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Heterogeneity-mediated cellular adaptation and its trade-off: searching for the general principles of diseases.

Henry H Heng
Published in: Journal of evaluation in clinical practice (2016)
Big-data-omics have promised the success of precision medicine. However, most common diseases belong to adaptive systems where the precision is all but difficult to achieve. In this commentary, I propose a heterogeneity-mediated cellular adaptive model to search for the general model of diseases, which also illustrates why in most non-infectious non-Mendelian diseases the involvement of cellular evolution is less predictable when gene profiles are used. This synthesis is based on the following new observations/concepts: 1) the gene only codes "parts inheritance" while the genome codes "system inheritance" or the entire blueprint; 2) the nature of somatic genetic coding is fuzzy rather than precise, and genetic alterations are not just the results of genetic error but are in fact generated from internal adaptive mechanisms in response to environmental dynamics; 3) stress-response is less specific within cellular evolutionary context when compared to known biochemical specificities; and 4) most medical interventions have their unavoidable uncertainties and often can function as negative harmful stresses as trade-offs. The acknowledgment of diseases as adaptive systems calls for the action to integrate genome- (not simply individual gene-) mediated cellular evolution into molecular medicine.
Keyphrases
  • genome wide
  • copy number
  • mitochondrial dna
  • big data
  • dna methylation
  • single cell
  • artificial intelligence
  • machine learning
  • physical activity
  • transcription factor
  • genome wide identification
  • deep learning