I have my own thoughts and response esp., on evolution and the arguments put forward in favor of it using genetics. In this post, I will address every argument put in favor of evolution using standard procedures and methodologies used in most software companies for designing, developing, testing and roll-out of software.
Know that the LORD, He [is] God; [It is] He [who] has made us, and not we ourselves; [We are ]His people and the sheep of His pasture.Ps 100:3
Genetic similarity of humans with primates
One of the most common answers I always find is the genetic similarity of humans with primates. This statement must be analyzed what it means, to understand what is assumed. The assumption here is the consideration of only the genes and the rest of the Junk DNA is useless. Hence, the genes (which are the only biologically significant) are identical up to 90%. A gene is the basic physical and functional unit of heredity. Genes, which are made up of DNA, act as instructions to make molecules called proteins. Every developer has a set of libraries. Most of the developers, don’t develop these libraries with common functions from scratch. They put all common functions in one dll or library and link it either statically or dynamically with every other program they require.
Hence, a gene is comparable to a function or set of functions, more like a library, a dll file, used in all the organisms that require it. Even though Microsoft Word and Excel loads the exact same set of common libraries and calls same functions, and has similar look and feel, they function differently and both are designed. Human and chimp using same genes for their function which is used as an evidence for evolution, is like saying Microsoft Word and Excel had a common ancestor and evolved because they share the same set of functions and load the same common libraries. Just because a dll is used in a program does not necessarily means the program will use all functions in that dll. A program will use only the functions it requires and the rest will never used. This is exactly why some genes are switched off or inactivated while others function. As we know operating systems came a long way, but does that have a common ancestor and evolved itself? Identical operating systems actually share code, libraries etc but they are always developed. Take Linux for example: They share code from Unix – Does that mean Linux evolved itself from Unix?
Hence, identical genes between species is actually an evidence for Designer at work rather than evolution.
C-value enigma or the Onion Test
Some organisms (like some amoebae, onions, some arthropods, and amphibians) have much more DNA per cell than humans, but cannot possibly be more developmentally or cognitively complex, implying that eukaryotic genomes can and do carry varying amounts of unnecessary baggage. If you look into the code of any developer, you might find unused functions, unused imports, unused libraries, debug and trace information. I personally have the habit of embedding the data within the code to make the code more portable. These things increase the size of the code but not it’s functionality. For example, some software doing simple stuff like a dictionary are 100 MB in size, while Stuxnet which brought down Iranian nuclear reactors is just half a megabyte. The size of the code is not directly proportional to it’s complexity, which every developer knows this. Any project manager would know that complexity does not depend on the number of code and does not estimate the costs based on lines of code required.
Measuring programming progress by lines of code is like measuring aircraft building progress by weight.Bill Gates
Murphy’s law on software engineering states, The chances of a program doing what it’s supposed to do is inversely proportional to the number of lines of code used to write it1. If this is the case for computer code designed and developed by humans, isn’t it a perfect evidence for a grand Designer at work, creating complex organisms with less genomes perfectly satisfying Murphy’s law?
There is an another logic which is even used in DOS and Windows. If there is a fixed size and you are to write in them, the remaining left over space is huge when the actual information is very less, which makes the overall size huge. The DOS and Windows file systems use fixed-size clusters. Even if the actual data being stored requires less storage than the cluster size, an entire cluster is reserved for the file. This is why when very less information like 1 byte on a million files will not be 1 MB in size but rather, 4 GB for a 4K cluster. Does genome has such features? We do know introns within a gene are removed by RNA splicing indicating some kind of cluster like structures.
Hence, C-value enigma or Onion test is actually an evidence for Designer at work, satisfying Murphy’s laws, cluster style design rather than evolution.
Reverse Engineering Genome
Most of the work biologists do today are just reverse engineering the genome, trying to understand it’s design and documenting it. Most decompilers provide data region, code region, functions used, the data string used and the entry point. However, none of these luxury exists in genome reverse engineering. All we have is a sequence of code when executed has a specific functions and codons to identify them. Most of the remaining genome, we have no clue. If you look into the structure of any executable file, without having any clue of the structure, you can quickly come to conclusion that most of the info in there is junk. But if you know the actual structure, it isn’t junk but actual data in them.
As you can see, the only readable info in the file is MZ. Apart from this, nothing is decode-able without actually having documentation on it.
The same logic is with genome. Just because a sequence of binary code is not a part of a function (or a gene), does not mean it is not used. If I want to be more accurate, a genome is not just a code, but much more to it. A genome is like a code that has instructions to build the computer itself and then install all the required operating systems and the application code and then finally make the computer boot execute the required code in the right order.
I would like to add more, but these are more than enough as it covers most of the arguments used in genetics. Only a designer can understand and detect the architecture of an another Designer. People who can’t detect design or understand the complex architecture do require some designing skills to actually understand them. If they can’t, it is better to atleast see the computer word and understand in order to relate and detect design concepts. Evolutionary biologists are not designers or developers which is understandable, but that does not mean they should reject on design concepts and ways to detect design. In 16 trillion bits in my computer, there is not a single 0 or 1 was by chance.
1 Wang, Yingxu. (2020) Software Engineering Foundations: A Software Science Perspective – Yingxu Wang – Google Books. Retrieved December 30, 2020, from https://books.google.com.au/books?id=W-k9eNQ5oj8C