Week 3 HW: Cell-Free Reaction Design

Zia Baig | HTBAA 2026 | October 6, 2026

A. Comparing reaction components

Potassium glutamate helps maintain the ionic conditions needed for protein synthesis. Magnesium glutamate supports ribosomes and many enzymes, but nucleotides and phosphate bind magnesium and change how much remains available. Magnesium is therefore the more sensitive variable: small changes can sharply reduce expression, while potassium usually has a broader useful range (Olsen et al., 2026).

A PEP/NTP formulation supplies nucleotide triphosphates for transcription and uses PEP to regenerate ATP. It is well established, but its inputs are costly and phosphate can accumulate. A ribose/NMP formulation relies on the lysate to generate ATP and convert monophosphates into triphosphates. This can lower reagent costs, but performance depends more on lysate metabolism and oxygen transfer (Olsen et al., 2026).

FeaturePANOx SP (Jewett & Swartz, 2004)RFopt (Olsen et al., 2026)
Energy / RNAPEP 30 mM; ATP 1.25 mM; other NTPs 0.875 mM eachRibose 50 mM; AMP 3 mM; other NMPs 2.15 mM each
K / Mg glutamate130 / ~8 mM362 / ~8 mM
Amino acids2 mM each5 mM each
Other inputsAmmonium glutamate, oxalate, polyamines, NAD, CoA, folinic acidHEPES 75 mM; phosphate 15 mM; glucose 10 mM; nicotinamide 4 mM
Main tradeoffEstablished PEP system with costly inputsLower reagent cost, but greater dependence on lysate metabolism and oxygen

B. Proposed 20 Β΅L reactions

The table shows one reaction for each target. All volumes are in nL, in the designer’s 25 nL increments, and each reaction totals 20,000 nL. Each uses the matching DNA template. The fixed buffer contributes 200 mM potassium glutamate, 2.6 mM magnesium glutamate, 30 mM HEPES, and 1 mM each of the amino acid mix, tyrosine, and cysteine. All listed reagents were in stock on October 6, 2026 (Ginkgo Bioworks, 2026; HTBAA, 2026).

Reagent (nL)sfGFPPETaseReteplase
Base buffer2,0002,0002,000
Lysate5,0005,0005,000
Target DNA template2,0002,0002,000
17 amino acid mix1,600400400
Ammonium glutamateβ€”200200
AMP600β€”β€”
CMP / GMP / UMP425 eachβ€”β€”
ATPβ€”250250
CTP / GTP / UTPβ€”175 each175 each
Coenzyme Aβ€”100100
DsbCβ€”500800
Folinic acidβ€”5050
HEPES KOH pH 7.5900550750
Magnesium glutamate225300225
NADβ€”7575
Nicotinamide800β€”β€”
Oxidized glutathione GSSGβ€”β€”475
Reduced glutathione GSHβ€”β€”200
PEPβ€”6,0006,000
Potassium glutamate3,325β€”β€”
Potassium oxalateβ€”150150
Potassium phosphate mix600β€”β€”
Putrescineβ€”5050
Ribose1,500β€”β€”
Spermidine50125125
Nuclease free water1251,725625
Total20,00020,00020,000

sfGFP. I would add 0.625 mM spermidine to the site’s ribose/NMP reference recipe and reduce the water by 50 nL. This changes one ingredient, so I could compare sfGFP fluorescence and cost per gram with the original recipe (Ginkgo Bioworks, 2026; Olsen et al., 2026).

PETase. I would use standard lysate and reduce DsbC from 5 to 2.5 Β΅M, replacing the removed 500 nL with water. At the listed stock price, this saves $2.816 per reaction. I would check whether PETase activity remains high enough to improve activity per dollar (Ginkgo Bioworks, 2026; HTBAA, 2026).

Reteplase. I would keep the oxidizing DSB lysate and GSSG/GSH mixture, raise HEPES from 57.5 to 67.5 mM, and reduce DsbC from 5 to 4 Β΅M. The lower DsbC amount saves about $1.126 per reaction. I would use IPR-AMC cleavage to test whether the reaction maintains activity (Ginkgo Bioworks, 2026; HTBAA, 2026).

I would compare each design with its unchanged reference and a no-DNA control, then repeat any promising result. The PETase and Reteplase assays report activity, not protein mass, so they cannot establish cost per gram on their own (HTBAA, 2026).

Community reagent: FkpA

FkpA is my choice because it may help fold disulfide-rich Reteplase. In an E. coli cell-free system, FkpA and DsbC improved antibody folding together (Groff et al., 2014). Its effect on Reteplase activity and cost remains to be measured.

References

Ginkgo Bioworks. (2026). CFPS coopetition overview and guidelines [Project specification]. https://docs.google.com/document/d/1E9RKXezEU5pMO5A10kkUHjHrfL5f8segL0QeNHXAs_E/edit

Groff, D., Armstrong, S., Rivers, P. J., Zhang, J., Yang, J., Green, E., Rozzelle, J., Liang, S., Kittle, J. D., Steiner, A. R., Baliga, R., Thanos, C. D., Hallam, T. J., Sato, A. K., & Yam, A. Y. (2014). Engineering toward a bacterial β€˜endoplasmic reticulum’ for the rapid expression of immunoglobulin proteins. mAbs, 6(3), 671–678. https://doi.org/10.4161/mabs.28172

HTBAA. (2026). Cell free synthesis coopetition for humans and AI [Reaction designer]. https://coopetition.htgaa.org/cfps

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. https://doi.org/10.1002/bit.20026

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, 17, Article 3478. https://doi.org/10.1038/s41467-026-69605-8

OpenAI. (2026). Codex [Large language model]. https://openai.com/codex/