“Code” isn’t a great term since it is a physical interaction that causes chemical and physical changes, sort of like a lock and key. Biologists usually refer to it as a signal. As stated earlier, when something binds to a receptor that is sitting on the cell membrane it kicks off a whole string of events. Here is a diagram for neuropeptide Y (NPY) signaling, just to give you an idea of what is going on:
Got it… Thanks for the clear explanation! So it is the physical property of the peptide that begins a reaction. If it is one peptide, it begins a certain reaction, and if it is another peptide, it begins another reaction (or possibly none at all.) So the value of the peptide is not arbitrary at all, it is specific, in that a certain kind will invoke a certain reaction, thus a certain result.
So, at the forks in the red lines with arrows, what is happening? It seems like a decision of sorts, but it is not shown. For instance, from PIP2 to either IP3 or DAG.
The receptor is also specific. It is the ligand-receptor interaction that is important.
In this case, the membrane lipid PIP2 is cleaved into IP3 and DAG by phospholipase C (PLC). Both products go on to trigger other reactions further down the pathway. This is a classic G-protein pathway.
Incredible! Love it! Thanks so much! What an amazing tiny realm that is!
That’s why they call it molecular biology!
It is also an amazing field to work in, IMHO. There are times when you have your head down working hard to get data and forget just how amazing it is that we can probe these types of reactions to figure out what is going on inside of cells at the molecular level.
I love to see the computer reconstructions of the protein machines in action. What an incredible world. I would imagine that you love the work, entirely.
Yes! This is what gets me. That you can discern the makeup, contents, processes, etc., from something that can be barely seen with an electron microscope. Exciting times, indeed.
So, then, it is not a decision, but rather it is a fork and both paths are taken?
Just a protein bein’ a G!
This may be drifting off topic just a bit, but this stuff is cool . . .
Some of the fun stuff are things you can see with light microscopy. One of the projects I worked on used isolated mouse cardiomyocytes (heart muscle cell). By applying electrical currents you can cause the cells to contract, and you can measure the contractions using the distance between the light and dark bands in the cardiomyocyte. The light and dark bands are alternating bands of myosin and actin, so you can see differences in protein using light microscope at about 200-400x magnification.
You can also get a fluorescent dye into the cells that fluoresce when they bind to calcium. Internally stored calcium is the signalling molecule that causes contractions, and you can measure the release and uptake of calcium over a few hundred milliseconds by measuring fluorescence hundreds or thousands of times a second. This is the graph that you get:
You get a spike of calcium that triggers the contraction, and then uptake of the Ca++ by SERCA2 back into the endoplasmic reticulum. This all happens in less than half a second.
This is somewhat similar to the G-protein pathway for neuropeptides, but it is a lot slower, obviously.
I never like those diagrams… So many times they’re both too simplistic – beneath each ‘line’ hides a wealth of rather complicated mechanisms – and too complex – making it seem that these factors interact with equal ‘strength’ or are equally relevant. If we’re going to do them right, they really need info like control coefficients (very, very hard to determine) and the specific cellular contexts. Plus so many of the molecular players show up in so many other ‘pathways’. It’s rare that a signalling intermediate has only one role in a cell.
There has always been a battle between clarity, specificity, and completeness in representing these types of pathways. If I am not mistaken, there are scientists whose career is focused on integrating genomics, transcriptomics, proteomics, and metabolomics. It’s not something I am much interested in, but it would seem to tickle the fancy of people interested in big data sets, databases, theory, and computer programming (someone like @swamidass?).
A source for clarity on “bad -omics”, the proliferation of junk terms ending in -omics:
I don’t know if the author despises the other -omics I listed, but he seems to be somewhat reasonable on the use of transcriptomics:
I haven’t read through all of the blog posts, but it would appear that the -omes I listed may have made the cut.
I feel fairly certain that “metabolomics”, at least, would and should be viewed as an abomination. “Transcriptome” and “proteome” at least have the decency to be tied directly to the genome.
Meh. It’s mostly marketing for increased attention and/or grants.