NONE 20260709132507955 wseas/wseas content-registration-form@crossref.org content-registration-form MOLECULAR SCIENCES AND APPLICATIONS 2944-9138 2732-9992 Stefcho Bankov University of Agribusiness and Rural Development (UARD), Plovdiv, BULGARIA Radoslav Rangelov Institute of Biology and Immunology of Reproduction “Acad. Kiril Bratanov” – Bulgarian Academy of Sciences Sofia, BULGARIA The convergence of biology, artificial intelligence (AI), and automated digital systems is catalyzing a new class of research with inherent dual-use characteristics that can accelerate biomedical progress and support national security, while also risking the amplification of biological threats if misused [1], [2]. Mitochondrial networks, governed by non-linear dynamics, feedback loops, and near-critical phase behavior, constitute a particularly sensitive substrate in this context [4]–[7]. Consequently, computational modeling of mitochondrial networks should be conceptualized not merely as a tool for fundamental research, but as an element of modern biosecurity and cyberbiosecurity risk governance [3], [8]. This paper presents a computational model of mitochondrial network dynamics that is explicitly designed to be defense-oriented and dual-use-aware. The model is intentionally agnostic to specific biological agents: it uses an abstract stress input A(t) that does not represent a pathogen, toxin, chemical compound, dosage, or experimental protocol. Its purpose is not to optimize biological impact, but to identify instability thresholds, bifurcations, and early-warning indicators (e.g., the critical decline of mitochondrial membrane potential (ΔΨm) toward zero, increasing variance and oscillatory behavior in reactive oxygen species (ROS), and progressive mitochondrial network fragmentation) that can support resilience engineering and early warning. The framework aligns with contemporary dual-use AI governance by enabling the evaluation of “capability thresholds” for unacceptable risk without generating actionable knowledge of exploitation [2]. Overall, the study contributes a non-exploitative, systems-engineering-style tool for assessing mitochondrial resilience that can be used in biomedical and defensive settings (e.g., early warning, stress testing, safety evaluation) while remaining, by design, below dual-use capability thresholds. 07 09 2026 07 09 2026 29 5 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232023.2026.6.5 https://wseas.com/journals/msa/2026/a10msa-005(2026).pdf D. DiEuliis, J. Giordano, “Why Gene Editors Like CRISPR/Cas May Be a Game-Changer for Neuroweapons,” Health Security, vol. 15, no. 3, pp. 296–302, 2017. doi:10.1089/hs.2016.0120. J. Pannu, D. Bloomfield, R. MacKnight, et al., “Dual-use capabilities of concern of biological AI models,” PLoS Computational Biology, vol. 21, no. 5, e1012975, 2025.doi:10.1371/journal.pcbi.1012975. J. Peccoud, J. E. Gallegos, R. Murch, et al., “Cyberbiosecurity: From Naive Trust to Risk Awareness,” Trends in Biotechnology, vol. 36, no. 1, pp. 4–7, 2018. doi:10.1016/j.tibtech.2017.10.012. D. C. Chan, “Fusion and Fission: Interlinked Processes Critical for Mitochondrial Health,” Annual Review of Genetics, vol. 46, pp. 265–287, 2012. doi:10.1146/annurevgenet-110410-132529. R. J. Youle, A. M. van der Bliek, “Mitochondrial Fission, Fusion, and Stress,” Science, vol. 337, no. 6098, pp. 1062–1065, 2012. doi:10.1126/science. 1219855. D. B. Zorov, M. Juhaszova, S. J. Sollott, “Mitochondrial Reactive Oxygen Species (ROS) and ROS-Induced ROS Release,” Physiological Reviews, vol. 94, no. 3, pp. 909–950, 2014. doi:10.1152/physrev. 00026.2013. N. Zamponi, E. Zamponi, S. A. Cannas, et al., “Universal dynamics of mitochondrial networks: a finite-size scaling analysis,” Scientific Reports, vol. 12, 17074, 2022. doi:10.1038/s41598-022-14946-9. A. M. George, “The National Security Implications of Cyberbiosecurity,” Frontiers in Bioengineering and Biotechnology, vol. 7, 51, 2019. doi:10.3389/fbioe. 2019.00051. M. Picard, N. J. Murugan, “The energy resistance principle,” Cell Metabolism, vol. 37, no. 11, pp. 2107–2127, 2025. doi:10.1016/j.cmet.2025.09.002. F. Bringezu, J. C. Gomez-Tamayo, M. Pastor, “Ensemble prediction of mitochondrial toxicity using machine learning technology,” Computational Toxicology, vol. 20, 100189, 2021— doi:10.1016/j.comtox.2021.100189. K. Furuta, R. Komiyama, T. Kanno, H. Fujii, S. Yoshimura, and T. Yamada, “Resilience Analysis of Critical Infrastructure,” WSEAS Transactions on Computers, vol. 21, pp. 58–65, 2022. doi:10.37394/23205.2022.21.8. N. Anjum, H. Alshahrani, A. Shaikh, M. UlHassan, M. Kiran, and S. Raz, “Cyberbiosecurity challenges in next-generation sequencing: a comprehensive analysis of emerging threat vectors,” IEEE Access, vol. 13, pp. 52006–52035, 2025. doi:10.1109/ACCESS.2025.3552069.