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State space analysis of diphtheria pathogenesis using semi-tensor products and permutation methods
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State space analysis of diphtheria pathogenesis using semi-tensor products and permutation methods
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Journal Article
State space analysis of diphtheria pathogenesis using semi-tensor products and permutation methods
2025
Overview
In this study, we analyze the state space of a Boolean network modeling diphtheria pathogenesis, focusing on key genes such as Tox, Rep, INF1/INF2, TLR, AP1, IL6, and TNF. We introduce targeted perturbations to reveal how the network responds and converges to its attractors. Our approach utilizes semi-tensor product techniques and permutation methods to recast the Boolean dynamics into a linear algebraic scheme, enabling efficient identification of transient states, stable attractors, and Garden-of-Eden states. This work fills an important gap by clarifying how specific gene interactions drive the network toward non-pathogenic states. Our results show that altering regulatory relationships, particularly those between Rep, Tox, and interferon signals, significantly influences basin sizes and attractor stability, thereby enhancing our understanding of the network's resilience and informing potential therapeutic strategies.
