Happy Sunday folks — It’s a sunny day. Hope it’s also sunny wherever you are and that you have an awesome day. If you like what you’re reading feel free to reach out or share this around to other people who might find this interesting.
Today we’ll be digging into the differences between Genes and Cells or the Hardware and Software. With this distinction we’ll have a better understanding of why we might have been tackling disease diagnostics and treatment all wrong.
You know, once we humans get stuck or attached to an idea/concept it takes a long time for us to see a new angle or a new perspective. I’m unable to say exactly why, but maybe it’s because there are too many people that benefit from it? Or maybe it’s because it pains us too much (collectively) to start again? Especially when we think we’ve been going the right path all along.
What are DNA, Genes and Cells?
DNA is the material that exists in every cell in our bodies. It holds the genetic code. DNA has a double-helix structure. It has a language that it uses to write code — it’s made up of four chemical bases:
Adenine (A)
Cytosine (C)
Thymine (T)
Guanine (G)
Our computers in comparison uses two — 0 and 1. Now a gene or genes are segments of that DNA that give instructions to do a job. That could be to create proteins or it could be to create RNA which will have another job.
Now, “zoom out” or go to a higher organisational layer. These genes or segments of DNA (and proteins) is a chromosome. That “X” looking thing. We have 46 of these in total. Proteins called histones are able to wrap/pack these guys up so they are able to fit into a small nucleus. And if you “zoom out” again you’ll see this nucleus where most of these chromosomes > genes > DNA resides. “Zoom out" once more and you’ll see a cell.
Now how did we get here?
Gregor Mendel “The Father of Genetics”
It’s the mid-1800s. We’re in a monastery, observing Gregor Mendel. He was an Austrian monk. Mendel was conducting experiments on pea plants in the garden of his monastery. Mendel discovered that each pea has two copies of a gene for any trait (one from each parent), and these two gene copies separate when making sperm or egg cells. Each sperm or egg just gets one copy.
Imagine a pea plant with two types of flowers: purple and white. The plant has two flower genes: one for purple and one for white. When this plant makes pollen or egg cells, it passes on just one of these flower genes to each cell. This was the first time we got a hint that life passes down information to it’s offspring through generations.
From Chromosomes to DNA to Gene Editing
Mendel’s work was rediscovered by some folks in the early 1900s. That’s when it was proposed that genes are located on chromosomes. Thomas Hunt Morgan and his colleagues came through in the 1910s to prove experimentally (using fruit flies) the chromosome theory of inheritance. He proposed that genes located close to each other on the same chromosome tend to be inherited together.
Now, fast forward to the post-war era in the mid-1940s. A few scientists proposed that the stuff that genes and chromosomes were made of was something called DNA. They did this by infecting bacteriophages (virus) which are composed of DNA and proteins, into bacteria. Their DNA enters the host bacterial cell, but most of their proteins did not.
In 1953 you have the most famous discovery in biology, when Watson, Crick and Franklin discovered the double helix structure of DNA. Now that sheds light on how genetic information is stored and transmitted. In the 60s we deciphered the genetic code, how sequences of DNA and RNA were translated into proteins — the building blocks of all life. That naturally brought us to manipulation of genes which created the genetic engineering and biotech industries. In 2003 we mapped the entire human genome, identifying and sequencing all of the genes in the human DNA. It cost nearly $3 billion and required collaborative effort from the entire world. But that’s half the story — we mapped it, only to discover that the majority of our genome is non-coding or “useless” as it’s termed. This left us even more confused. Then in 2009 we invented CRISPR, making it way easier to edit DNA.
And yet. People still die of cancer and all sort of diseases.
But then a subculture of biologists, computer scientists and philosophers looked at this with a different lens and asked some mind boggling questions.
PS: There have been ideas and concepts from the time of antiquity (Plato, Aristotle) till today. I haven’t included that and these are only the cinematic highlights. There have been a bunch of work before, in-between, and in recent period that have been “ignored” in this piece. This just provides us with a high-level timeline of genes.
What If We Looked at This All Wrong?
Genes. Gene editing. “Scientists have discovered a new gene [Insert complicated gene name] that might be the key to [Insert something clickbaity]”. Everything you hear about is in regards to DNA, genes, proteins — how the promise of eliminating human ailments can be found there. We got no fucking clue on what these things really do and 0 clue on what the implications are. It’s been 15 years since we discovered CRISPR. So what? We still have no idea what implications any of these DNA changes will have upstream.
It’s as if we’re trying to understand how “God” created all the building blocks and how all of these building blocks act on each other — work together in various pathways. That’s fucking complicated. And my god it takes a long time to crack. With each iteration someone else found another gene, or pathway that might be the end-all. What if “God” doesn’t even know how all of this works together? What if he just has access to the language, the nudge to get the micro units to accomplish their goals?
As you might have observed in that timeline of the history of genes, most things have been discovered — not created or engineered. We are blind in biology, most of what we call biology are just various humans doing work on something for long stretches of time discovering something by random. Bringing engineering into the bio mix is key.
Hardware vs. Software
Now what I am about to postulate has come from the great minds of unorthodox scientists — Michael Levin as the current prime leader today. He has connected the dots of people before him again, but to put a face to the renaissance of biology I am singling him out.
The problem is that we’ve been messing around with the “hardware” of life — the genetic code, the building blocks of it all. A human brain will not be able to map out of all the different paths, the changes in one gene to see what it does upstream. Not even a generation could that. Maybe with AI. However, it will take a long time. Time we don’t really have.
Think of this (might not be the best analogy), what if every time you wanted to turn on or off the light you had to get into the electronics and the wiring — pull up an instruction manual to do so. Or if you want to switch from Photoshop to Microsoft Word you open up your computer, start getting into the microchips, the transistors and start re-wiring your computer. The margin of error would be incredibly high and it would take ages. But that’s not what you do, you just tap a button or a switch to turn on or off the light. And you just switch from one application to another on your computer. The tool accomplishes the goal for you.
Our genome nails down the hardware our cells have: the proteins, the signalling components, and the computational components. Once you provide the smallest unit of organism (cells) the hardware it’s now ready to run, to solve it’s own problems — to get energy and divide. It can start solving a few problems in it’s own little space.
Relating Back to Cancer and Human Diseases
As we’ve examined in previous posts, bioelectricity is the language of cells. These micro units with their own little hardware when combined together create a form of collective intelligence that tries to accomplish various goals — create an arm here, create an eye here — and then it knows when to stop (we wouldn’t be here if it didn’t).
Rather than looking at the hardware, the individual components, we ought to study the software — the bioelectric voltage patterns in and amongst a collective of cells. For example, we might think we’ve identified some mutation in genes that we think cause cancer and we’re trying to modify the DNA of that gene(s). But upstream it’s messing up the software which causes other horrendous problems which we couldn’t predict off the bat. It’s too complex.
Alternatively, what if we understood the language of cells — bioelectric voltage patterns that control shape and functions? We could provide the right type of stimuli to get the micro organisms (cells) to fall in line, to do the work for us. An anatomical compiler. You provide the goal, it’s translated to bioelectric voltage patterns, stimuli is provided to your cells, and the cells accomplish the goal for you (overriding any genetic default settings). This is bio-engineering. This is how humans truly becomes the master of life. Fuck random discoveries.
What’s the Challenge and Future Directions?
As I started to dig into this I identified the challenges and future directions of Levin’s lab, highlighting a few:
tools for AI-enabled bioinformatics of shape
software models of scaling of cognition across problem spaces
cracking the bioelectric code (mapping of voltage prepatterns to anatomical behaviours of cell groups)
detecting, preventing, and reprogramming cancer bioelectrically
Now the problem is that we’ve done a lot of work in the genes landscape and we understand this quite well in comparison to bioelectricity. There are open source databases on genes and there is even AlphaFold which does predictions in protein structures.
However, there is little data on bioelectric code and the data that exists is fragmented across various papers and publications. It isn’t centralised in a place to easily start doing modelling work. With my limited knowledge on this stuff I think there might be some work on aggregating all of this existing data and creating more of it via experiments. I am still trying to crack how one could utilise AI in simulating any of these, but without data to go on it’s tough (I think). There might be some adjacent datasets or something I am too slow to think about that we could leverage.
This got me thinking. Cancer is the goal to solve, and as an extension all other diseases. (The reasoning is that solving cancer would send such a ripple effect in the world that I believe capital and resources would then be placed in cracking the bioelectric language for all other human ailments).
But a “smaller”, less intrusive problem to solve could be hair loss in humans. Ultimately, all things in our bodies are made of cells. I wonder if we could understand the disturbance in the bioelectric patterns when humans loose hair and start balding. Could we add some form of stimuli to correct this and see if it actually starts regrowing hair? Could be some interesting experiments and might also be easier to do these studies. Pondering…
That’s it. I really got worked up when writing this. Most of the world are genes maxists. This is what we collectively believe in. But as explained here, there might be another way. Something that would upend biology and the sciences. I think we might be at the cusp of a renaissance in biology — truly moving into bioengineering.
Happy Sunday - Thank you for reading and catch you next week,
Lush