On Programs and Genomes

We outline the global control architecture of genomes. A theory of genomic control information is presented. The concept of a developmental control network called a cene (for control gene) is introduced. We distinguish parts-genes from control genes or cenes. Cenes are interpreted and executed by the cell and, thereby, direct cell actions including communication, growth, division, differentiation and multi-cellular development. The cenome is the global developmental control network in the genome. The cenome is also a cene that consists of interlinked sub-cenes that guide the ontogeny of the organism. The complexity of organisms is linked to the complexity of the cenome. The relevance to ontogeny and evolution is mentioned. We introduce the concept of a universal cell and a universal genome.

💡 Research Summary

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The paper proposes a radical reconceptualization of the genome, shifting the focus from a static collection of “part‑genes” that encode proteins to a dynamic, executable control architecture that governs cellular behavior. Central to this view is the introduction of the term “cene” (control gene). Unlike conventional genes, a cene does not code for a structural product; instead it encodes a set of regulatory instructions that a cell reads, interprets, and executes. These instructions direct fundamental cellular processes such as growth, division, differentiation, and the coordinated actions required for multicellular development, including inter‑cellular communication and environmental response.

Cenes are not isolated units; they are linked together into larger subnetworks. The complete, organism‑wide network of inter‑connected sub‑cenas is termed the “cenome”. The cenome functions as a global program that each cell partially reads, thereby determining its fate within the developmental context. The authors argue that the complexity of an organism’s morphology and behavior correlates directly with the complexity of its cenome: more intricate organisms possess cenomes with a greater number of sub‑cenas and more elaborate connectivity.

To provide a theoretical scaffold for evolutionary dynamics, the authors introduce the concepts of a “universal cell” and a “universal genome”. A universal cell is an abstract entity capable of interpreting any possible cene, while a universal genome contains the superset of all conceivable cenes. This abstraction allows the authors to model evolutionary innovation as the addition, modification, or recombination of cenes within the cenome, analogous to software updates rather than simple point mutations. In this framework, evolutionary novelty emerges from the accumulation and re‑organization of control information, not merely from changes in protein‑coding sequences.

The paper extends the cene/ cenome framework beyond biology into robotics and multi‑agent systems. It argues that autonomous robots or software agents require an internal control network analogous to a biological cenome to achieve adaptive, coordinated behavior. By embedding cene‑like modules that govern communication protocols, self‑assembly, and context‑dependent action selection, a swarm of robots could exhibit complex collective dynamics without centralized control. This approach promises greater scalability, robustness, and flexibility compared with traditional hierarchical control architectures.

In the discussion of ontogeny and evolution, the authors contend that traditional genotype‑phenotype mappings are insufficient to explain the emergence of complex traits. Instead, the evolution of regulatory control (the cenome) provides a more powerful explanatory layer. The accumulation of new cenes, the rewiring of existing connections, and the modular reuse of sub‑cenas can generate novel developmental pathways and, consequently, new phenotypic possibilities.

The paper concludes with a research agenda: (1) develop computational and experimental methods to identify and characterize cenes within real genomes; (2) test the feasibility of designing synthetic organisms whose developmental programs are specified at the cene level; (3) implement cene‑based control architectures in robotic swarms and evaluate their performance against conventional designs. By pursuing these directions, the authors anticipate that the cene/ cenome paradigm could unify insights across genetics, developmental biology, evolutionary theory, synthetic biology, and autonomous systems engineering, offering a comprehensive framework for understanding and engineering complex, adaptive systems.