and Online Resources

10. HTBAA CFPS Coopetition.
    Used to design and submit the 20 µL CFPS reaction compositions for sfGFP, PETase, and Reteplase.
    Accessed October 2026.

11. OpenAI ChatGPT.
    GPT-5.6 Sol was used as a learning companion, literature-review assistant, and design-support tool for comparing CFPS formulations and developing first-round reaction hypotheses.
    Accessed October 2026.

Week 4 LAB: Cell-Free Manufacturing

Week 5 HW: Digital Microfluidics

Week 6 LAB: Microfluidic Manufacturing

Week 7 HW: Measurement & Assays

Week 8 LAB: Bacterial Cell Manufacturing

Week 9 HW: Genetic Circuit Design

Week 10 LAB: Mammalian Cell Manufacturing

Final Project