Abstract: 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.

Keywords: dual-use, cyberbiosecurity, mitochondrial network, non-linear dynamics, bifurcation, criticality, ΔΨm, ROS, resilience engineering, early warning

Pages: 29-34

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