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<p>Classical genetics has traditionally conceptualized biological inheritance as the transmission of DNA sequence information. In this framework, genes function as instructions specifying biological traits, while development represents the execution of those instructions under environmental modulation. However, increasing empirical evidence from epigenetics, developmental systems biology, and single-cell genomics demonstrates that identical genetic sequences can produce divergent phenotypic outcomes depending on cellular context, parental history, and environmental conditions. This paper proposes the interpretive genome framework, a probabilistic dual-track model of inheritance that distinguishes between two interacting components of biological information. Track 1 consists of the genetic archive—the inherited DNA sequence that constrains the space of possible phenotypic outcomes. Track 2 consists of the interpretive machinery—epigenetic regulation, gene regulatory networks, cellular context, and environmental inputs that determine how genetic information is probabilistically expressed. Within this framework, development is not the deterministic execution of a genetic program but a probabilistic process in which interpretive systems weight expression outcomes within genetically bounded possibility spaces. Reproduction is reconceptualized as a quad exchange, integrating maternal and paternal genetic archives alongside parental interpretive contributions. To explain how probabilistic systems maintain stability, the framework introduces the concept of biological ballast, referring to stabilizing regulatory mechanisms—such as feedback loops, redundancy, epigenetic memory, and population diversity—that constrain phenotypic variance within viable ranges. A minimal probabilistic formalization of the model is presented, along with testable predictions regarding stochastic gene expression, parental interpretive weighting, developmental canalization, and diversity–resilience relationships in populations. By reframing inheritance as a probabilistic interpretive process rather than deterministic code execution, the interpretive genome framework integrates insights from genetics, epigenetics, systems biology, and evolutionary theory into a unified account of biological stability and adaptability.</p>

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The interpretive genome: a probabilistic dual-track model of biological inheritance

  • 期刊论文

  • 发表日期：

    2026年4月21日

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  • Matthew Brian Dominik

摘要

Classical genetics has traditionally conceptualized biological inheritance as the transmission of DNA sequence information. In this framework, genes function as instructions specifying biological traits, while development represents the execution of those instructions under environmental modulation. However, increasing empirical evidence from epigenetics, developmental systems biology, and single-cell genomics demonstrates that identical genetic sequences can produce divergent phenotypic outcomes depending on cellular context, parental history, and environmental conditions. This paper proposes the interpretive genome framework, a probabilistic dual-track model of inheritance that distinguishes between two interacting components of biological information. Track 1 consists of the genetic archive—the inherited DNA sequence that constrains the space of possible phenotypic outcomes. Track 2 consists of the interpretive machinery—epigenetic regulation, gene regulatory networks, cellular context, and environmental inputs that determine how genetic information is probabilistically expressed. Within this framework, development is not the deterministic execution of a genetic program but a probabilistic process in which interpretive systems weight expression outcomes within genetically bounded possibility spaces. Reproduction is reconceptualized as a quad exchange, integrating maternal and paternal genetic archives alongside parental interpretive contributions. To explain how probabilistic systems maintain stability, the framework introduces the concept of biological ballast, referring to stabilizing regulatory mechanisms—such as feedback loops, redundancy, epigenetic memory, and population diversity—that constrain phenotypic variance within viable ranges. A minimal probabilistic formalization of the model is presented, along with testable predictions regarding stochastic gene expression, parental interpretive weighting, developmental canalization, and diversity–resilience relationships in populations. By reframing inheritance as a probabilistic interpretive process rather than deterministic code execution, the interpretive genome framework integrates insights from genetics, epigenetics, systems biology, and evolutionary theory into a unified account of biological stability and adaptability.