Evidence of Common Descent - Evidence From Computation and Mathematical Iteration

Evidence From Computation and Mathematical Iteration

Computer science allows the iteration of self changing complex systems to be studied, allowing a mathematical understanding of the nature of the processes behind evolution; providing evidence for the hidden causes of known evolutionary events. The evolution of specific cellular mechanisms like spliceosomes that can turn the cell's genome into a vast workshop of billions of interchangeable parts that can create tools that create tools that create tools that create us can be studied for the first time in an exact way.

"It has taken more than five decades, but the electronic computer is now powerful enough to simulate evolution" assisting bioinformatics in its attempt to solve biological problems.

Computational evolutionary biology has enabled researchers to trace the evolution of a large number of organisms by measuring changes in their DNA, rather than through physical taxonomy or physiological observations alone. It has compared entire genomes permitting the study of more complex evolutionary events, such as gene duplication, horizontal gene transfer, and the prediction of factors important in speciation. It has also helped build complex computational models of populations to predict the outcome of the system over time and track and share information on an increasingly large number of species and organisms.

Future endeavors are to reconstruct a now more complex tree of life.

Christoph Adami, a professor at the Keck Graduate Institute made this point in Evolution of biological complexity:

To make a case for or against a trend in the evolution of complexity in biological evolution, complexity must be both rigorously defined and measurable. A recent information-theoretic (but intuitively evident) definition identifies genomic complexity with the amount of information a sequence stores about its environment. We investigate the evolution of genomic complexity in populations of digital organisms and monitor in detail the evolutionary transitions that increase complexity. We show that, because natural selection forces genomes to behave as a natural "Maxwell Demon", within a fixed environment, genomic complexity is forced to increase.

David J. Earl and Michael W. Deem—professors at Rice University made this point in Evolvability is a selectable trait:

Not only has life evolved, but life has evolved to evolve. That is, correlations within protein structure have evolved, and mechanisms to manipulate these correlations have evolved in tandem. The rates at which the various events within the hierarchy of evolutionary moves occur are not random or arbitrary but are selected by Darwinian evolution. Sensibly, rapid or extreme environmental change leads to selection for greater evolvability. This selection is not forbidden by causality and is strongest on the largest-scale moves within the mutational hierarchy. Many observations within evolutionary biology, heretofore considered evolutionary happenstance or accidents, are explained by selection for evolvability. For example, the vertebrate immune system shows that the variable environment of antigens has provided selective pressure for the use of adaptable codons and low-fidelity polymerases during somatic hypermutation. A similar driving force for biased codon usage as a result of productively high mutation rates is observed in the hemagglutinin protein of influenza A.

"Computer simulations of the evolution of linear sequences have demonstrated the importance of recombination of blocks of sequence rather than point mutagenesis alone. Repeated cycles of point mutagenesis, recombination, and selection should allow in vitro molecular evolution of complex sequences, such as proteins." Evolutionary molecular engineering, also called directed evolution or in vitro molecular evolution involves the iterated cycle of mutation, multiplication with recombination, and selection of the fittest of individual molecules (proteins, DNA, and RNA). Natural evolution can be relived showing us possible paths from catalytic cycles based on proteins to based on RNA to based on DNA.