Week 2 HW: DNA design
Author: Dr. Michellie Hernandez, MD
Assisted by Kernel AI and ChatGPT for tutorial use of Kernel and broaden understanding.
Content ideation and creation was done without AI and solely done by Dr. Michellie Hernandez
Release date: September 27, 2026
Part 0: Set up in Kernel
Victoria Makerspace Node
Username: htgaa2@makerspace.ca
Name of Repository in All repositories folder: Tfh_Michellie_Hernandez_test
Construct: Tfh cell simulator (IL-4, IL-21 and CD40-ligand Combination plasmid)
Part 1: Define your expression target
What molecule are you expressing?
- Cytokine Interleukin-4 (IL-4)
- Interleukin-21 (IL-21)
- CD40-Ligand
What is the sequence?
What host or chassis?
- Mammalian Cell-free system
Where does the product need to go?
- Combined plasma will go in a cytoplasm in a cell-free system that needs to be secreted from the cell-free system
Part 2: Design your genetic parts
Vector context- Backbone appropriate to host
- Part name: Signal regulatory region
- Why? This part is a Regulatory DNA element that will respond to an external signal thus is a Signal-responsive regulator
Transcription start - Promoter (+ enhancer / intron if used)
- Part name: Short HsEef1a1 promoter
- From Kernel part Repository: Asimov Public
- Why? Promotes DNA transcription in homo sapiens; Host: Mammalian cell-free system
Translation start - 5’ UTR with Kozak
- Part name: HBA1 5’UTR
- From Kernel part Repository: Mariana Diaz Luna_William&Mary
- Why? Provides correct initiation of the translation.
Targeting - Signal peptide (secretion), tag
- Part name: Signal peptide (sig-peptide)
- From Homosapiens IL-4 gene from GenBank
- Why? Tags the proteins in the Golgi apparatus to signal that proteins will be placed in vesicles for secretion.
Coding sequence - CDS (+start +stop)
- Part name: 13677 bp
- From Homo Sapien Gene sequence of IL-4 (8693 bp) from GenBank
- From Homo Sapien mRNA of IL-21 (3137 bp)from GenBank
- From Homo Sapien Gene sequence of CD40-Ligand mRNA (1852 bp) from GenBank:
- Why? Provides the mRNA sequences of IL-4, IL-21 and CD40-Ligand each with appropriate start and stop codon for translation.
Transcription stop
Terminator + Poly(A) signal
- Part name: HBB 3’UTR
- From Kernel part Repository: Mariana Diaz Luna_William&Mary
- Part name: Poly(A) tail
- From Kernel part Repository: Mariana Diaz Luna_William&Mary
- Why for both transcription stop parts? Assures the termination of the translation.
Part 3: Assemble your construct
Figure 1 above
This construct is a mammalian cell-free system plasmid providing the substrates for the LF-OOAC organ-on-a-chip maintenance such as cytokines (IL-4 and IL-21) and costimulating CD40-Ligand that are normally produced in Follicular T helper cells (Tfh cells) as part of the HOPE factory workflow.
Part 4: Optimize and quality-check
- Optimization of Codons:
portion of IL-4 without stop codons in sequence Michellie Hernandez
ATC,GTT,AGC,TTC,TCC,ACT,AAT,TGC,CTC,ACA,TTG,TCA,CTG,CAA,ATC,GAC,ACC,TAT,TGG,GTC,TCA,CCT,CCC,AAC,TGC,TTC,CCC,CTC,TGT,TCT,TCC,TGC,CAT,GTG,CCG,GCA,ACT,TTG,TCC,ACG,GAC,ACA,AGT,GCG,ATA,TCA,CCT,TAC,AGG,AGA,TCA,TCA,AAA,CTT,ACA,GCC,TCA,CAG,AGC,AGA,AGG,GTA,CCT,ATC,TGG,CAC,CAT,CTC,TCC,AGA,TGT,TCT,GGT,GAT,GCT,CTC,AGT,ATT,TCT,AGG,CAT,GAA,AAC,GTT,AAC,AGC,TGC,AGA,AGT,TGG,AAC,TGG,TGG,TTG,GTG,GCA,GTC,CAG,GGC,ACA,CAG,CGA,GGC,TTC,TCC,CCT,GCC,ACT,CTT,TTT,TCT,GAG,GGT,TTG,GAA,GTT,TCC,TCA,GTT,GGA,GGG,AGT,GAG,AGC,TGC,TCA,TCA,AGG,ACT,TCT,CTG,TCC,GGT,TGG,AGG,TTA,ACT,CTG,TCT,CTT,GCT,CTC,TCA,TTT,CTG,CCT,GGA,CCA,AGA,CTC,TGT,GCA,CCG,AGT,CCG,CAG,ACA,TCT,TTG,CTG,CCT,CCA,AGG,GAA,GCC,GTC,CCA,CGG,TCT,GTT,TTA,GCA,AAT,GGG,GAG,ATC,CAT,CCC,CAA,ATG,TCT,GAA,CAA,GAA,ACT,TGT,CTA,ATG,GAA,AAC,GAG,CGG,GCC,CAA,ATT,AAC,TCT,AAG,GTG,TTA,GAT,GTT,TTC,AAA,GAA,CGA,GAA,GTC,TCT,TTA,CTC,TTA,AGC,ATG,TTT,TGG,TCT,TTC,TGG,TTT,CAC,TTG,ATT,AAG,ACA,TGT,AAT,AGA,AAG,CTT,ACA,TGC,TGT,AGT,CCT,GAC,TCA,GAT,CCT,GGT,CAA,AGA,AAA,GCC,CTC,TTG,GGT,TTT,ACT,CTT,TGG,CAT,AGT,GCC,TGG,AAC,GTA,GGA,GGC,ACT,CAA,ATG,CCT,TGT,ATG,AGA,GAA,TTT,TTC,TGG,CCC,ATA,CAT,TTC,AAA,ACC,AAA,TAC,TCT,CAC,AGA,AAC,AGA,TAT,GAT,GAC,AGG,TTG,AGG,GAG,CTT,TCA,TTT,TGT,CTA,AGA,GAC,TTC,CTA,TGG,CAA,CAG,AAA,AGG,TAT,CGC,CAG,AGC,CCC,TCC,TCT,TCC,ACA,GCC,TGG,CCA,CCT,AAC,AGC,CCT,CTG,GGT,TCC,GGG,GCT,GGC,CGT,CCA,GAG,CTC,CTC,AGC,TTG,CTC,TGG,CCG,GCC,GAA,CTC,CCC,TCC,AGC,TCG,GTC,TGG,AAC,CAT,CCT,GCT,GGG,CAG,CGT,CCA,GCA,CAT,CCC,TGC,TTC,GGG,CTG,CCT,GGG,CAC,CTC,GCC,TCT,CTG,CCT,CCT,GTG,CTG,CCT,CAC,CCC,CAC,CCC,TCT,ATC,TGT,AGT,GGG,AGA,GAG,ATA,GAT,TTG,ACA,GCT,GAT,AGT,GCA,TTT,TCT,CTG,ACA,AAC,ACA,CTA,CAG,CCG,TAT,CAA,TTT,TGT,GCA,TTT,CAG,TTC,CTG,TTT,TCA,TGG,AAA,CAC,ACG,GCT,GAG,AAT,GAA,AGC,CCA,AAG,CCT,CAA,TTT,CAC,AGT,GGT,CTC,CTA,ACT,ACC,TGC,TTT,CCA,TGC,AAA,CTA,GGG,AGA,TAT,GGC,CAG,GAG,AGC,CCT,GTG,TGT,TGG,GCA,GGG,TCA,CAC,TCC,AGC,ACC,CAG,ACC,ATA,GAA,CAG,GGC,CCA,TCC,TGC,TTC,ATG,AGG,GAA,ACT,GCT,CTT,CGG,GCC,TTT,AGC,TGG,ACT,ATC,TCA,TTT,CAT,TTA,TCC,CGG,GAG,TCC,GAT,ACA,GGA,GAT,TCT,GAA,GGG,CAA,ATT,ACA,CAC,TTT,TTT,TTT,TTT,TTG,AGA,GGT,CTT,GTT,CTG,TCA,CCC,AGG,CTG,GAG,TGC,AGT,GGT,GCG,ATT,TCA,GCT,CAT,AGC,AGC,CTC,CAC,CTC,CCA,GGC,TCA,AGC,TAT,CTT,CCT,ACC,TCA,GCC,TCC,CAA,GTA,GCC,GGG,ACG,ACA,GGT,GTG,CAC,CAC,CAC,GCC,TGG,CTA,ATT,TTT,GTA,TTT,TTT,TTG,AGA,TGG,AGT,CTT,GCC,ATT,TTG,CCC,AGG,CTT,GTC,TCG,AAC,TTC,TGG,GCT,CAA,GCA,ATC,CGT,CCA,CCT,CGG,CCT,CTC,AAA,GTG,CTG,GGA,TTA,GCC,ACT,GCA,CCT,GGG,CAA,CAG,TTT,ATG,TGT,GTG,TGT,GTG,TGT,GTG,TGT,GTG,TAT,ATA,TGT,GTG,TGT,ATA,TAT,ATG,TGT,GTA,TGT,ATA,TAT,GTG,TGT,GTG,TGT,GTG,TGT,GTG,TGT,GTA,AAT,CTC,CAA,GTC,CAT,CCA,ACC,GAG,AGG,CTC,CTA,CTA,GAA,GCC,AAG,AGT,CCA,CCG,GGT,GCA,CGG,GTC,TCT,GGA,GGC,CTG,TGG,CAC,TGC,GAA,GGC,TCT,AAC,AAA,GCC,AAG,GGA,AGG,GCC,ACC,TCA,CTA,GAA,GCC,AGG,CCT,GGA,GGA,AGG,GTG,AGG,GCT,GAG,GGC,CTG,GAG,GTA,AGA,CTG,CCT,GTG,GTT,TTA,GAC,CCA,GCT,CTG,CCA,CTG,ACT,AGC,TGT,GTG,GCT,GGC,CTT,CAG,CAC,ATC,TTC,ACA,CCT,CTC,TGC,ACC,TCA,GTT,TCC,ACA,TGT,GAA,GAT,ATG,AAA,GTG,ATT,CTG,AAG,GTG,ATT,GCA,AGG,TTG,ATT,GGA,ATC,CAG,CTC,TTG,AGT,TGC,AAA,GTG,TTA,TTG,GAT,GAT,ATA,ACC,ACG,ATT,AAA,AGC,AAG,AAC,AGG,TGC,AGA,GAA,GCG,TTC,GAA,GGA,GGG,GAC,CGG,GTT,GGA,AAG,GAT,CAA,ACC,TCC,AGG,ATG,CCG,AGT,CTG,GGG,CAA,TCC,ATC,TGG,GCT,GTT,TCT,GGA,AGA,CCC,CGG,GTT,GCA,GGC,CAG,GAC,ACT,GCT,GCC,CTC,CCG,TCC,TTA,ACT,CCC,CTC,TTC,ACT,CAG,TCC,TCA,CTC,ACC,TCC,CTC,TCA,CAC,ACA,CAA,CAT,CTC,CTA,GAA,TCC,CCA,CTG,CCT,GCT,TCA,CTC,TTA,CCC,GTC,TCA,TTT,GCC,TCC,CCT,GAA,CTT,CAT,CCT,CCT,GGA,GTT,CAC,GAT,CTC,ACT,CTT,CAC,TCT,TTT,CTT,CCC,CTC,GAA,GAT,TCA,GCA,CTG,CTT,ACT,TAC,ATG,TTA,AGA,TAT,TTC,AGA,ACA,GTG,AAA,TGT,TGC,TAT,TTT,CAA,AAA,CCT,ACA,AAG,GTG,GTA,TGC,AGA,GGA,AAA,GGT,ACT,TCT,TTG,TGT,TCC,CAA,AGA,AAA,CAT,CTT,TCC,AAA,ATC,CAG,CCT,ATT,GAT,TTT,ATT,TCT,TCG,GGG,GAA,CAA,GAA,TTT,TAT,CTC,GTT,GGG,CAT,TCT,ACT,CTT,GGC,AGT,TGC,TGG,AAG,AAG,GCA,CTG,GTC,GTC,CTG,GGC,TTC,ACA,GGT,AAC,ACC,TGT,CAG,GGT,GTC,TAT,GAA,GTC,AAG,GCT,GTC,GGA,ACA,GCA,AAG,TGG,GAA,GAA,GCA,GCT,GGC,TGG,CTG,ATG,AAG,GGT,TTC,TTG,GGT,GGA,CAA,GTA,GTT,GGA,GCT,ATT,TCC,TAT,TTA,CCA,AAG,AGA,GCT,AAA,GTT,CAT,AAT,TCT,ACA,GAG,AGT,TCC,ATA,ATG,AAC,CTC,AAA,TAC,CTC,TGT,TTT,TTG,AAG,GAG,TTT,CTC,ATA,TAC,AGC,ACT,AGC,CTA,TCC,TGG,GCA,GGA,TGG,GAG,ATG,ATG,CAG,TGC,CAA,TCG,GGC,TGG,ATT,TAT,ATG,GTC,CTC,AGT,GAG,GCT,GGT,CAA,GAA,CCG,AGT,AAC,TCT,CAC,AGA,GTC,ACT,GCC,ACA,GAA,GAA,ATC,TCC,CAA,GTG,GCT,GTT,TCC,CAT,TCC,CGG,GAG,GGA,CAG,GCC,TCC,TTC,GTC,ACT,CCC,GCA,GTT,CTG,AAC,TGT,GAG,GTC,AGC,CAG,GCT,GTC,CAA,GTG,CAC,TCC,CTG,AGC,CAC,TGG,CAG,ACA,CAC,TCA,GCA,GCC,AGA,GCT,AGA,CAG,GCA,GGT,GGT,AGG,AGT,CCA,GGG,CCA,CGG,CAG,GGA,TGG,AGT,GTC,GCC,CCC,CTC,GCT,GCG,ATA,CCA,GAG,CAA,CTA,AAA,CGT,GGC,CTT,GCA,CTA,AAG,CTG,CCC,CTA,GGA,TGC,ATT,CTT,TTA,AAG,TTT,TCC,ATT,TGC,AGA,CTC,TTT,TCA,ATT,CTT,ATT,TTA,TCC,TTG,TTT,CCT,TTA,GAA,AGT,CCT,TTC,AAA,AAT,ATC,TTT,AGA,GGG,TTT,TTC,CAT,ACT,ATG,TGG,CCA,TAT,ACG,GGT,CAA,AAT,GTT,TTT,CCA,GGC,TCC,AAG,CCA,GCG,TTT,CAG,AAA,ATC,TCA,CCA,AGG,TTG,TGG,AGA,AGC,AAA,GGC,CTT,TTT,GGT,TTC,CTT,GAA,TCT,CAC,TGT,TCC,CTC,TGC,AGC,AGC,ATG,CAT,GTC,TGC,CCA,CCT,CCA,GAC,ACA,CAG,GCA,CCA,TCT,GCC
- Search your sequence
AAC,ACA,ACT,GAG,AAG,GAA,ACC,TTC,TGC,AGG,GCT,GCG,ACT,GTG,CTC,CGG,CAG,TTC,TAC,AGC,CAC,CAT,GAG,AAG,GAC,ACT,CGC,TGC,CTG,GGT,GCG,ACT,GCA,CAG,CAG,TTC,CAC,AGG,CAC,AAG,CAG,CTG,ATC,CGA,TTC,CTG,AAA,CGG,CTC,GAC,AGG,AAC,CTC,TGG,GGC,CTG,GCG,GGC,TTG
Part 5: From design to build
How would you build it?
I would suggest building it from a pool of oligos synthesized on a chip due to the length and complexity of reducing the amount of error during DNA synthesis.
What does synthesis constrain?
Synthesis has error rates thus the amount of errors in the synthesis would have to be reduced drastically and multiple mRNA sequences and its corresponding signalling tag might require the introduction of a plasmid per each substrate instead of a combination plasmid with all substrates in one plasmid.
Prototype vs. scale.
The microfluidic automation in the HOPE factory workflow will measure the substrate concentration in the LF-OOAC and send the external signal that are detected by the vector context providing the signaling needed to enhance the substrate production. The plasmid itself will not need to be replicated, as only transcription and translation will occur upon stimulation from the automation.
Part 6: What don’t you know yet?
2–3 biggest unknowns or risks in your construct:
- Vector context (Signal Regulatory region) is from Homo sapiens BRCA1 intron 2 regulatory region thus from a totally different gene from the genes of IL-4, IL-21 or CD40-Ligand. Thus it is unknown if it will function as detailed.
- The signal peptide is from IL-4 gene not found in IL-21 nor CD40-Ligand. Thus it is unknown if it will function as detailed.
- Although the simulator shows successful transcription of mRNA, I do not know if the Tfh cell simulator plasmid will successfully produce IL-4, IL-21 and CD40-Ligand when prompted to do so externally via automation.
- Another unknown are the post translational modification issues that may arise during translation, since this is a common issue even in chassis of mammalian cells thus one must suspect the same issue in mammalian cell-free systems as well.
AI Prompts
Kernel AI
How to extract mRNA sequence from the gene from GenBank
From repository Tfh_Michellie_Hernandez how can I extract mRNA
how to add parts to a sequence
5’URL in public repository
Parts of the IL-4 gene in humans
which parts in a plasmid sequence are needed to stimulate the transcription and translation of mRNA for IL-4
How to optimize the codons
AAC,ACA,ACT,GAG,AAG,GAA,ACC,TTC,TGC,AGG,GCT,GCG,ACT,GTG,CTC,CGG,CAG,TTC,TAC,AGC,CAC,CAT,GAG,AAG,GAC,ACT,CGC,TGC,CTG,GGT,GCG,ACT,GCA,CAG,CAG,TTC,CAC,AGG,CAC,AAG,CAG,CTG,ATC,CGA,TTC,CTG,AAA,CGG,CTC,GAC,AGG,AAC,CTC,TGG,GGC,CTG,GCG,GGC,TTG search repositories for similar DNA sequences
OpenAI ChatGPT 5
Do Natural bacterial plasmids all start with 5’UTR and end with 3’UTR and a Poly A tail
What is the plasmid architecture in cell-free systems
How can I stimulate a mammalian cell-free system DNA architecture?
What is a Vector context backbone in the construct of a plasmid
What vector context can be used in cell-free system plasmid that needs to be enhanced by exterior signaling
What Signal peptide (secretion), tag are available in cell-free systems
Week 3 HW: Cell-Free Protocols
Author: Dr. Michellie Hernandez, MD
Assisted by ChatGPT for tutorial and broaden understanding.
Content ideation and creation was done without AI and solely done by Dr. Michellie Hernandez
Release date: October 5, 2026
A. COMPARE CELL-FREE REACTION COMPOSITIONS
Why does the concentration of potassium glutamate and magnesium glutamate differ between compositions?
Potassium Glutamate
PanOx system: 200 mM
PanOx SP system: 175 mM
Cytomim system: 130 mM
Magnesium Glutamate
PanOx system: 0
PanOx SP system: 20 mM
Cytomim system: 8 mM
Which is more sensitive?
Maximum protein synthesis yield decreases at higher or lower concentrations from those listed above on each of the systems.
“Glutamate is the principal anion produced as a result of osmotic stress in E. coli, and it has been shown that protein – nucleic acid interactions are more favorable in glutamate than in acetate salt solutions (Record et al., 1998b, Jewett et. al 2004)”
Protein synthesis yield varies by concentrations of potassium and magnesium per system.
Explain how a PEP+NTP reaction formulation is different from a Ribose+NMP reaction formulation.
PEP+NTP reaction formulation utilizes PEP as the energy reagent with NTP reagents. Ribose+NMP reaction formulation utilizes ribose as the energy source reagent and utilizes NMP reagents. Per Smith et. al (2026), GMP can replace GTP and AMP can replace ATP without a loss in protein titer. However CMP cannot replace CTP nor UMP replace UTP without a change in titer. Thus partial replacement of NTP by NMP can yield a more cost effective reaction formulation. Hence I propose a reaction formulation of a combination of NTP and NMP; specifically containing GMP, AMP, CTP and UTP reagents.
“In concurrence with Olsen et al.,42 GPT-5 concluded that use of NTPs is not a
cost-effective route to high protein titers and that NMPs yield a better specific cost. The
model also noted that NMPs cannot be fully replaced by the corresponding nucleosides or
bases, but partial replacement does reduce costs while preserving titers. Again, this
finding agrees with Olsen et al., who note that GMP may be replaced by guanine and
ribose without loss of titer, a result we confirmed (Tables 1 and 2, reaction compositions
177786377 and 179366574). GPT-5 also found that AMP may be partially replaced by
adenosine (Tables 1 and 2, reaction composition 1793122_35). In Step 5, reactions with
adenosine had a median titer of 0.279 (95% confidence interval 0.273–0.285) and those
without had a median titer of 0.138 (95% confidence interval 0.136–0.139). However,
attempts to replace CMP and UMP with their pyrimidine nucleoside counterparts failed.
(Smith et. al 2026).”
Choose two cell-free reaction compositions from the literature, compare and contrast the choice of reagents and the concentration of reagents.
Cytomim system is a cell free system that allows pyruvate as an energy source and closely resembles the E. coli natural cytoplasm environment (Jewett et. al 2004). The cytomim does so by synthesizing chloramphenicol acetyltransferase (CAT) and removing PEG, pH buffers and polyethylene glycol from the PANOx system. Acetate accumulates in the Cytomim system. Optimal pH for CAT production is 6.8. Cytomim has a 5-fold yield increase of CAT production in comparison to the PANOx system, but has the same yield as the PANOx-SP system. Unnatural reagents that are useful: oxalic acid. Genetically engineered E. coli removing the pps gene will reduce the need for oxalic acid (Jewett et. al 2004). I propose that the E. coli lysate be obtained from genetically engineered E. coli that has removed the pps gene from the E. coli genome. I propose we then reevaluate the yield of the Cytomim system in this genetically modified E. coli lysate. Concentration of reagents: 1.2 mM adenosine triphosphate (ATP); 0.85 mM each of GTP, UTP, and CTP; 34 Ag/mL folinic acid; 170.6 Ag/mL of E. coli tRNA mix-
ture; 13.3 Ag/mL plasmid; 100 Ag/mL T7 RNA polymerase; 5 AM L-[U-14C]-leucine; 2 mM each of 20 unlabeled amino acids; 0.33 mM nicotinamide adenine dinucleotide (NAD); 0.26 mM coenzyme-A (CoA); and 0.24 volume of S30 extract.
PANOx energy regeneration system (Kim et. al 2001) utilizes phosphoenolpyruvate (PEP) as the energy source, but the accumulation of phosphate and acidic environment hinders protein synthesis.
“In the PANOx energy regeneration system, 57 mM HEPES – KOH (pH 7.5), 2% (w/v) polyethylene-glycol 8000 (PEG), 200 mM potassium glutamate, 80 mM ammonium acetate, 16 mM magnesium acetate, 33 mM PEP, and 2.7 mM sodium oxalate were added. In the PANOx-SP system, 175 mM potassium glutamate, 10 mM ammonium glutamate, 20 mM magnesium glutamate, 33 mM PEP, 1.5 mM spermidine, 1 mM putrescine, and 2.7 mM sodium oxalate were added. Solutes added to the Cytomim system included 130 mM potassium glutamate, 10 mM ammonium glutamate, 8 mM magnesium glutamate, 33 mM sodium pyruvate, 1.5 mM spermidine, 1 mM putrescine, and 4 mM sodium oxalate. There was approximately an additional 3.3 mM magnesium, 14.4 mM potassium, 2.4 mM Tris, and 23.5 mM acetate in each reaction originating from the cell extract (Jewett et. al 2004).”
B. DESIGN ONE REACTION COMPOSITION FOR EACH OF THE THREE PROTEINS
Concentration Rank of sfGFP Composition:
- Potassium Glutamate 279.84 mM
- HEPES-KOH pH 7.5 57.50 mM
- Ammonium Glutamate 10.00 mM
- Magnesium Glutamate 8.22 mM
- Potassium phosphate 1.6:1
monobasic:dibasic (molar ratio) 5.00 mM
- 17 Amino Acid Mix 2.81 mM
- Glucose 2.78 mM
- Spermidine 1.56 mM
- Putrescine Dihydrochloride 1.25 mM
- NAD 375.00 uM
- Coenzyme A 250.00 uM
- AMP 125.00 uM
- CMP 125.00 uM
- GMP 125.00 uM
- UMP 125.00 uM
- Adenosine 62.50 uM
- Cytidine 62.50 uM
- Guanosine 31.25 uM
- Uridine 31.25 uM
- Folinic Acid 0.025 g/L
- Water 6.575 uL|
Reagent supplement JSON of sfGFP:
{
“sample_id”: “michellie_hernandez_2026-10-04_0aqv1h”,
“sample_type”: “experimental”,
“reagent_list”: {
“adenosine”: 50,
“amino_acid_mix_17”: 725,
“ammonium_glutamate”: 200,
“amp”: 25,
“base_buffer”: 2000,
“cmp”: 25,
“coa”: 100,
“cytidine”: 50,
“folinic_acid”: 50,
“glucose”: 50,
“gmp”: 25,
“guanosine”: 25,
“hepes_koh”: 550,
“kpo_monobasic_mix”: 200,
“lysate”: 5000,
“magnesium_glutamate”: 225,
“nad”: 75,
“nuclease_free_water”: 6575,
“potassium_glutamate”: 1825,
“putrescine”: 50,
“spermidine”: 125,
“template_sfgfp”: 2000,
“ump”: 25,
“uridine”: 25
},
“metadata”: {
“ai_model”: “”,
“protein_target”: “sfGFP”,
“acknowledgements”: “”,
“comments”: ""
}
} |
Composition PETase
Concentration Rank of PETase composition
- HEPES-KOH pH 7.5 57.50 mM
- Magnesium Glutamate 10.10 mM
- Ammonium Glutamate 10.00 mM
- Glucose 5.55 mM
- Potassium Oxalate 4.38 mM
- Oxidized Glutathione (GSSG) 4.04 mM
- 17 Amino Acid Mix 2.00 mM
- Spermidine 1.56 mM
- ATP 1.25 mM
- Putrescine Dihydrochloride 1.25 mM
- PEP mono (0.1M stock) 1.00 mM
- CTP 875.00 uM
- GTP 875.00 uM
- UTP 875.00 uM
- NAD 375.00 uM
- Coenzyme A 250.00 uM
- DsbC (E. coli, 25.5 kDa) 1.13 uM
- Folinic Acid 0.025 g/L
- Water 7.200 uL
Reagent supplement JSON of PETase composition
{
“sample_id”: “michellie_hernandez_2026-10-05_scv2ia”,
“sample_type”: “experimental”,
“reagent_list”: {
“amino_acid_mix_17”: 400,
“ammonium_glutamate”: 200,
“atp”: 250,
“base_buffer”: 2000,
“coa”: 100,
“ctp”: 175,
“dsbc_ecoli”: 225,
“folinic_acid”: 50,
“glucose”: 100,
“gtp”: 175,
“hepes_koh”: 550,
“lysate”: 5000,
“magnesium_glutamate”: 300,
“nad”: 75,
“nuclease_free_water”: 7200,
“oxidized_glutathione”: 475,
“pep_mono”: 200,
“potassium_oxalate”: 175,
“putrescine”: 50,
“spermidine”: 125,
“template_petase”: 2000,
“utp”: 175
},
“metadata”: {
“ai_model”: “”,
“protein_target”: “PETase”,
“acknowledgements”: “”,
“comments”: ""
}
}
Composition Reteplase:
Concentration Rank of Reteplase Composition
- HEPES-KOH pH 7.5 57.50 mM
- Ammonium Glutamate 10.00 mM
- Magnesium Glutamate 8.22 mM
- Glucose 5.55 mM
- Oxidized Glutathione (GSSG) 4.04 mM
- Potassium Oxalate 3.75 mM
- 17 Amino Acid Mix 2.00 mM
- Spermidine 1.56 mM
- ATP 1.25 mM
- Putrescine Dihydrochloride 1.25 mM
- CTP 875.00 uM
- GTP 875.00 uM
- UTP 875.00 uM
- NAD 375.00 uM
- Coenzyme A 250.00 uM
- DsbC (E. coli, 25.5 kDa) 5.00 uM
- Folinic Acid 0.025 g/L
- Water 6.725 uL
Reagent supplement JSON of Reteplase Composition
{
“sample_id”: “michellie_hernandez_2026-10-04_lm3vpf”,
“sample_type”: “experimental”,
“reagent_list”: {
“amino_acid_mix_17”: 400,
“ammonium_glutamate”: 200,
“atp”: 250,
“base_buffer”: 2000,
“coa”: 100,
“ctp”: 175,
“dsbc_ecoli”: 1000,
“folinic_acid”: 50,
“glucose”: 100,
“gtp”: 175,
“hepes_koh”: 550,
“lysate”: 5000,
“magnesium_glutamate”: 225,
“nad”: 75,
“nuclease_free_water”: 6725,
“oxidized_glutathione”: 475,
“potassium_oxalate”: 150,
“putrescine”: 50,
“spermidine”: 125,
“template_reteplase”: 2000,
“utp”: 175
},
“metadata”: {
“ai_model”: “”,
“protein_target”: “Reteplase”,
“acknowledgements”: “”,
“comments”: ""
}
}
▸Concentration Rank of Reteplase Composition
⧉
- Potassium Glutamate 200.00 mM
- HEPES-KOH pH 7.5 57.50 mM
- Magnesium Glutamate 20.10 mM
- Ammonium Glutamate 10.00 mM
- Cysteine 5.00 mM
- Oxidized Glutathione (GSSG) 4.46 mM
- 17 Amino Acid Mix 4.00 mM
- Potassium Oxalate 3.75 mM
- Spermidine 1.56 mM
- AMP 1.50 mM
- GMP 1.50 mM
- Putrescine Dihydrochloride 1.25 mM
- Tyrosine pH 12 1.00 mM
- CTP 875.00 uM
- UTP 875.00 uM
- Folinic Acid 52.81 uM
- DsbC (E. coli, 25.5 kDa) 1.00 uM
- Ribose 3.125 g/L
- Glucose 1.000 g/L
- CMP 0.0 nM
- UMP 0.0 nM
- Guanine 0.0 nM
- Potassium phosphate dibasic 0.0 nM
- Potassium phosphate monobasic 0.0 nM
- Nicotinamide 0.0 nM
- Water 5.175 uL
Reagent supplement JSON of Reteplase Composition
{
“sample_id”: “michellie_hernandez_2026-10-06_gt4yxb”,
“sample_type”: “experimental”,
“reagent_list”: {
“amino_acid_mix_17”: 1200,
“ammonium_glutamate”: 200,
“amp”: 300,
“base_buffer”: 2000,
“ctp”: 175,
“cysteine”: 400,
“dsbc_ecoli”: 200,
“folinic_acid”: 50,
“glucose”: 100,
“gmp”: 300,
“hepes_koh”: 550,
“lysate”: 5000,
“magnesium_glutamate”: 700,
“nuclease_free_water”: 5175,
“oxidized_glutathione”: 525,
“potassium_oxalate”: 150,
“putrescine”: 50,
“ribose”: 625,
“spermidine”: 125,
“template_reteplase”: 2000,
“utp”: 175
},
“metadata”: {
“ai_model”: “”,
“protein_target”: “Reteplase”,
“acknowledgements”: “”,
“comments”: ""
}
}
Comment
– Substituted ATP for AMP and GTP for GMP, but maintained CTP and UTP due to results in Smith et. al 2026. Thus last composition combines ribose NMP with partial NTP to include in the composition: AMP, GMP, CTP and UTP. Composition was adjusted to reflect in sfGFP, Petase and Reteplase in the coopetition platform.
Suggestion of new reagents to add to the cell-free reagent inventory
Template GSSG Redox category
Reasoning: To experiment determine whether or not the hypothesis of adding a template of enzymatic reagents may reduce costs. We can test with another enzymatic reagent that is not already added to the lysate, as DsbC is already in the repletase lysate. We can add to the GSSG template a tag and regulatory enzymes to the plasmid so that translation the GSSG only occurs when needed, instead of producing GSSG constantly. The regulatory gene ideally will respond to an external additive that is added to the well or in response to measurements of GSSG within the well.
C. ASIMOV KERNAL (OPTIONAL)
Build T7 compatible plasmids for cell-free expression using the amino acid coding sequences below:
- Use of AI
You are encouraged to use AI and online tools for this assignment to assist your reagent composition development process. As you will present your work during recitation, you must be able to explain and justify the reasoning behind your design.
If you use AI, ensure that you cite the AI client and your prompts used.
Rosalind AI in HGTAA
– Rosalind, How does glutamate affect the protein synthesis yield?
– Roselind, how does glutamate affect the protein synthesis yield?
– Whether leftover metabolites accumulate in cell-free system from the use of NMP that can inhibit protein synthesis
– Whether AMP use makes the formulation cheaper.
– Whether ribose in the Ribose+NMP formulation serves as the energy source, and which paper covers the formulation.
– Whether the cost-effective formulation must be a combination of GMP, AMP, CTP, and UTP?
Chat GPT
– Review oxidation reduction reaction inside the Endoplasmic reticulum and Golgi apparatus
– What makes the interior of the Golgi apparatus more oxidizing than the cytosol? What chemicals or molecules allow a more oxidizing environment?
– What must a E. coli H7 plasmid contain so that the E. coli can translate a mammalian protein?
– Does a cds do the function of ATG and stop codon?
– Which organism produces petase naturally?
– Which organism produces sfGFP naturally?
– Which organism produces reteplase naturally?
- REFERENCES AND RESOURCES
Smith, A. A., et al. (2026). Using a GPT-5-driven autonomous lab to optimize the cost and titer of cell-free protein synthesis (Ginkgo Bioworks × OpenAI). bioRxiv preprint. DOI: 10.64898/2026.02.05.703998. https://www.biorxiv.org/content/10.64898/2026.02.05.703998v1
Olsen, M. L., Copeland, C. E., Sundberg, C. A., Aw, R., Shaver, Z. M., Rao, G., Swartz, J. R., Karim, A. S., &
Jewett, M. C. (2026). Design-driven optimization of low-cost reagent formulations for reproducible and high-yielding cell-free gene expression. Nature Communications. DOI: 10.1038/s41467-026-69605-8.
Jewett, M. C., & Swartz, J. R. (2004). Mimicking the Escherichia coli cytoplasmic environment activates long-lived and efficient cell-free protein synthesis. Biotechnology and Bioengineering, 86(1), 19–26. DOI: 10.1002/bit.20026.
Silverman, A. D., Karim, A. S., & Jewett, M. C. (2020). Cell-free gene expression: an expanded repertoire of applications. Nature Reviews Genetics, 21, 151–170. DOI: 10.1038/s41576-019-0186-3.
Rasor, B. J., Vögeli, B., Landwehr, G. M., Bogart, J. W., 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.
