Computational Molecular Biology 2025, Vol.15, No.4, 193-207 http://bioscipublisher.com/index.php/cmb 205 nonlinear patterns, and deep models can reverse-engineer DNA sequences that satisfy specific dynamic characteristics. Nowadays, there are even studies that allow reinforcement learning algorithms to "evolve" their loop structures in virtual environments, enabling them to learn to resist noise or metabolic burdens on their own. The study of synthetic genetic circuits is no longer a game for a single discipline; it requires biologists, engineers, mathematicians, and even computer scientists to collaborate on the same table. Future researchers must be proficient in both experiments and modeling. Only in this way can we handle increasingly complex systems and transform models from auxiliary tools into design engines. It can be foreseen that in the future, we will witness the emergence of more intelligent and stable artificial gene networks in the medical, industrial and ecological fields. At that time, synthetic biology will truly move from "being able to create" to "knowing how to create". Acknowledgments The author extends sincere thanks to two anonymous peer reviewers for their invaluable feedback on the manuscript. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Turpin B., Bijman E.Y., Kaltenbach H.M., and Stelling J., 2023, Efficient design of synthetic gene circuits under cell-to-cell variability, BMC Bioinformatics, 24(Suppl 1): 460. https://doi.org/10.1186/s12859-023-05538-z Cao Z., and Grima R., 2019, Accuracy of parameter estimation for auto-regulatory transcriptional feedback loops from noisy data, Journal of the Royal Society Interface, 16(153): 20180967. Choi K., Medley J., König M., Stocking K., Smith L., Gu S., and Sauro H., 2018, Tellurium: an extensible python-based modeling environment for systems and synthetic biology, BioSystems, 171: 74-79. https://doi.org/10.1016/j.biosystems.2018.07.006 Dahlquist K., Fitzpatrick B., Camacho E., Entzminger S.D., and Wanner N.C., 2015, Parameter estimation for gene regulatory networks from microarray data: cold shock response in Saccharomyces cerevisiae, Bulletin of Mathematical Biology, 77(8): 1457-1492. https://doi.org/10.1007/s11538-015-0092-6 Dey A., and Barik D., 2021, Potential landscapes, bifurcations, and robustness of tristable networks, ACS Synthetic Biology, 10(2): 391-401. https://doi.org/10.1021/acssynbio.0c00570 Gao G., Bian Q., and Wang B., 2023, Synthetic genetic circuit engineering: principles, advances and prospects, Synthetic Biology Journal, 6(1): 45-64. https://synbioj.cip.com.cn/EN/10.12211/2096-8280.2023-096 Goetz H., Zhang R., Wang X., and Tian X.J., 2025, Resource competition-driven bistability and stochastic switching amplify gene expression noise, PLoS Computational Biology, 21(4): e1012931. https://doi.org/10.1371/journal.pcbi.1012931 Henningsen J., Schwarz-Schilling M., Leibl A., Gutierrez J., Sagredo S., and Simmel F., 2020, Single cell characterization of a synthetic bacterial clock with a hybrid feedback loop containing dCas9-sgRNA, ACS Synthetic Biology, 9(12): 3377-3387. https://doi.org/10.1021/acssynbio.0c00438 Hu C.Y., and Murray R., 2019, Design of a genetic layered feedback controller in synthetic biological circuitry, bioRxiv, 1101: 647057. https://doi.org/10.1101/647057 Kelly C.L., Harris A.W.K., Steel H., Hancock E., Heap J., and Papachristodoulou A., 2017, Synthetic negative feedback circuits using engineered small RNAs, Nucleic Acids Research, 46(18): 9875-9889. https://doi.org/10.1101/184473 Leon M., Woods M., Fedorec A.J.H., and Barnes C., 2016, A computational method for the investigation of multistable systems and its application to genetic switches, BMC Systems Biology, 10(1): 130. https://doi.org/10.1186/s12918-016-0375-z Likhoshvai V.A., Golubyatnikov V., and Khlebodarova T., 2020, Limit cycles in models of circular gene networks regulated by negative feedback loops, BMC Bioinformatics, 21(Suppl 11): 255. https://doi.org/10.1186/s12859-020-03598-z Liu X., and Niranjan M., 2017, Parameter estimation in computational biology by approximate Bayesian computation coupled with sensitivity analysis, arXiv Preprint, 1704: 9021. Loman T.E., Schwall C.P., Saez T., Liu Y., and Locke J.C., 2025, Mixed positive and negative feedback loops drive diverse single-cell gene expression dynamics, bioRxiv, 14: 632931. https://doi.org/10.1101/2025.01.14.632931
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