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Currently, phenotypes of all life forms are primarily explained in the context of function and polymorphism of “conventional” genes on a static platform. Thus far, conventional gene-focused attempts to decode the mechanisms underlying normal and disease phenotypes of humans, animals, and plants have often become inconclusive. Humans and mice share ~85% of conventional gene sequences; this is inconsistent with the notion that species-specific phenotypes are determined by conventional genes when vast phenotypic differences separate them. Conventional genes comprise ~3% of the human and mouse genome information systems (GISs), and the majority of the residual is occupied by a plethora of repetitive elements (REs), including transposable REs (TREs), named the “TREome.” The TREome, which constitutes at least 45% of the human and mouse GISs, is inherently diverse within each species, and has the potential for temporal and spatial shaping of the GISs’ configuration through “copy and paste” and/or “cut and paste” functions. In addition, some TREome members harbor coding potentials for functional proteins even though they are frequently called as “non-coding” long RNAs.

In the absence of a single fully sequenced GIS of a human or any other mammal and the very limited understanding of the GISs as a whole, conventional gene-focused approaches may be insufficient to properly define their normal and disease phenotypes. The inherent diversity and lifelong accumulation of acquired TREome activity in response to acute and chronic stress signals (e.g., aging, diet, injury, infection, environment) conceivably contribute to the structural variations (temporal and spatial) within an individual’s GIS. These structural changes may temporally and spatially alter and/or fine-tune the GIS’ function, including regulation of conventional genes, in the affected regions.

Our investigations aim to identify, compute, and account for the impacts of multi-dimensional TREome activity on GIS functionality as well as normal and disease phenotypes of life forms by employing selected concepts and tools from biology, mathematics, and/or information technology. This presentation provides a small set of clues as to why the entire landscape of the dynamic and multi-dimensional GIS, not just the conventional gene fraction on a static GIS platform, needs to be interrogated and computed to properly decipher the biology of life forms.