DNA is widely known as the code of life. Consisting of four nucleotides that form a form of digital code that contains all the instructions to make and run a particular Organism. A typical genome for complex organisms such as humans can have billions of bits of information. This represents a problem not only for a naturalistic origin of life, but the entire idea of universal common descent as well. Information theory focuses primarily on the transmission and reception of information, but there are also matters of the content.
The number one problem with regard to DNA and information theory is that all known information coding systems, as well as complex, specific Information comes from only one source, and that is an intelligent mind. This is a huge problem for any ideas of a naturalistic origin and progression of life.
The Provocative Challenge
We’ve been told for decades that random mutations are the engine of life’s complexity, but information theory tells a much different story. In any other field of science, stochastic noise destroys data; it never writes code. Is it possible that the very mechanism supposed to build us is actually tearing us apart? If you are randomly swapping letters in a computer program you would never expect to upgrade to a more advanced operating system. Sometimes random changes can prevent a problem by interference, causing a specific benefit. However, it is always a net deterioration, It just has a benefit in that specific situation.
The Logical Paradox
If you randomly swapped letters in a computer program, would you ever expect it to be upgraded into a more advanced operating system? Shannon’s logic proves that the entropy change resulting from noise only moves in one direction: toward decay. When we apply the laws of information science to the human genome, we see why random noise cannot create life. Random changes can be helpful under a specific circumstance, but they are a problem outside of those circumstances. In other words, even a local mutation can be beneficial in specific circumstances. This is one reason why universal common descent does not really work.
Breaking the Filter
DNA is the densest, sophisticated information storage system in the known universe, yet we treat its origin like a series of fortunate accidents. Natural selection acts as a filter, but a filter can’t create the water it cleans. Instead, the laws of mathematics suggest our genetic code is fighting a losing battle against entropic noise. Now it is possible to create an information filter that does turn noise into complex specific information, but that information needs to be encoded in the filter to some degree. Furthermore, the broader that information the broader the results are going to be. For example, if the filter uses a specific phrase, you will get that phrase, however if you simply filter for readable words, you will get a random jumble of words. Natural selection, however, is way too broad to serve as a filter of anything but deadly mutations.
The Genetic Hard Drive
It is hard to visualize the sheer density of data stored within a single strand of DNA compared to modern silicon storage. For example, all the nuclear DNA in a human, by itself contains over a billion bits of information. That is 1 GB in a space far smaller than even a single bit of storage of a hard drive or solid-state drive. When you add all that up, we can fit into the volume of an average hard drive over a Billion terabytes It gets even harder when you see these biological instructions as a high-fidelity language rather than a simple chemical coincidence. The fact is that the data is not just stored in a dense storage system that it is a highly sophisticated language that can be read in many ways. The simple fact is that there is no real way that a series of typos could ever write a cohesive software program, you will not even have the simplest type, let alone the highly complex code that is found in DNA.
Shannon Logic and the Noise Floor
Claude Shannon’s Information Theory is central to understanding how information is stored and transmitted. One of its most important aspects is explaining how noise inevitably corrupts transmitted signals. Even a minimal level of functional information with little complexity can show that random sequences lack instructional value. Enough noise will inevitably overrun the signal. But what about natural selection, the evolutionists would say? If they think about it, they imagine it as a filter, but a filter has to know what it is looking for with a good degree of specificity to recover a signal from a lot of noise. The actual selection is way too broad a process. Ultimately, mutations are what we can see as biological static; they are the primary Enemy of good, healthy genes, not their source.
The Filter Without a Pencil
Now, natural selection does effectively remove the "worst" mistakes, but it cannot write new complex, specific information above noise, let alone whole new chapters. Filters are always a subtractive process that relies entirely on a pre-existing library of functional code. In fact, many noise reduction programs are based on a sample of the sound without any noise, so as to know what to get rid of. There is a huge difference between surviving a change and the mathematical impossibility of inventing a new biological system through errors. Natural selection is way too broad a process to do the job that is necessary to make new specific information from the noise of mutations.
Entropy in the Genome
In thermodynamics, entropy is related to the number of equivalent states of the system. The higher the number of equivalent states, the higher the entropy, in a logarithmic relationship. In DNA, the accumulation of near-neutral mutations that bypass natural selection will slowly degrade the system. This is because these mutations have more equivalent states than what they are mutating from. Applying the Second Law of Thermodynamics to digital information shows that unguided system changes always trend toward disorder. This is because, like in the second law of thermodynamics, energy is being applied to the system result in more equivalent states when the original system. It has also been demonstrated in recent genomic studies that rapid mutational loads lead to fitness decay rather than innovation. They should actually be expected by anybody with knowledge of information theory.
The Search for the Source Code
Random probability distributions are patterns analyzed statistically but cannot be precisely predicted are mathematically insufficient to account for the leap from noise to high-level data. This is a huge problem not just when trying to produce the original genetic code but also when trying to develop new variations of that code. So, can you think of any other known area of science that accepts randomness as a creator of complex software or complex structures? Any system based on random changes that claims to produce complex, specific information usually has a filter based on such information as part of the filter for it to work.
Conclusion
Information theory is a very good argument against both abiogenesis and universal common descent. It strongly indicates that any such system that Is trying to produce complex specific information without existing complex specific information is doomed to failure. This is one of the reasons why evolutionists like to confuse the use of statistical information and complex specific information. Random processes can produce plenty of statistical information, but they cannot produce complex specific information, and there is a huge difference between the two.
