Patrick
December 31, 2018, 6:14pm
1
My background is in Information theory. I looked into @EricMH ID Information Theory and found it to be nonsense. I am still trying to see how Information Theory can be used to study biology. Sure you can call the natural process when DNA “codes” how to create proteins but I don’t see any evidence of ID in it, just purely an evolved natural chemical process.
4 Likes
EricMH
(Eric Michael Holloway)
January 2, 2019, 9:28pm
2
I’m always happy to hear any specific errors you have found.
1 Like
Let’s start with information = entropy. What are the mathematical formulas for information and entropy?
1 Like
swamidass
(S. Joshua Swamidass)
January 2, 2019, 9:57pm
4
I respect the openness @EricMH . I’ll let you and @patrick hash it out here, and plan to largely (completely?) stay out of this one.
EricMH
(Eric Michael Holloway)
January 4, 2019, 2:51am
5
Let’s grant the formulae are the same. What is the conclusion?
1 Like
Ok now we are getting some where. Given that we start with Shannon’s 1948 paper, what are your conclusions about information/entropy in biological systems?
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EricMH
(Eric Michael Holloway)
January 4, 2019, 10:22am
7
I don’t know much about biology so cannot really say.
Do you have some specific error in mind that you spotted in my previous discussions here? If not, I’ll have to be on my way.
1 Like
Mercer
(John Mercer)
January 4, 2019, 3:08pm
8
Is there something preventing you from learning about biology?
So you don’t know much about biology, you just know all the world’s biologists are wrong about biological evolution. Tell us again why anyone should take your claims seriously?
You might be interested in an article written by Thomas Schneider.
Nucleic Acids Res. 2000 Jul 15;28(14):2794-9.
Evolution of biological information.
Schneider TD(1).
How do genetic systems gain information by evolutionary processes? Answering this question precisely requires a robust, quantitative measure of information. Fortunately, 50 years ago Claude Shannon defined information as a decrease in the uncertainty of a receiver. For molecular systems, uncertainty is closely related to entropy and hence has clear connections to the Second Law of Thermodynamics. These aspects of information theory have allowed the development of a straightforward and practical method of measuring information in genetic control systems. Here this method is used to observe information gain in the binding sites for an artificial ‘protein’ in a computer simulation of evolution. The simulation begins with zero information and, as in naturally occurring genetic systems, the information measured in the fully evolved binding sites is close to that needed to locate the sites in the genome. The transition is rapid, demonstrating that information gain can occur by punctuated equilibrium.
Evolution of biological information - PMC