Learning the Language of Life: Chaotic Exploration and the Emergence of Biological Abstraction

Understanding the fundamental principles governing living systems remains a central challenge in biology. While advances in synthetic genomics have enabled the construction of minimal cells, a significant fraction of essential genes remains functionally uncharacterized, highlighting persistent gaps in our comprehension of biological organization. In parallel, theoretical models of self-replicating systems, notably those introduced by John von Neumann, provide a formal framework for describing autonomous, information-driven entities.

In this work, we propose a conceptual framework that unifies minimal cell engineering with a guided chaotic exploration of genomic space. We introduce the notion of a “Von Neumann minimal cell” as a biological platform embodying the core components required for self-maintenance and replication: an informational substrate, an execution machinery, and a boundary-defined environment. Within this constrained architecture, we define a “chaotic exploration algorithm” in which large-scale, AI-assisted generation of genomic variants is iteratively evaluated under controlled, non-propagative conditions.

Rather than optimizing for predefined functions, this approach emphasizes exploratory diversity and emergent behavior, enabling the identification of novel genotype–phenotype relationships and potentially uncovering alternative organizational principles of living matter. By coupling stochastic variation with computationally guided selection and reverse inference, the framework aims to transform biological engineering into a process of systematic discovery.

We discuss the theoretical implications of this approach for synthetic biology, artificial life, and the study of universal constraints on living systems, while outlining safety considerations and experimental boundaries. This perspective suggests a shift from incremental modification of existing organisms toward the construction of a programmable, exploratory substrate for life-like systems.