Difference between revisions of "Cellular automata in biomedicine"
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<b> Click here for more:</b> [http://www.what-is-cancer.com/papers/ca/ca01.htm] | <b> Click here for more:</b> [http://www.what-is-cancer.com/papers/ca/ca01.htm] | ||
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==Related Domains== | ==Related Domains== | ||
<WikiPages> | <WikiPages> | ||
| − | <b> Introduction to Cellular Automata in Bio-Medicine </b> | + | <b> Introduction to Cellular Automata in Bio-Medicine </b>[http://cellularautomat.blogspot.com] |
| − | [http://cellularautomat.blogspot.com] | ||
<b>Streaming Organism </b>[http://streamingorganism.blogspot.com] | <b>Streaming Organism </b>[http://streamingorganism.blogspot.com] | ||
</WikiPages> | </WikiPages> | ||
Revision as of 09:53, 22 June 2007
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Top down and bottom up models
There are two kinds of models, centralized or top-down, and distributed, or bottom-up models. Most physical models are of the first kind. They are governed by top-down laws that control entire systems. None of these suffices to describe even the simplest organism, which is complex and its properties emerge. Traditional mathematical tools fail to untangle life's complexity. We may distinguish between two kinds of complexity linear and non linear. Only the first can be resolved with traditional mathematical tools like logic, or induction. Life's complexity is non linear. Artificial life
Take the Mandelbrot set. It is extremely complex and its properties emerge. It cannot be resolved with traditional logic. You may discover some generalized rules or mappings that underlie its structure, like fractal geometry, but these cannot be regarded as top down rules. They describe the set, and do not control it, since it is generated by a bottom-up iteration.
The Game of Life, a bottom-up model, is even more unpredictable than the Mandelbrot set, since it lacks a geometry or mappings which might summarize (or simplify) its emerging structure .Despite its name, the Game of Life does not capture the behavior of the simplest life form. Neither do Artificial Life (AL) models, like Neural Nets, or Genetic Algorithms. They generate extremely complex structures whose properties emerge unpredictably, nevertheless they do not capture a profound attribute of life, which is oriented turnover.
Life is an oriented change. Like a river that flows in one direction. Yet even a river could not serve as an adequate model for life, since its water is carried to the sea as such and does not change, while the ingredients of life continually transform. Fire might be regarded as best metaphor for life. It is born in the burning wood. As it raises upward, its color continually changes, from yellow to red, and blue. None of AL models can simulate a fire, neither a river, and yet some serious scientists claim that these simplistic models are a form of life, life in silico. Cellular automata
And here comes Wolfram's book "A New Kind of Science" (1). Might cellular automata (CA) simulate life better than other AL models? Chapter 8, "Implications from Everyday Systems" is an attempt to show that CA are indeed the best tool for simulating life. Yet the examples are less convincing. Like the fractal nature of plants and animals (p. 400). Life is more than that, since it defies any geometry. Or the implicit adoption of the central dogma of molecular biology according to which there exist a linear mapping from genotype to phenotype.
One is impressed with Wolfram's success to simulate a river (p.376), which cannot be done with other AL tools. Or the oriented change of a fire. What about life? CA are infinite and immortal, while life is not. They consists of simple geometrical structures like triangles, while life is amorphous. Above all CA lack an essential ingredient of life, oriented turnover.(streaming) Why not augment CA so as to portray this property of life?
Biological age
In the present study every CA is endowed with a biological age. A death mechanism eliminates the old and spares the young. You plant a single CA, called zygote, whose age is zero. Select a rule and a death mechanism, , and start iterating in the same way as described in Wolfram's book. Cells age and ultimately die, and you confront an artificial creature which simulates life. You watch it grow and respond to stimuli and start contemplating some biomedical concepts.
The experiments described herewith are designed for evaluating concepts which apply to real life. Like what causes change? Or what is creativity?
Here are some concepts which were evaluated on CA models:
- 1. Medicine: Health , disease, immunity, injury and repair , and infection.
- 2. Biology: Biological and chronological time, biological age, and evolution.
- 3. Molecular Biology: Cloning , and knockout genes.
- 4. Philosophy: Creativity , Aristotle's four causes , Kant's synthetic a priori statement , and recollection.
- 5. Computer: Distributed memory. Massively parallel non linear computer.
References
1. Wolfram S. A New Kind of Science ISBN 1-57955-008-8
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