Evolutionary processes mutation

The evolutionary process of a genetic algorithm is accomplished by genetic operators which translate the evolutionary concepts of selection, recombination or crossover, and mutation into data processing to solve an optimization problem dynamically. Possible solutions to the problem are coded as so-called artificial chromosomes, which are changed and adapted throughout the optimization process until an optimrun solution is obtained. 

The primary sequence of proteins, with identical function varies within different species by natural mutations of amino acids. With increasing distance in the evolutionary process the number of variations between the sequences of proteins increase. 

The data in Figure 2.12 are the results of initial mutagenesis experiments, which does not yet constitute directed evolution. An evolutionary process was subsequently induced by combining the mutations of two improved mutants of the first round [46]. Thereby new mutants were obtained, which show an increase of activity relative to the WT by more than 2 orders of magnitude. Although enantioselectivity was not the... 

Evolutionary computation which is learned by watching population dynamics the most important programming are genetic algorithms which are inspired by the evolutionary processes of mutation, recombination, and natural selection in biology. 

The huge diversity of enzymes observed in nature is attributed to evolutionary processes where diverse populations of gene variants (with sequence variations generated by mutation and recombination) are subjected to selection of the fittest enzyme functions. Those genes which confer advantageous traits to their hosts are more likely to be maintained and disseminated in populations than those that do not. 

The MFCs for a species are coefficients for real trends of plant traits during the period studied. Numerically, it can have a value from 0.00 to 1.00 and can also be interpreted, if needed, as a percentage. The lower the MFC, the weaker the rate of micro-evolution for the period of time studied. A maximum value (1.00) for the MFC signifies that a micro-evolution (trait change) has occurred in all the plants studied and, therefore, macro-evolution has occurred in the population. In this case, the new hypothetical constant coefficient is valid (no trends, no mutations, no trajectories). The MFCs can be positive or negative. Positive MFCs show that the traits are developing in a micro-evolutionary process, and negative MFCs indicate the reverse. The MFCs represent very useful parameters for measurement of micro-evolution in plant species. 

The evolutionary tree shows sequential branching of species and proteins as a consequence of chance processes (mutations). 

Several attempts to describe replication-mutation networks by stochastic techniques were made in the past. We cannot discuss them in detail here, but we shall brieffy review some general ideas that are relevant for the quasispecies model. The approach that is related closest to our model has been mentioned already [51] the evolutionary process is viewed as a sequence of stepwise increases in the populations mean fitness. Fairly long, quasi-stationary phases are interrupted by short periods of active selection during which the mean fitness increases. The approach towards optimal adaptation to the environment is resolved in a manner that is hierarchical in time. Evolution taking place on the slow time scale represents optimization in the whole of the sequence space. It is broken up into short periods of time within which the quasi-species model applies only locally. During a single evolutionary step only a small part of sequence space is explored by the population. There, the actual distributions of sequences resemble local quasispecies confined to well-defined regions. Error thresholds can be defined locally as well. 

During the course of any evolutionary process, proteins become trapped in local energy minima. Dramatic moves, such as swaps and juxtaposition, are needed to break out of these regions. Dramatic moves are usually deleterious, however. The evolutionary success of these events depends on population size, generation time, mutation rate, population mixing, selective... 

The two characteristics required for protein native-state structures to be targets of an evolutionary process are stability and diversity. Stability is needed because one would not want to mutate away a DNA molecule able to code for a useful protein, and diversity is needed to allow evolution to build complex and versatile forms. The mechanism for natural selection arises naturally in this context DNA molecules that code for amino-acid sequences that fit well into one of these predetermined folds and have useful functionality thrive at the expense of molecules that create sequences that are not useful. Indeed, in this picture, sequences and functionality evolve in order to fit within the constraints of these folds, which, in turn, are immutable and determined by physical law. 

---