Computational Design and Biosecurity Assessment of a Dual-Function E. coli Circuit for S. aureus Sensing and Targeted Lysostaphin Therapy

Abdulwahid M. Dhahir; Duaa A. Marza; Khalil K. Jafar

doi:10.33102/mjosht.568

Authors

Abdulwahid M. Dhahir College of Medical and Health Technologies, Ahlulbait University, Karbala 56001, Iraq.
Duaa A. Marza College of Medical and Health Technologies, Ahlulbait University, Karbala 56001, Iraq.
Khalil K. Jafar Institute of Genetic Engineering and Biotechnology for Postgraduate Studies, University of Baghdad, Al-Jadriya Campus, 47024, Baghdad, Iraq.

DOI:

https://doi.org/10.33102/mjosht.568

Keywords:

synthetic biology, in silico risk assessment, dual-function genetic circuit, staphylococcus aureus, biosecurity

Abstract

Engineered therapeutic microbes represent a promising strategy to combat antibiotic-resistant pathogens such as Staphylococcus aureus; however, the absence of predictive biosafety frameworks remains a major barrier to their clinical translation. Here, we present an in silico design of a dual-function Escherichia coli genetic circuit integrating pathogen sensing via the AgrC/AgrA system, targeted lysostaphin-mediated killing, and a tightly regulated MazE/MazF kill switch for biocontainment. A hybrid computational framework was developed to systematically evaluate functional performance and biosafety risks, combining ordinary differential equations (ODEs) for deterministic dynamics with Gillespie-based stochastic simulations to capture intrinsic molecular noise. The model predicts robust and responsive bacteriolytic activity upon detection of S. aureus signaling molecules, while maintaining controlled system behavior under varying conditions. Additionally, critical thresholds for kill-switch leakiness were identified, and evolutionary stability was assessed through failure-time analysis. Collectively, this study provides a quantitative framework for integrating circuit design with biosafety evaluation, offering practical guidance for the development and regulatory assessment of engineered therapeutic microbes. However, these computational predictions require future experimental validation to confirm circuit efficacy and safety.

Downloads

Download data is not yet available.

References

[1] M. Kang, D. Choe, K. Kim, B.-K. Cho, and S. Cho, “Synthetic Biology Approaches in the Development of Engineered Therapeutic Microbes,” Int. J. Mol. Sci., vol. 21, no. 22, p. 8744, Nov. 2020, doi: 10.3390/ijms21228744.

[2] K. Jin, Y. Huang, H. Che, and Y. Wu, “Engineered Bacteria for Disease Diagnosis and Treatment Using Synthetic Biology,” Microb. Biotechnol., vol. 18, no. 1, p. e70080, Jan. 2025, doi: 10.1111/1751-7915.70080.

[3] T. H. Kim, B. K. Cho, and D.-H. Lee, “Synthetic Biology-Driven Microbial Therapeutics for Disease Treatment,” J. Microbiol. Biotechnol., vol. 34, no. 10, pp. 1947–1958, Oct. 2024, doi: 10.4014/jmb.2407.07004.

[4] J. Fuerte-Stone and M. Mimee, “Advances in biocontainment strategies of engineered microbes for use in humans,” Curr. Opin. Microbiol., vol. 91, p. 102727, Jun. 2026, doi: 10.1016/j.mib.2026.102727.

[5] S. Wu, J. Liu, C. Liu, A. Yang, and J. Qiao, “Quorum sensing for population-level control of bacteria and potential therapeutic applications,” Cell. Mol. Life Sci., vol. 77, no. 7, pp. 1319–1343, Apr. 2020, doi: 10.1007/s00018-019-03326-8.

[6] J. Jayakumar, V. A. Kumar, L. Biswas, and R. Biswas, “Therapeutic applications of lysostaphin against Staphylococcus aureus,” J. Appl. Microbiol., vol. 131, no. 3, pp. 1072–1082, Sep. 2021, doi: 10.1111/jam.14985.

[7] J. K. Kumar, “Lysostaphin: an antistaphylococcal agent,” Appl. Microbiol. Biotechnol., vol. 80, no. 4, pp. 555–561, Sep. 2008, doi: 10.1007/s00253-008-1579-y.

[8] A. Tripathi, P. C. Dewan, S. Ahmed, and R. Varadarajan, “MazF-induced Growth Inhibition and Persister Generation in Escherichia coli,” J. Biol. Chem., vol. 289, no. 7, pp. 4191–4205, Feb. 2014, doi: 10.1074/jbc.M113.510511.

[9] J. Cao, X. Qi, and H. Zhao, “Modeling Gene Regulation Networks Using Ordinary Differential Equations,” in Next Generation Microarray Bioinformatics, vol. 802, J. Wang, A. C. Tan, and T. Tian, Eds., in Methods in Molecular Biology, vol. 802, Totowa, NJ: Humana Press, 2012, pp. 185–197. doi: 10.1007/978-1-61779-400-1_12.

[10] D. D. Seaton, “ODE-Based Modeling of Complex Regulatory Circuits,” in Plant Gene Regulatory Networks, vol. 1629, K. Kaufmann and B. Mueller-Roeber, Eds., in Methods in Molecular Biology, vol. 1629, New York, NY: Springer New York, 2017, pp. 317–330. doi: 10.1007/978-1-4939-7125-1_20.

[11] X. Kang, B. Hajek, and Y. Hanzawa, “From graph topology to ODE models for gene regulatory networks,” PLOS ONE, vol. 15, no. 6, p. e0235070, Jun. 2020, doi: 10.1371/journal.pone.0235070.

[12] J. Li et al., “A stochastic model of Escherichia coli AI‐2 quorum signal circuit reveals alternative synthesis pathways,” Mol. Syst. Biol., vol. 2, no. 1, p. 67, Jan. 2006, doi: 10.1038/msb4100107.

[13] J. M. Frazier, Y. Chushak, and B. Foy, “Stochastic simulation and analysis of biomolecular reaction networks,” BMC Syst. Biol., vol. 3, no. 1, p. 64, Dec. 2009, doi: 10.1186/1752-0509-3-64.

[14] P. Lecca, “Stochastic chemical kinetics: A review of the modelling and simulation approaches,” Biophys. Rev., vol. 5, no. 4, pp. 323–345, Dec. 2013, doi: 10.1007/s12551-013-0122-2.

[15] N. Anesiadis, W. R. Cluett, and R. Mahadevan, “Model-driven design based on sensitivity analysis for a synthetic biology application,” in Computer Aided Chemical Engineering, vol. 29, Elsevier, 2011, pp. 1446–1450. doi: 10.1016/B978-0-444-54298-4.50068-4.

[16] G. De Carluccio, V. Fusco, and D. Di Bernardo, “Engineering a synthetic gene circuit for high-performance inducible expression in mammalian systems,” Nat. Commun., vol. 15, no. 1, p. 3311, Apr. 2024, doi: 10.1038/s41467-024-47592-y.

[17] F. Stirling et al., “Rational Design of Evolutionarily Stable Microbial Kill Switches,” Mol. Cell, vol. 68, no. 4, pp. 686-697.e3, Nov. 2017, doi: 10.1016/j.molcel.2017.10.033.

[18] C. Helenek et al., “Synthetic gene circuit evolution: Insights and opportunities at the mid-scale,” Cell Chem. Biol., vol. 31, no. 8, pp. 1447–1459, Aug. 2024, doi: 10.1016/j.chembiol.2024.05.018.

[19] S. Sadr, B. Zargar, M. G. Aucoin, and B. Ingalls, “Construction and Functional Characterization of a Heterologous Quorum Sensing Circuit in Clostridium sporogenes,” ACS Synth. Biol., vol. 14, no. 12, pp. 4857–4868, Dec. 2025, doi: 10.1021/acssynbio.5c00628.

[20] X. Song et al., “Development of a Quorum Sensing-Mediated Bacterial Autolytic System in Escherichia coli for Automatic Release of Intracellular Products,” ACS Synth. Biol., vol. 13, no. 6, pp. 1956–1962, Jun. 2024, doi: 10.1021/acssynbio.4c00084.

[21] C. T. Y. Chan, J. W. Lee, D. E. Cameron, C. J. Bashor, and J. J. Collins, “‘Deadman’ and ‘Passcode’ microbial kill switches for bacterial containment,” Nat. Chem. Biol., vol. 12, no. 2, pp. 82–86, Feb. 2016, doi: 10.1038/nchembio.1979.

[22] M. B. Elowitz, A. J. Levine, E. D. Siggia, and P. S. Swain, “Stochastic Gene Expression in a Single Cell,” Science, vol. 297, no. 5584, pp. 1183–1186, Aug. 2002, doi: 10.1126/science.1070919.

[23] A. Raj and A. Van Oudenaarden, “Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences,” Cell, vol. 135, no. 2, pp. 216–226, Oct. 2008, doi: 10.1016/j.cell.2008.09.050.

[24] T. W. Lo, H. J. Choi, D. Huang, and P. A. Wiggins, “Noise robustness and metabolic load determine the principles of central dogma regulation,” Sci. Adv., vol. 10, no. 34, p. eado3095, Aug. 2024, doi: 10.1126/sciadv.ado3095.

[25] H. M. Salis, E. A. Mirsky, and C. A. Voigt, “Automated design of synthetic ribosome binding sites to control protein expression,” Nat. Biotechnol., vol. 27, no. 10, pp. 946–950, Oct. 2009, doi: 10.1038/nbt.1568.

[26] K. Guan, E. R. Curtis, D. J. Lew, and T. C. Elston, “Particle-based simulations reveal two positive feedback loops allow relocation and stabilization of the polarity site during yeast mating,” PLOS Comput. Biol., vol. 19, no. 10, p. e1011523, Oct. 2023, doi: 10.1371/journal.pcbi.1011523.

[27] S. C. Sleight, B. A. Bartley, J. A. Lieviant, and H. M. Sauro, “Designing and engineering evolutionary robust genetic circuits,” J. Biol. Eng., vol. 4, no. 1, p. 12, 2010, doi: 10.1186/1754-1611-4-12.

[28] R. Srivastava and C. F. Lesser, “Living Engineered Bacterial Therapeutics: Emerging Affordable Precision Interventions,” Microb. Biotechnol., vol. 17, no. 11, p. e70057, Nov. 2024, doi: 10.1111/1751-7915.70057.

[29] Y. Al Tall, Y. Alkurdi, N. Alshraiedeh, and S. H. Sabi, “Designing Novel Antimicrobial Agents from the Synthetic Antimicrobial Peptide (Pep-38) to Combat Antibiotic Resistance,” Pharmaceuticals, vol. 18, no. 6, p. 862, Jun. 2025, doi: 10.3390/ph18060862.

[30] Committee on Strategies for Identifying and Addressing Potential Biodefense Vulnerabilities Posed by Synthetic Biology, Board on Chemical Sciences and Technology, Board on Life Sciences, Division on Earth and Life Studies, and National Academies of Sciences, Engineering, and Medicine, Biodefense in the Age of Synthetic Biology. Washington, D.C.: National Academies Press, 2018, p. 24890. doi: 10.17226/24890.

[31] C. D. Nadell, K. Drescher, and K. R. Foster, “Spatial structure, cooperation and competition in biofilms,” Nat. Rev. Microbiol., vol. 14, no. 9, pp. 589–600, Sep. 2016, doi: 10.1038/nrmicro.2016.84.

[32] F. Ceroni, R. Algar, G.-B. Stan, and T. Ellis, “Quantifying cellular capacity identifies gene expression designs with reduced burden,” Nat. Methods, vol. 12, no. 5, pp. 415–418, May 2015, doi: 10.1038/nmeth.3339.

[33] D. Del Vecchio, A. J. Dy, and Y. Qian, “Control theory meets synthetic biology,” J. R. Soc. Interface, vol. 13, no. 120, p. 20160380, Jul. 2016, doi: 10.1098/rsif.2016.0380.

[34] Y. Wang, H. Wang, L. Wei, S. Li, L. Liu, and X. Wang, “Synthetic promoter design in Escherichia coli based on a deep generative network,” Nucleic Acids Res., vol. 48, no. 12, pp. 6403–6412, Jul. 2020, doi: 10.1093/nar/gkaa325.

[35] E. Righetti, C. Uluşeker, and O. Kahramanoğulları, “Stochastic Simulations as a Tool for Assessing Signal Fidelity in Gene Expression in Synthetic Promoter Design,” Biology, vol. 10, no. 8, p. 724, Jul. 2021, doi: 10.3390/biology10080724.

[36] C. Zhang, R. Tsoi, and L. You, “Addressing biological uncertainties in engineering gene circuits,” Integr. Biol., vol. 8, no. 4, pp. 456–464, 2016, doi: 10.1039/C5IB00275C.

[37] K. S. Farquhar, M. Tyler Guinn, G. Balázsi, and D. A. Charlebois, “Synthetic Gene Circuits for Antimicrobial Resistance and Cancer Research,” in Synthetic Genomics - From BioBricks to Synthetic Genomes, M. Fernández-Niño and L. H. Reyes, Eds., IntechOpen, 2022. doi: 10.5772/intechopen.99329.

[38] A. Boo, R. Ledesma Amaro, and G.-B. Stan, “Quorum sensing in synthetic biology: A review,” Curr. Opin. Syst. Biol., vol. 28, p. 100378, Dec. 2021, doi: 10.1016/j.coisb.2021.100378.

[39] L. S. Gonzalez-Delgado et al., “Two-site recognition of Staphylococcus aureus peptidoglycan by lysostaphin SH3b,” Nat. Chem. Biol., vol. 16, no. 1, pp. 24–30, Jan. 2020, doi: 10.1038/s41589-019-0393-4.

[40] H. Engelberg-Kulka, R. Hazan, and S. Amitai, “mazEF : a chromosomal toxin-antitoxin module that triggers programmed cell death in bacteria,” J. Cell Sci., vol. 118, no. 19, pp. 4327–4332, Oct. 2005, doi: 10.1242/jcs.02619.

[41] X. Song et al., “Development of a Quorum Sensing-Mediated Bacterial Autolytic System in Escherichia coli for Automatic Release of Intracellular Products,” ACS Synth. Biol., vol. 13, no. 6, pp. 1956–1962, Jun. 2024, doi: 10.1021/acssynbio.4c00084.