Solving Complex Diseases at the Source Code Level

If you have a bug in the user interface of a software system, do you look for fixing the problem in the manifested UI on millions of computers? Or do you fix the underlying code that is causing the problem?

Here is another one. Let us say you have a manufacturing unit that produces the statue of a gnome. Let’s say the nose of the gnome is found defective. Let’s also say you tend to produce thousands of gnomes per day. Do you fix the defective nose across thousands of gnomes, or do you fix the mold that produces the nose of the gnome?

Trying to solve complex diseases by focusing on downstream proteins instead of the source code, which is the upstream genes, is exactly like the above scenario. A complex disease manifests as a consistent flaw in downstream phenotypes that avoids cell death and mutates bad code at an uncontrollable rate. The origination of many such diseases (if not all of them) starts in the source code of the DNA.

The genome is a more stable and accessible dataset compared to the proteome. Genomic DNA can be easily extracted, sequenced, and analyzed using well-established techniques, whereas proteomics, which deals with the entire set of proteins expressed by a genome, requires more complex and less standardized methodologies.

DNA sequences are less variable and more straightforward to study than proteins. Proteins can undergo various post-translational modifications and exist in multiple forms, which complicates their analysis. In contrast, genomic sequences provide a consistent and enduring blueprint, which can be incredibly useful for identifying hereditary conditions and potential genetic disorders.

The last 50 years of tech and innovation have focused on proteomics. Only in the last 5 years have we started looking at novel polygenic targets in the regulatory regions of the genome. The next decade and century will be focused on solving problems at the roots where they originate, at the genomic level.