A Priori recognition for biological phenomena prior to empirical understanding

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Abstract

Biological phenomena include unrecognized events. These unrecognized events often unknowingly increase observer bias, which inhibits open and reproducible science. In this study, we focused on the recognition procedures underlying primary data and modeled cell population behavior. Using agent-based modeling (ABM), we demonstrated that cellular behaviors can be categorized into 11 distinct types, a framework we defined as the Behavioral Eleven Cell Class (BECC). We further validated BECC by describing the differential STAT3 phosphorylation patterns in leukocyte subsets and polarized T cell differentiation in OT-II transgenic mice. BECC serves as a novel descriptive method with three defining features: (i) it functions as a symbolic system independent of numerical or linguistic constraints, (ii) it represents the minimal unit of recognition, and (iii) it enables the expression of inherently unrecognizable phenomena. BECC allows for observer subjectivity and the relativity of results while enforcing rigorous recognition. This unique approach provides a practical and conceptual basis for advancing open and reproducible science.