# Questions on computing FI in cancer

If your computation were true, wouldn’t be the case that kidney cancer would be much, much more frequent than lung or colon cancers?

Yes, if FI had much to do with the difficulty of evolving something, but it doesn’t. That’s the point of the exercise. FI just doesn’t give us that information. Period.

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Is it your contention that kidney cancer requires 2 specific mutations whereas lung cancer requires
6?

Nope.

Then, I am confused.

My contention is that the FI of those different cancers is approximately those numbers of bits. I did use simplified calculations, but I also know that the simplifications don’t change things much.

As for the number of DRIVER mutations (not specific mutations), those numbers are from a specific review study, and are only approximate. We can derive better estimates directly from the data if we like.

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If the driver mutations are not specific, why did you say the following about them ?
It takes about 31.5 bits of information to specify each mutation. We obtain this by computing the log base 2 of the human genome size, which is about three billion bases long

There are multiple possible driver mutations, and only specific sets will give rise to mutations.

Even though there are multiple possibilities, it doesn’t deduct the FI computation by much, perhaps 3 to 5 bits, so I neglect it here. Moreover, ID calculations of FI don’t take this into account, so I’m not sure why I’d have to do so.

And because there are multiple possibilities, drivers are not fully specific.

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I think you are building a stawman for ID theorists would not compute the probability of, say, a lung cell acquiring a cancer phenotype as you do.

Not a strawman.

Using their own formula, there is billions of bits of FI. It was a correction to their math that brought it down to hundreds or thousands of bits.

It is notable that DI did not respond by producing a better calculation (or any calculation) but just made a verbal argument against it.

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Let’s take a normal lung cell (nlc) and a cancer lung cell (clc). We have FI(nlc)>FI(clc). This is because there are more ways for a cell to be a cancer lung cell than to be a normal lung cell.
As a result, no theoretical hurdle exists that prevents a normal cell from becoming a cancer cell by a purely natural process (because the transition involves a net loss of information). The inverse is not true. A cancer cell will never come back to the normal phenotype by a purely natural process because the transition would involve a significant gain of information that only a mind could produce. This is ID thinking I think.

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Thanks for demonstrating my point:

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The FI of a system is -log2[F(Ex)], where F(Ex) is the fraction of all possible configurations of the system that possess a degree of function.
So for a normal lung cell, F(Ex)nlc is the fraction of all possible configurations of a more or less 3x10^9 bp genome that possess a degree of lung function. For a cancer lung cell, F(Ex)clc is the fraction of all possible configurations of a more or less ~3x10^9 bp genome that possess a degree of lung cancer function. Since there are much more ways for a cell to be a cancer lung cell than to be a normal lung cell, it follows that F(Ex)clc > F(Ex)nlc. As a result, the FI of a normal lung cancer is higher than the FI of a cancer lung cell.

All you did was repeat your previous unsupported claim. Try again.

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Are you contesting the fact that the FI of a normal lung cell is higher than the one of a cancer lung cell?

Are you also contesting that the FI of cancer cell is lower than its normal counterpart ?

Yes, of course I am, and I have empirical evidence to show it.

Since there is an inverse relationship between information and Entropy, by contesting the fact that the FI of a cancer cell is lower than the one of its normal cell counterpart, I conclude that you are also contesting that the Entropy of a cancer cell is higher than the one of its normal cell
counterpart. But by doing so, you are totally at odds with the immense body of knowledge accumulated over the last decades in oncology. As proof, you can google the words « Cancer & Entropy »

Actually, there is not an inverse relationship between the two. Entropy is information, as is well known.

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