You, by which I mean all the mammals in the audience, are running on an operating system a couple of billion years old.
Your genome is full of primordial libraries, monkey patches, self-modifying code, viral hacks and even containers running a different operating system.
This talk is all about the freakish parallels between cell biology and computer architecture.
My name’s Nick Moore, I’m a consultant, working in software development and systems architecture. I’ve been working with computers for a very long time and Python for a fair proportion of that.
Recently I’ve been doing some work in bioinformatics. Bioinformatics is the study of biological systems using numerical analysis. Biological systems are pretty complex, so typically this analysis requires computers. I’m been lucky enough to get involved in that part.
Thanks to my colleagues at Walter and Eliza Hall Institute of Medical Research and at the University of Washington Genome Sciences for their patience while I’ve found my feet in this field.
Mistakes and oversimplifications are all mine.
The one true rule of biology is that all rules have exceptions, and those exceptions have exceptions, and it’s exceptions all the way down. So some of this talk will oversimplify complex issues and processes but hopefully it’ll be enough to give you a general idea and some inspiration to find out more.
It’s not entirely surprising that cellular biology and computing are a bit intertwined given their overlapping history of discovery:
We’ve had written language, expressed as a sequence of symbols, for several thousand years.
But as our understanding of inheritance developed it became apparent that genetic data was also structured as a sequence of smaller units.
Slowly we came to realize that the kind of linear symbolic information processing we were developing to solve
We’ve learned a lot about cellular biology while we’ve been working out computing, but evolution has billions of years of head-start on us.
Computing has taken off exponentially, and so has genetic sequencing. In 50 years, we went from the discovery of the structure of DNA to having reference sequence of the entire human genome which you can download for free.
The personal computing era got started when enthusiasts mailed each other printouts, punch tapes, cassettes, floppy discs, whatever it took to share their advances.
Early genetics enthusiasts discovered that drosophila melanogaster, a kind of fruit fly, could be caught easily just about anywhere in the world, selectively bred and exchanged with other experimenters to increase the understanding of inheritance.
picture of a cell
The basic unit of life is the cell.
Primitive organisms like bacteria are unicellular, each organism is one cell and each cell is one organism, and they reproduce by copying their innards and splitting in two.
A single cell is a little bit like a tiny computer.
There’s an outer cell membrane which keeps the insides in and the outsides out. Inside there’s a bunch of programs called genes which tell the cell how to make proteins, which are the working machinery of the cell.
The cell interacts with the world through channels in the cell membrane, which are kind of like ports which selectively let specific molecules in and out of the cell.
Picture of DNA
Genes, the programs of the cell, are encoded as chromosomes, very long DNA molecules not unlike a tape. DNA is built up out of four “bases”, Adenine, Cytosine, Guanine and Thymine, generally just abbreviated as A C G and T.
| base | complement |
|---|---|
| A | T |
| C | G |
| G | C |
| T | A |
They zip together in pairs, A complements T and C complements G, so we generally refer to this smallest piece of genetic information as a “base pair”. This is the unit we’ll use to compare genome sizes. Because there’s four possible pairs each “base pair” is equivalent to 2 binary bits of information. So “1 million base pairs” that means 2 megabits of information.
Cells picture
Bacteria have relatively small genomes, typically a single circular chromosome of hundreds of thousands through to a million or so base pairs. The human genome by contrast has about 3 billion base pairs, and each of us have two copies spread over multiple chromosomes.
We’re a lot more complicated than a bacteria, but there’s a species of lungfish with 130 billion base pairs and an amoeba with 670 billion base pairs. So who’s counting?
Unicellular organisms reproduce by copying, and errors arise during the copying process, sometimes leading to novel features in the cells. Good features thrive, bad features dwindle, and that’s natural selection, leading to evolution.
Copying chromosomes can make simple mutations, kinda like typos, and these can also cause a gene to be shortened or lengthened or split into pieces or fused with another gene. Bigger errors can also result in multiple copies of a gene appearing, and then these multiple copies can evolve in different directions.
On top of this, pesky things like retroviruses can introduce new genes entirely, effectively writing themselves into a cell’s genome to get the cell to make more retroviruses.
It’s pretty rare that any of these changes are helpful. But over evolutionary timescales, the few helpful changes add up.
slide: plasmids
Bacteria also exchange genes with different bacteria or even other organisms, by passing plasmids, which are like mini chromosomes. This is called horizontal gene transfer.
It may seem counter-intuitive to give away your code to your competitors, but consider: the thing evolution is optimizing here is not the organism, but the gene. If a gene which confers, say, antibiotic resistance gets copied into a new organism and that organism thrives, then the gene is successful even if the old organism is out-competed by the new. The gene will get passed on to more organisms.
Yep, Free Software is billions of years old, and there’s been a whole lot of cutting and pasting from the primordial script archive.
There’s no documentation or source control, but by looking at the common features of all cellular life we can hypothesize a “univeral common ancestor” which arose about 4 billion years ago, and from which all life is descended.
central dogma slide
DNA --transcription--> RNA --translation--> Protein
The “source code” of the genome is DNA, but DNA is just a stable storage for the genome, it doesn’t actually do anything. Instead, DNA is transcribed into RNA, and then RNA is translated into proteins. This is often called the “central dogma” of cellular biology.
add some arrows for replication, retrotranscription, ncRNA
There’s also cell replication to consider. Retroviruses can translate RNA back into DNA so we’d better include that There’s also non-coding RNA which functions directly rather than being translated into a protein first.
add RNAP arrow back from proteins to DNA->RNA edge
Transcription from DNA to RNA is done by a protein complex called RNA polymerase (RNAP). This little machine unzips the DNA and assembles an RNA molecule, but RNAP is itself made of proteins.
Proteins are produced from genes, so to build the transcription mechanism we first have to run the transcription mechanism …
add arrows for splicing
There’s also splicing to consider. DNA is translated into RNA but that RNA isn’t in it’s final form, first it needs to be spliced. This is done by, yep, more ncRNA and more proteins.
add arrows for ribosomes and tRNA
Translation isn’t simple either. A protein is a long chain of amino acids, which is built up by a complex molecular machine called a ribosome, built from ncRNA and proteins …
So, this is pretty simple, right? Before we can compile the compiler, we need to compile the compiler.
In computing, we call this bootstrapping. You start off by using very primitive tools, possibly even a pencil, to create a very simple first compiler, and then using that compiler you can build a more sophisticated compiler, and so on.
All of this only works because the oocyte, the egg you grew from, contained enough of these mechanisms to get the whole process started. Kind of a boot disk. You can think of these genes, as an “operating system” which the rest of a cell’s biology is implemented on top of.
standard coding table
The mapping from RNA to protein is done by transfer RNA (tRNA). Groups of three bases called codons correspond to different tRNAs which each bring an amino acid molecule to add onto the protein.
This table shows the “standard code” which maps codons to amino acids. Not all organisms have the exact same code: this table isn’t a law of nature. It’s a result of what tRNA happen to be around, and tRNA is produced from the genome, so this translation is happening “in software”.
It somewhat resembles the instruction decoding tables used in microprocessors — there’s some redundancy where 64 codons translate to 20 amino acids. Translation starts at a “start” codon and finishes when it reaches a “stop” codon.
But there’s no looping or branching or I/O or whatever, so how is this like a program?
Well, genes are not expressed equally. When genes are packed into a chromosome, that’s kind of like a library. There’s some header information before and after each gene, known as the “Regulatory Sequences”. These affect the way RNA Polymerase attaches to DNA, and how Ribosomes attach to RNA. Genes, or groups of genes, can be promoted or suppressed. Splicing can be suppressed or altered to produce different proteins.
All this is under the control of the molecules within the cell, which themselves exist through the action of other genes or from external stimuli.
If each gene is a statement, the cell’s “program” is found in the interaction between those statements. And those interactions are extremely complicated, and not yet well understood.
At some point about 3 billion years ago, a particularly entrepreneurial branch of the Archaea developed a more sophisticated internal cell structure. These are the Eukaryotes.
Animals (including humans), plants, fungi, algae and slime molds are all Eukaryotes.
The Eukaryotic genome is protected inside a nucleus, and specialist organelles perform specific functions within the cell. They’re linked together by the cytoskeleton, a network of filaments and tubes which spans the inside of the cell.
You can think of these as peripheral controllers or coprocessors supporting the main processor, linked by buses. DNA to RNA transcription occurs in the nucleus, and then RNA to protein translation occurs outside the nucleus, in the endoplasmic reticulum which surrounds the nucleus.
Some of these organelles, the mitochondria, appear to once have been independent Proteobacteria, which were engulfed by the proto-Eukaryotes and instead of being destroyed they were put to work.
Mitochondria are like little containers with their own separate genome. The cell benefits from their ability to produce ATP, and the mitochondria benefit from the cell’s protection.
But they’re effectively running their operating system, like little containers within the host cell, and they have their own replication and protein synthesis. They even have their own tRNA, and as a result use a slightly different translation code to the host cell. They’ve been engulfed but they’re still separate and running their own OS.
We like to think of vertebrates as the pinnacle of evolution but plants have done this twice! In addition to harnessing proteobacteria as mitochondria, plants have harnessed photosynthetic cyanobacteria as chloroplasts. Also a species of algae has been discovered to have harnessed another cyanobacteria to let it fix nitrogen[^nitroplast].
[^nitroplast] https://en.wikipedia.org/wiki/Nitroplast
You’re probably familiar with the phrase “Embrace, Extend, Extinguish”[^eee] referring to the way proprietary systems can use and then destroy open ones. This may be happening here too: there’s evidence that the functions in mitochondria are slowly migrating into the nuclear DNA and being lost from mitochondria.
[^eee] `https://en.wikipedia.org/wiki/Embrace,_extend,_and_extinguish
Most (not all) Eukaryotes are multicellular, an organism is made up of many, many cells. Every cell in an organism has the same DNA, the same programming, and what role it ends up performing depends on how it is specialized.
You can think of a the DNA as a container image which gets specialized at runtime. The cells in your kidneys contain all the instructions needed to be a brain cell, they just don’t use the brain-specific ones, and vice-versa.
Okay, great, so biology is hard. Well, mostly squishy. Let’s have a look at a couple more weird overlaps.
print() slide
Debuggers are nice, but who here has ever resorted to print() (or printf() or console.log()` or &c) just to find out if a bit of code is even running?
LED slide
The embedded software equivalent is the LED.
Things go wrong quickly in embedded code, and your CPU can halt and restart faster than the first H in Hello, World! can make it out the serial port at RS-232 speeds.
So if you want to know where your code is getting to, why not light a series of LEDs, one at a time?
fluoresence slide
Debugging isn’t easy in biological systems either.
RS-232 is actually fairly modern by comparison.
Thankfully we have an unlikely ally in the jellyfish.
Jellyfish have a gene GFP which codes for a fluorescent protein.
This gene can be fused with a gene of interest and then when that gene is expressed, so is the fluorescent protein.
So cells which express that gene, glow!
This can be used to sort cells by activity.
Messenger RNA, or mRNA has been in the news a lot recently, for all the wrong reasons. A very large proportion of us here today will have benefited from mRNA vaccine technology and a lot of us will get to benefit further as research continues into mRNA therapies.
Your immune system is very effective, but also kind of slow to anger. If you can give it a hint about what things to look for it can respond much more rapidly and effectively.
That’s called vaccination. Historically it’s been done using similar but less deadly infections, such as cowpox inoculations for smallpox, or by introducing an attenuated, inactivated or dead organism for our immune system to learn from without risk of infection.
Ideally we’d like to be able to target vaccination more accurately and develop new vaccines more effectively. This can be a tricky thing to manage.
Cells don’t just read any old mRNA they find laying around. Instead, stray mRNA will induce an immune response and get cleaned up by your immune system — get caught your body’s spam filter, in other words.
Instead, mRNA has to get smuggled into the cell with a bit of a bait-and-switch … just like spam:
Ηο𝚝 Ꮮսхս𝚛у ꓪа𝚝сℎ℮ꜱ
ıո γοᴜⲅ ⍺𝖗ҽа
None of those letters are what they seem, instead they’re a smattering of letters from Armenian, Cyrillic, Greek, Turkish and other alphabets and some other symbols and smallcaps thrown in [^1][^2]. This can help sneak messages past your spam filters while still remaining human readable
[^1] thanks to https://util.unicode.org/UnicodeJsps/confusables.jsp
[^2] this isn’t just a problem with humans either, see https://embracethered.com/blog/posts/2024/hiding-and-finding-text-with-unicode-tags/
Similarly, substituting naturally occurring alternate “characters” such as pseudouridine for uridine in RNA can prevent an immune response to the mRNA, allowing it to be taken up by cells.
| nucleoside | modified nucleoside |
|---|---|
| uridine (U) | pseudouridine (Ψ) 5-methyluridine (m5U) 2-thiouridine (s2U) |
| adenosine (A) | N6-methyladenosine (m6A) |
| cytidine (C) | 5-methylcytidine (m5C) |
Like the unicode characters, the modified nucleosides are different enough to evade spam filtering but similar enough to be “read” by RNA polymerases and used to make proteins, and those proteins go on to produce the desired immune response.
The technique of using pseudouridine to smuggle mRNA vaccines into cells[^3] was pioneered by Katalin Karikó in 2005, used in the Pfizer and Moderna COVID vaccines in 2020 and won Karikó a Nobel prize in 2023.
[^3] https://pmc.ncbi.nlm.nih.gov/articles/PMC2775451/