Here, one of my brilliant MD PhD students and I study one of the “information” arguments against evolution. What do you think of our study?
I recently put this preprint in biorxiv. To be clear, this study is not yet peer-reviewed, and I do not want anyone to miss this point. This is an “experiment” too. I’m curious to see if these types of studies are publishable. If they are, you might see more from me. Currently it is under review at a very good journal. So it might actually turn the corner and get out there. An a parallel question: do you think this type of work should be published?
I’m curious what the community thinks. I hope it is clear enough for non-experts to follow too. We went to great lengths to make the source code for the simulations available in an easy to read and annotated format. My hope is that a college level student could follow the details. And even if you can’t, you can weigh in on if the scientific community should publish this type of work.
Functional Information and Evolution
“Functional Information”—estimated from the mutual information of protein sequence alignments—has been proposed as a reliable way of estimating the number of proteins with a specified function and the consequent difficulty of evolving a new function. The fantastic rarity of functional proteins computed by this approach emboldens some to argue that evolution is impossible. Random searches, it seems, would have no hope of finding new functions. Here, we use simulations to demonstrate that sequence alignments are a poor estimate of functional information. The mutual information of sequence alignments fantastically underestimates of the true number of functional proteins. In addition to functional constraints, mutual information is also strongly influenced by a family’s history, mutational bias, and selection. Regardless, even if functional information could be reliably calculated, it tells us nothing about the difficulty of evolving new functions, because it does not estimate the distance between a new function and existing functions. Moreover, the pervasive observation of multifunctional proteins suggests that functions are actually very close to one another and abundant. Multifunctional proteins would be impossible if the FI argument against evolution were true.
I am working on a series of tutorials to cover the basics of Intelligent Design, especially the mathematics of it. This is my tutorial on Specified Complexity, and I would appreciate any thoughtful criticism of it.
Given the importance of information theory to some intelligent design arguments I thought it might be nice to have a toolkit of some basic functions related to the sorts of calculations associated with information theory, regardless of which side of the debate one is on.
What would those functions consist of?
In the “Elon Musk” discussion, in the midst of a whole lotta epistemology goin’ on, commenter BruceS referred to the concept of a “Boltzmann Brain” and suggested that Boltzmann didn’t know about evolution. (In fact Boltzmann did know about evolution and thought Darwin’s work was hugely important). The Boltzmann Brain is a thought experiment about a conscious brain arising in a thermodynamic system which is at equilibrium. Such a thing is interesting but vastly improbable.
BruceS explained that he was thinking of a reddit post where the commenter invoked evolution to explain why we don’t need extremely improbable events to explain the existence of our brains (the comment will be found here).
What needs to be added is that all that does not happen in an isolated system at thermodynamic equilibrium, or at least it has a fantastically low probability of happening there. The earth-sun system is not at thermodynamic equilibrium. Energy is flowing outwards from the sun, at high temperature, some is hitting the earth, and some is taken up by plants and then some by animals, at lower temperatures. Continue reading
TSZ has made much ado about P(T|H), a conditional probability based on a materialistic hypothesis. They don’t seem to realize that H pertains to their position and that H cannot be had means their position is untestable. The only reason the conditional probability exists in the first place is due to the fact that the claims of evolutionists cannot be directly tested in a lab. If their claims could be directly tested then there wouldn’t be any need for a conditional probability.
If P(T|H) cannot be calculated it is due to the failure of evolutionists to provide H and their failure to find experimental evidence to support their claims.
I know what the complaints are going to be- “It is Dembski’s metric”- but yet it is in relation to your position and it wouldn’t exist if you actually had something that could be scientifically tested.
Michael Behe is best known for coining the phrase Irreducible Complexity, but I think his likening of biological systems to Rube Goldberg machines is a better way to frame the problem of evolving the black boxes and the other extravagances of the biological world.
On the left is a photograph of a real snowflake. Most people would agree that it was not created intentionally, except possibly in the rather esoteric sense of being the foreseen result of the properties of water atoms in an intentionally designed universe in which water atoms were designed to have those properties. But I think most people here, ID proponents and ID critics alike, would consider that the “design” (in the sense of “pattern”) of this snowflake is neither random nor teleological. Nor, however, is it predictable in detail. Famously “no two snowflakes are alike”, yet all snowflakes have six-fold rotational symmetry. They are, to put it another way, the products of both “law” (the natural law that governs the crystalisation of water molecules) and “chance” (stochastic variation in humidity and temperature that affect the rate of growth of each arm of the crystal as it grows). We need not, to continue in Dembski’s “Explanatory Filter” framework, infer “Design”.
I see long-time commenter at Uncommon Descent, Mung, in a thread entitled Backwards eye wiring? Lee Spetner comments, asks:
How do you calculate the size of amino acid sequence space?
As this seems somewhat off-topic there, I thought I’d attempt to answer Mung’s question. I’ll try and be brief. Continue reading
I recently came across this book:
Biosemiotics: Information, Codes and Signs in Living Systems
This new book presents contexts and associations of the semiotic view in biology, by making a short review of the history of the trends and ideas of biosemiotics, or semiotic biology, in parallel with theoretical biology. Biosemiotics can be defined as the science of signs in living systems. A principal and distinctive characteristic of semiotic biology lies in the understanding that in living, entities do not interact like mechanical bodies, but rather as messages, the pieces of text. This means that the whole determinism is of another type.
Pardon my skepticism, but
- There is no information in living systems.
- There are no codes in living systems.
- There are no signs in living systems.
Biosemiotics is the study of things that just don’t exist. Theology for biologists.