Literature

A simulation model of early biological evolution has been developed which claims to advance our knowledge of how the first gene families developed. Although we know very little about how the genotype relates to phenotype, it was necessary for the architects to build something about this into their model. They postulated that the death rate of an organism is determined by the stability of the least stable of their proteins. Initial conditions were as follows: "Our evolution dynamics runs start from an initial population of 100 organisms, each having the same one primordial gene in their genomes. Initial gene sequence is random."
The simulation was executed using a cycle of 4 steps: "(i) random mutation of a nucleotide in a randomly selected gene with constant rate m per unit time per DNA length; mutations leading to the stop codon are rejected to ensure the constant length of protein sequences; (ii) duplication of a randomly selected gene within an organism's genome with constant rate u; (iii) birth of an organism via duplication of an already existing organism with constant rate b (the genome is copied exactly); and (iv) death of an organism with the rate d per unit time."
After running the model, the number of genes increased, but never exceeded 10. "We found that out of 50 simulation runs starting with different starting sequences, 27 runs successfully resulted in a steady exponential growth of the population, whereas in 23 runs the population has quickly gone extinct." Numerous characteristics of the simulations are reported, with this comment: "Based on these observations, we conjecture that biological evolution, exponential population growth, and existence of stable genomes are possible only after the discovery of a narrow set of specific protein structures."
Their general conclusion: "Together, these results and their analysis suggest a plausible comprehensive scenario of emergence of the protein universe in early biological evolution."
One of the principles of simulation is that models are first verified (they perform as they were designed) and second validated (they provide a realistic model of the real world). Unfortunately, there is nothing in the paper about validation. This does raise concerns. The first relates to the descriptor: "advantageous": "We find that exponential population growth is possible only after the discovery of a very small number of specific advantageous protein structures." The problem is: what gives the protein structure an advantage? In the real world, proteins are advantageous because they do something useful by virtue of the way they fold. In the model, the proteins do not have functions and the only significant differences relate to their stability.
The model starts with organisms possessing 1 gene and, after the simulation of biological evolution, they never have more than 10. Compare this with the real world, where a minimal genome of less than 200 genes has to be regarded as a dream.
The authors correctly state: "Our model of natural selection is minimalistic and is limited in its scope". Also: "This work is in progress." The authors suggest their model relates more closely to viruses: "our model can be directly applicable to (and can be experimentally tested on) the evolution of RNA viruses, which often encode for a handful of proteins, all of which are essential for the virus." This is far more realistic. If the authors had focussed on this rather than making grandiose claims about modelling early biological evolution, their work would deserve more respect.

A First-Principles Model of Early Evolution: Emergence of Gene Families, Species, and Preferred Protein Folds
Konstantin B. Zeldovich, Peiqiu Chen, Boris E. Shakhnovich, Eugene I. Shakhnovich
PloS Computational Biology, 3(7): e139 doi:10.1371/journal.pcbi.0030139

Abstract: In this work we develop a microscopic physical model of early evolution where phenotype - organism life expectancy - is directly related to genotype - the stability of its proteins in their native conformations - which can be determined exactly in the model. Simulating the model on a computer, we consistently observe the "Big Bang" scenario whereby exponential population growth ensues as soon as favorable sequence-structure combinations (precursors of stable proteins) are discovered. Upon that, random diversity of the structural space abruptly collapses into a small set of preferred proteins. We observe that protein folds remain stable and abundant in the population at timescales much greater than mutation or organism lifetime, and the distribution of the lifetimes of dominant folds in a population approximately follows a power law. The separation of evolutionary timescales between discovery of new folds and generation of new sequences gives rise to emergence of protein families and superfamilies whose sizes are power-law distributed, closely matching the same distributions for real proteins. On the population level we observe emergence of species-subpopulations that carry similar genomes. Further, we present a simple theory that relates stability of evolving proteins to the sizes of emerging genomes. Together, these results provide a microscopic first-principles picture of how first-gene families developed in the course of early evolution.