Cells are computers, organisms are fractals

     Cells are computers, organisms are fractals

Some notes towards a concept. I’ve long thought that the notion that a neuron as an on-off switch was too simplistic. These notes represent an attempt to produce a better notion. 2016-06-03 & 05. WEK.

The metaphor of DNA as blueprint is misleading. Better: DNA is a program guiding the assembly of proteins. Better yet: It’s the operating system, since it’s RNA that produces the proteins. But if DNA is a program, then the question is, How does it execute? The answer: like any program, at any given time some part is running, the other parts are silent. A program can also trigger other programs. The operating system controls how multiple programs run, it allocates memory and CPU time, access to video and audio subsystems etc. A “call” from one program will stop or start some part of another program. An “interrupt” will cause (re-)allocation of memory, access to subsystems, etc. DNA starts and stops protein synthesis, turns genes on and off, analogous to OS controlling program execution. So the cell is a computer

Recent research shows that inputs to the cell “turn genes on and off”, analogous to calls and interrupts controlling how a program runs. The genes control the functioning of the cell. Exactly how is complicated, but the general pattern is chemical feedback loops. A substance increases, which triggers or stops gene expression, which results in a series of reactions, which cause that substance to decrease, which stops or triggers gene expression, and the cycle repeats.

A neuron responds to the chemical environment outside it by adjusting its internal processes. These processes control gene expression. The feedback loops within the neuron determine the types and quantity of neurotransmitters emitted at the synapse with the next neuron in the circuit. Since both type and quantity of neurotransmitter vary depending on the inputs to the neuron, the neuron is computing the output. The concept of a neuron as simple on-off switch is inadequate.

But a cell is an odd kind of computer. The relation between input and output depends on the internal feedback loops. A given substance may be implicated in two or more feedback loops, which means that the neuron is topologically a net. The computation of the output depends on the topology of the net of chemical reactions, which happen both simultaneously and in sequence. That makes the cell a parallel computer.

More precisely, the cell is a net whose topology varies over time as the chemical feedback loops cycle between limiting states and intersect with each other. Thus, the cell cycles through a series of topologies. It’s a self-modifying net.

The concept of a self-modifying net applies to assemblies of cells (tissues), to organs, and to the organism as whole. The organism too is a complex system of feedback loops. Mathematically it’s a chaotic system: it tends to maintain itself within an envelope of states (the attractors). Illness and disease move the system outside the envelope, and recuperation is a return of the system to the dynamically stable cycles within the envelope.

Conclusion: An organism is a multi-dimensional net of feedback loops. Its topology varies over time at many scales, which implies it’s a fractal system.

The Cavalier in White (mystery)

     Marcia Muller. The Cavalier in White (1988) Joanna Stark, partner in a security firm specialising in museums and art galleries, finds herself sucked back into the business when a client’s murder ties into the theft of a Frans Hals painting, Cavalier in White, stolen from a gallery owned by her friends. Much conversation, a second murder, family secrets and the past come together in a nice melange of entertaining characters and plots. The novel often reads more like a Harlequin romance than a mystery. Muller’s Sharon McCone tales are solidly in the PI tradition; this book dances on the borders of the two genres as if Muller couldn’t make up her mind which one she wanted to write. There was one more Joanna Stark novel which I haven’t read. This one is OK for a few hours pleasant entertainment, but only a diehard Muller fan would want to keep it. *½

Colin Dexter, The Jewel that was Ours (Chief Inspector Morse)

 

    Colin Dexter. The Jewel that was Ours (1991) This novel began as a TV script, The Wolvercote Tongue, in which a Saxon artefact figures as the McGuffin. A gaggle of US tourists descends on Oxford, one of them dies, the Tongue disappears, then one of the presenters turns up naked and very dead at Parson’s Pleasure. Morse as usual hares off after the wrong solution until an unrelated datum noticed by Lewis as an odd coincidence triggers the re-arrangements of the facts, which are beautifully summed up in chapters 57-59, and explain the dual meaning of the title. Chapter 60, the last, ties up a loose thread, another amorous disappointment for Morse.
     The novel is easy to read. Short chapters allow interruptions without losing the threads, which are satisfyingly tangled. If you don’t know Morse, the book is a good intro. Still, the whole reads like a potboiler. Dexter has developed a set of tics and tropes that give the fan the comforting sense of a reliably familiar world. We can concentrate on the puzzle if we wish, or just let the TV-derived imagery carry us along. A well-done entertainment, but that’s all. **½

How to study Shakespeare and survive

     Richard Armour. Twisted Tales From Shakespeare (1957) A re-read. The introductions are nicely done parody of what the student reads in school editions of Shakespeare’s plays. Then Armour dissects the six school classics: Hamlet, Macbeth, The Merchant of Venice, A Midsummer Night’s Dream, Romeo and Juliet, and Othello. 

     The jokes are gentle. Armour likes puns, and he clearly has vivid memories of studying the plays, which inform his not-quite-fractured versions of the stories. Anybody who knows the plays will find amusement, those who don’t could do worse to read this book as a first intro to the canon. The best thing is Armour’s satire on the authorship question: It is a contemptible attack on higher education ... to suggest that a person who never went to college could have written poetry that is too difficult for most college students. Precisely so. 

     Recommended. ***

C S Lewis on Hell

C S Lewis wrote: "We must picture hell as a state where everyone is perpetually concerned about his own dignity and advancement and where everyone has a grievance."

Sounds like any large organisation, IOW, a bureaucracy.

Artificial Intelligence: a few musings

2005-06-20
“If it looks like a duck, and walks like a duck, and quacks like a duck, then it’s a duck” (Ancient wisdom)

Unless it’s a model of a duck.

Artificial Intelligence is model building – we want autonomous machines, but the best we can do is build models of autonomous machines.

Eg, an artificial ant – could be made to behave like an ant in many ways, but not as an ant in an anthill, or capable of making more ants.

2015-10-21
It’s probably possible to make an artificial ant that behaves like an ant in anthill. We may even be able to make an artificial ant that can reproduce in some way.

However, “behave like an ant” is not well defined. There are too many behaviours, and some are obviously easier to mimic than others. Nevertheless, it will soon be possible to make an ant-size robot that can navigate like an ant, climb vertical surfaces like an ant, etc.

But it will always be a model of ant, and therefore its behaviour will in some respect will not be antlike, and in other respects will be a bad imitation of ant behaviour. That’s simply the nature of models.

2016-05-15
Intelligence is even less well-defined than “ant behaviour”. We can mimic some intelligent behaviours, eg, sorting, learning correlations, recognising patterns, and so on, which are useful to augment human tasks such as diagnosis of a fault or illness, or finding the data we want. If a task is well enough defined, we can build a machine to do it.

But that’s the problem: “Intelligence” is simply not well enough defined. My notion of it is the ability to apply and adapt existing knowledge and insight to unanticipated problems. Every term in that definition is fuzzy and vague. Anyhow, some people (including me) would argue it’s more of a definition of creativity than intelligence.

Is consciousness part of  “intelligence”? Many people would say it is. A machine that merely solves problems isn’t intelligent, it’s just an algorithm. It’s not enough to know how to do long division, you have to be able to recognise when and why you should do it. And that you are doing it.

“Understanding” is another component of intelligence. Isn’t it? Well, it does have something to do with learning: an intelligent person is one who can make sense of new explanations. “I don’t get it” at one extreme means “I haven’t figured it out yet”, at the other it means “I can’t figure it out”. The latter is a measure of intelligence.

And that’s just three attempts to make sense of “intelligence”. We’re long way from knowing exactly what we mean by “artificial intelligence”. Far enough that we may not even recognise it when we see it.

The arithmetic of repairing a car

Should you trade in an old car or repair it? The accountants tell you that when the cost of a major repair comes in at about the value of the car, it’s time to buy a new one. “It’s costing you more than it’s worth”, they say.

I think that depends on how you look at it. At current prices, replacing an old car with a newer one will cost $200-$400 a month, whether you borrow the money or save for a future purchase in cash. Insurance and licensing will cost the same whether you drive the car or not. Regular maintenance and fuel costs depend on how much you drive. So $200 to $400 over and above those costs buys a month’s worth of car life.

Let’s define a major repair as one that costs at least $400. That’s about the cost of a brake job. So if the brake job lasts longer than a month, you’re money ahead. Buy a rebuilt transmission for, say, $2000. In less than half a year, you’ll be money ahead. And so it goes. Even body work, such as patching holes or a new paint job, can buy you more additional life than the same money spent on a newer car.

I think that repairing (and maintaining) cars to a nearly new level makes huge economic sense. But as a car ages, owners are less and less willing to do that. There are many reasons for that, but to discuss those would make this post far too long.

 

Update 2023: Cost of repair have risen, but so has cost of purchase. So the argument is still valid. FWIW, my current car is 10 years old and drives like new. Repair/maintenance costs have amounted to about two or three months worth of  financing/purchase cost per year.