by DoctorOetker | 2025-11-16 04:32:07+00:00
What I don't like when people bend over backwards to hype epigenetics:

1. the lack of historical context and opinion lock-in once expressed: before the human genome project many geneticists thought the number of "genes" (yes its an oversimplification) would be much much larger than what was eventually discovered to be the case. It was a shock to many biologists, that there were just about tens of thousands of genes in the genome, basically "a handful" in terms of control theory. The miracle of life started to look marginal and banal, just like many protested the earth not being the center of the solar system...

2. no highlighting the difference between single-cellular species and multi-cellular species: generational memory effects are obviously observable for single-cell organisms. For multi-cellular organisms, like humans, the concept of generation is ambiguous: is one talking about cellular generations (fertilized egg cell, dividing and the daughter cells dividing again, ...), or about organism-level generations (human parents of human children)? It becomes immediately apparent that the cell lineage from fertilized egg cell, to either sperm cell of the son or egg cells of the daughter, would involve lots of divisions, and the final sperm or egg cell would only have access to vague general variables: blood sugar levels, temperature, some hormonal levels, ...

3. Just like one can not truly study physics, without learning mathematics, and then formulating claims and observations mathematically, to truly study biology and the homeostasis it implements, you need systems biology: a mathematical description of biology. To understand multicellular organisms, one needs to understand the concept of cell types mathematically, and to summarize it in natural language cell types are the stable attractors forcing the cell contents to return to the closest state of the cell type. To make a rough analogy (cell/human, cell type/profession) then in contrary to humans, cell's don' t have any memory which is independent of their cellular content. So apart from the content of the cell, a cell is amnesiac. Imagine humans without memory, but upon seeing the room in which they work, they can constantly re-understand what role they perform, and any deviation from what is ingrained in your local DNA copy of the genome is responded to. If you find yourself in a room with ovens and lots of dough, and some of the ovens have bread, but for some reason there's a cop's badge on the table, then you know you are a baker, and you throw out the cop's badge. If you are in a special car with red and blue lights, and you are behind the wheel, but there is for some reason an oven sitting on the passenger seat, then you are a cop, and you take the oven out of your police car.

Once you understand how cell types implement the memory necessary so that a neuron in your brain doesn't start to behave like a skin cell on your anus or vice versa, you understand the ridiculous proposition of epigenetics, a quest for the holy grail of all the missing information that the human genome project failed to find... Euhm well sure there is some modulation of transcription by histone modifications etc... but all of that can be modeled in the same language of reactions that is currently used to model Gene Regulatory Networks, with Gillespie simulators etc. Instead of wishing to vindicate ones old (and wrong) forgotten statements from before the human genome project due to psychological lock-in effects, it would be more productive to point out that in practice a lot of known useful data is ignored, so why not first make use of information we know exists before ingraining vague ideas about epigenetics in the next generation of students? stop ignoring the promotor regions and consensus sequences etc when sequencing genomes, there is a wealth of information to be had there, and personalized DNA-driven medicine will never take off until these are by default sequenced as well, as they directly relate to transcription rates! you know, good old classical gene regulatory network data.