A new model for the inference of biological entities states: Ternary Entity State Inference System

Understanding the state transitions in biological systems and identifying critical steady states are crucial for investigating disease development and discovering key therapeutic targets. To advance the study of state transitions in specific biological entities, we proposed the Ternary Entity State Inference System (T-ESIS). T-ESIS builds upon the Entity State Inference System by providing richer information on entity states, where states can take values of 0, 1, or 1/2, representing activation, inhibition, and normal states, respectively. This method infers state transition pathways based on interaction relationships and visualizes them through the Entity State Network. Furthermore, the cyclic structures within the Entity State Network capture positive and negative feedback loops, providing a topological foundation for the formation of steady states. To demonstrate the applicability of T-ESIS, entity states were modeled, and attractor analysis was conducted in non-small cell lung cancer (NSCLC) networks. Our analysis provided valuable insights into targeted therapy for NSCLC, highlighting the potential of T-ESIS in uncovering therapeutic targets and understanding disease mechanisms. Moreover, the proposed T-ESIS framework facilitated the inference of entity state transitions and the analysis of steady states in biological systems, offering a novel approach for studying the dynamic principles of these systems. This ternary dynamic modeling approach not only deepened our understanding of biological networks but also provided a methodological reference for future research in the field.