123

10

Understand Evolution Better Applying

the Computer

Abstract

The evolution of populations creates new species; the individual living being or protein

is, after all, determined within narrow limits by the specific genome. New populations

with ever new typical characteristics (through mutation and, in the case of sexual repro­

duction, through recombination) are always created, which allow an almost optimal

adaptation to the prevailing environment, less environmentally related characteristics

are less often passed on in the population (selection). However, many variants are also

neutral or new structures only appear abruptly when enough mutations are present

(neutral pathways in RNA structures; “punctuated equilibrium” according to Gould).

Phylogeny helps to infer the evolution of different species on the basis of shared or non-­

shared characteristics via calculated predecessors. There are faster (neighbor joining)

and more accurate methods (parsimony, most accurate maximum likelihood).

Accompanying sequence and secondary structure analyses reveal conserved and vari­

able regions as well as the evolution of functional domains. Most accurate phylogenetic

trees require much practice and systematic comparison of all available information

(e.g. alternative phylogenetic trees; marker proteins).

It is important for the understanding of evolution that it can only affect a whole population.

In this respect, I have an even more complex task before me here than describing a com­

plex individual system (Chap. 9): Cells or individual genes or proteins can change in an

individual in the course of life. But since this mostly affects somatic cells (“somatic muta­

tions”), they are not passed on to the next generation. But across generations, if you look

at a whole population, there are changes over time due to the numerous mutations that

happened in single genomes of germ cells and even passed on to new born individuals.

Generally, this results in the population being better adapted to the environment that is

© Springer-Verlag GmbH Germany, part of Springer Nature 2023

T. Dandekar, M. Kunz, Bioinformatics,

https://doi.org/10.1007/978-3-662-65036-3_10