CMB_2025v15n4

Computational Molecular Biology 2025, Vol.15, No.4, 193-207 http://bioscipublisher.com/index.php/cmb 206 Madsen C., Goñi Moreno Á., Palchick Z., Roehner N., Bartley B., Bhatia S., Bissell M., Clancy K., Cox R., Gorochowski T., Grunberg R., Luna A., McLaughlin J., Nguyen T., Le Novere N., Pocock M., Sauro H., Scott-Brown J., Sexton J., Stan G., Tabor J., Voigt C., Zundel Z., Myers C., Beal J., and Wipat A., 2019, Synthetic biology open language visual (SBOL Visual) version 2.1, Journal of Integrative Bioinformatics, 16(2): 20180101. https://doi.org/10.1515/jib-2019-0025 Manninen T., Makiraatikka E., Ylipää A., Pettinen A., Leinonen K., and Linne M., 2006, Discrete stochastic simulation of cell signaling: comparison of computational tools, In: 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp.2013-2016. https://doi.org/10.1109/IEMBS.2006.260023 Merzbacher C., Mac Aodha O., and Oyarzún D., 2023, Machine learning for optimization of multiscale biological circuits, bioRxiv, 2: 526848. https://doi.org/10.1101/2023.02.02.526848 Meyer P., Cokelaer T., Chandran D., Kim K., Loh P., Tucker G., Lipson M., Berger B., Kreutz C., Raue A., Steiert B., Timmer J., Bilal E., Sauro H., Stolovitzky G., and Saez-Rodriguez J., 2014, Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach, BMC Systems Biology, 8(1): 13. https://doi.org/10.1186/1752-0509-8-13 Müller M.M., Arndt K.M., and Hoffmann S.A., 2025, Genetic circuits in synthetic biology: broadening the toolbox of regulatory devices, Frontiers in Synthetic Biology, 3: 1548572. https://doi.org/10.3389/fsybi.2025.1548572 Myers C., Barker N., Jones K., Kuwahara H., Madsen C., and Nguyen N., 2009, iBioSim: a tool for the analysis and design of genetic circuits, Bioinformatics, 25(21): 2848-2849. https://doi.org/10.1093/bioinformatics/btp457 Pájaro M., Otero-Muras I., Vázquez C., and Alonso A., 2017, SELANSI: a toolbox for simulation of stochastic gene regulatory networks, Bioinformatics, 34(5): 893-895. https://doi.org/10.1093/bioinformatics/btx645 Park J.H., Holló G., and Schaerli Y., 2024, From resonance to chaos by modulating spatiotemporal patterns through a synthetic optogenetic oscillator, Nature Communications, 15(1): 7284. https://doi.org/10.1038/s41467-024-51626-w Potvin-Trottier L., Lord N., Vinnicombe G., and Paulsson J., 2016, Synchronous long-term oscillations in a synthetic gene circuit, Nature, 538(7626): 514-517. https://doi.org/10.1038/nature19841 Rombouts J., and Gelens L., 2021, Dynamic bistable switches enhance robustness and accuracy of cell cycle transitions, PLOS Computational Biology, 17(1): e1008231. https://doi.org/10.1371/journal.pcbi.1008231 Sequeiros C., Pájaro M., Vázquez C., Banga J.R., and Otero-Muras I., 2023, IDESS: a toolbox for identification and automated design of stochastic gene circuits, Bioinformatics, 39(11): btad682. https://doi.org/10.1093/bioinformatics/btad682 Spartalis T.R., Lizano A., Copeland C.E., Kwon Y.C., and Tang X., 2024, Current application of modeling and cell-free system for synthetic gene circuit design, Synthetic Biology and Engineering, 2(3): 10013. https://doi.org/10.35534/sbe.2024.10013 Sun H., Comet J., Folschette M., and Magnin M., 2023, Condition for sustained oscillations in repressilator based on a hybrid modeling of gene regulatory networks, In: International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2023), SCITEPRESS-Science and Technology Publications, pp.29-40. https://doi.org/10.5220/0011614300003414 Sun Z., Wei W., Zhang M., Shi W., Zong Y., Chen Y., Yang X., Yu B., Tang C., and Lou C., 2022, Synthetic robust perfect adaptation achieved by negative feedback coupling with linear weak positive feedback, Nucleic Acids Research, 50(4): 2377-2386. https://doi.org/10.1093/nar/gkac066 Tyler J., Shiu A., and Walton J., 2019, Revisiting a synthetic intracellular regulatory network that exhibits oscillations, Journal of Mathematical Biology, 78(7): 2341-2368. https://doi.org/10.1007/s00285-019-01346-3 Vazquez-Vilar M., Selma S., and Orzaez D., 2023, The design of synthetic gene circuits in plants: new components, old challenges, Journal of Experimental Botany, 74(13): 3791-3805. https://doi.org/10.1093/jxb/erad167 Verdugo A., 2018, Hopf bifurcation analysis of the repressilator model, American Journal of Computational Mathematics, 8(2): 137-152. https://doi.org/10.4236/ajcm.2018.82011 Wang Y., Li R., Ji C., Shi S., Cheng Y., Sun H., and Li Y., 2014, Quantitative dynamic modelling of the gene regulatory network controlling adipogenesis, PLoS ONE, 9(10): e110563. https://doi.org/10.1371/journal.pone.0110563 Welsh C., Fullard N., Proctor C., Martinez-Guimera A., Isfort R., Bascom C., Tasseff R., Przyborski S., and Shanley D., 2018, PyCoTools: a Python toolbox for COPASI, Bioinformatics, 34(21): 3702-3710. https://doi.org/10.1093/bioinformatics/bty409

RkJQdWJsaXNoZXIy MjQ4ODYzNA==