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This article presents a synthetic biology framework called Chemical Organization Theory (COT), that formalizes synthetic biology concepts using reaction networks. We show how this formalism applies across synthetic biology scales, from genetic circuits and metabolic engineering to synthetic consortia, and demonstrate through a dedicated computational platform (pyCOT) how abstract concepts like self-organization, emergence, feedback, and resilience become operational and computable. Drawing on 4 years of pedagogical implementation across diverse student populations-with backgrounds spanning design, social sciences, mathematics, and ecology-we find that organizational reasoning transfers effectively across disciplines. This interdisciplinary experience revealed a key pedagogical principle: students develop stronger systems thinking when they first encounter organizational concepts through familiar, non-biological systems before transferring to synthetic biology. We argue that this graduated complexity approach, enabled by COT's domain-general formalism, addresses a fundamental gap in synthetic biology education and offers a pathway for spreading complex adaptive systems literacy beyond biology.
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Veloz et al. (2026) studied this question.
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