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Why highly expressed proteins evolve slowly.
It is hypothesized that selection to reduce the burden of protein misfolding will favor protein sequences with increased robustness to translational missense errors, and genome-wide tests favor the translational robustness explanation over existing hypotheses that invoke constraints on function or translational efficiency.
The evolutionary origin of complex features
Findings show how complex functions can originate by random mutation and natural selection.
Evolution of digital organisms at high mutation rates leads to survival of the flattest
According to quasi-species theory, selection favours the cloud of genotypes, interconnected by mutation, whose average replication rate is highest, and this prediction is confirmed using digital organisms that self-replicate, mutate and evolve.
What is complexity?
  • C. Adami
  • Biology, Medicine
    BioEssays : news and reviews in molecular…
  • 1 December 2002
It is reasoned that factors such as mutation rates, sexual populations, and time-dependent landscapes usually help, rather than hinder, the evolution of complexity, and that a theory of physical complexity for co-evolving species will reveal an overall trend towards higher complexity in biological evolution.
Evolutionary Learning in the 2D Artificial Life System "Avida"
It is found that the spatial geometry is conducive to the development of diversity and thus improves adaptive capabilities of a new tierra-inspired artificial life system with local interactions and two-dimensional geometry based on an update mechanism akin to that of 2D cellular automata.
Open Problems in Artificial Life
This article lists fourteen open problems in artificial life, each of which is a grand challenge requiring a major advance on a fundamental issue for its solution. Each problem is briefly explained,
Evolution of Biological Complexity
It is shown that, because natural selection forces genomes to behave as a natural "Maxwell Demon," within a fixed environment, genomic complexity is forced to increase.
Introduction To Artificial Life
  • C. Adami
  • Computer Science
    IEEE Trans. Evol. Comput.
  • 19 December 1997
This project brings together the necessary theoretical groundwork for understanding the dynamics of systems of self-replicating information, as well as the results of initial experiments carried out with artificial living systems based on this paradigm.
Thermodynamic prediction of protein neutrality.
A simple theory that uses thermodynamic parameters to predict the probability that a protein retains the wild-type structure after one or more random amino acid substitutions is presented and provides a basis for interpreting the response of proteins to substitutions in protein engineering applications.
Physical complexity of symbolic sequences
A practical measure for the complexity of sequences of symbols (“strings”) is introduced that is rooted in automata theory but avoids the problems of Kolmogorov‐Chaitin complexity, and is applied to tRNA sequence data.