The Genetic coding style of digital organisms

Recently, all the human genes were identified. But understanding the functions coded in the genes is of course a much harder problem. We are used to view DNA as some sort of a computer code, but there are striking differences. For example, by using entropy, it has been shown that the DNA code is much closer to random code than written text, which in turn is less ordered than ordinary computer code. Instead of saying that the DNA is badly written, using common programming standards, we might say that it is written in a different style − an evolutionary style. In this paper the coding style of creatures from the artificial life platform Avida has been studied. Avida creatures that have evolved under different size merit methods and mutation rates have been analysed using the notion of stylistic measures. The analysis has shown that the evolutionary coding style depends on the environment in which the code evolved, and that the choice of size merit method and mutation probabilities affect different stylistic properties of the genome. A better understanding of Avida’s coding style, might eventually lead to insights of evolutionary codes in general

Does the Red Queen reign in the kingdom of digital organisms?

In competition experiments between two RNA viruses of equal or almost equal fitness, often both strains gain in fitness before one eventually excludes the other. This observation has been linked to the Red Queen effect, which describes a situation in which organisms have to constantly adapt just to keep their status quo. I carried out experiments with digital organisms (self-replicating computer programs) in order to clarify how the competing strains' location in fitness space influences the Red-Queen effect. I found that gains in fitness during competition were prevalent for organisms that were taken from the base of a fitness peak, but absent or rare for organisms that were taken from the top of a peak or from a considerable distance away from the nearest peak. In the latter two cases, either neutral drift and loss of the fittest mutants or the waiting time to the first beneficial mutation were more important factors. Moreover, I found that the Red-Queen dynamic in general led to faster exclusion than the other two mechanisms.Comment: 10 pages, 5 eps figure

Surface state engineering of molecule-molecule interactions

Engineering the electronic structure of organics through interface manipulation, particularly the interface dipole and the barriers to charge carrier injection, is of essential importance to improved organic devices. This requires the meticulous fabrication of desired organic structures by precisely controlling the interactions between molecules. The well-known principles of organic coordination chemistry cannot be applied without proper consideration of extra molecular hybridization, charge transer and dipole formation at the interfaces. Here we identify the interplay between energy level alignment, charge transfer, surface dipole and charge pillow effect and show how these effects collectively determine the net force between adsorbed porphyrin 2H-TPP on Cu(111). We show that the forces between supported porphyrins can be altered by controlling the amount of charge transferred across the interface accurately through the relative alignment of molecular electronic levels with respect to the Shockley surface state of the metal substrate, and hence govern the self-assembly of the molecules

Long-term experimental evolution decouples size and production costs in Escherichia coli

Body size covaries with population dynamics across life’s domains. Metabolism may impose fundamental constraints on the coevolution of size and demography, but experimental tests of the causal links remain elusive. We leverage a 60,000-generation experiment in which Escherichia coli populations evolved larger cells to examine intraspecific metabolic scaling and correlations with demographic parameters. Over the course of their evolution, the cells have roughly doubled in size relative to their ancestors. These larger cells have metabolic rates that are absolutely higher, but relative to their size, they are lower. Metabolic theory successfully predicted the relations between size, metabolism, and maximum population density, including support for Damuth’s law of energy equivalence, such that populations of larger cells achieved lower maximum densities but higher maximum biomasses than populations of smaller cells. The scaling of metabolism with cell size thus predicted the scaling of size with maximum population density. In stark contrast to standard theory, however, populations of larger cells grew faster than those of smaller cells, contradicting the fundamental and intuitive assumption that the costs of building new individuals should scale directly with their size. The finding that the costs of production can be decoupled from size necessitates a reevaluation of the evolutionary drivers and ecological consequences of biological size more generally.Dustin J. Marshall, Martino Malerba, Thomas Lines, Aysha L. Sezmis, Chowdhury M. Hasan, Richard E. Lenski, and Michael J. McDonal

On the Interplay between the Evolvability and Network Robustness in an Evolutionary Biological Network: A Systems Biology Approach

In the evolutionary process, the random transmission and mutation of genes provide biological diversities for natural selection. In order to preserve functional phenotypes between generations, gene networks need to evolve robustly under the influence of random perturbations. Therefore, the robustness of the phenotype, in the evolutionary process, exerts a selection force on gene networks to keep network functions. However, gene networks need to adjust, by variations in genetic content, to generate phenotypes for new challenges in the network’s evolution, ie, the evolvability. Hence, there should be some interplay between the evolvability and network robustness in evolutionary gene networks. In this study, the interplay between the evolvability and network robustness of a gene network and a biochemical network is discussed from a nonlinear stochastic system point of view. It was found that if the genetic robustness plus environmental robustness is less than the network robustness, the phenotype of the biological network is robust in evolution. The tradeoff between the genetic robustness and environmental robustness in evolution is discussed from the stochastic stability robustness and sensitivity of the nonlinear stochastic biological network, which may be relevant to the statistical tradeoff between bias and variance, the so-called bias/variance dilemma. Further, the tradeoff could be considered as an antagonistic pleiotropic action of a gene network and discussed from the systems biology perspective

Simulating Evolution’s First Steps

Abstract. We demonstrate a simple artificial chemistry environment in which two small evolutionary transitions from the simplest self-replicators to larger ones are observed. The replicators adapt to increasingly harsh environments, where they must synthesise the components they need for replication. The evolution of a biosynthetic pathway of increasing length is thus achieved, through the use of simple chemical rules for catalytic action.

Experimental evolution.

Experimental evolution is the study of evolutionary processes occurring in experimental populations in response to conditions imposed by the experimenter. This research approach is increasingly used to study adaptation, estimate evolutionary parameters, and test diverse evolutionary hypotheses. Long applied in vaccine development, experimental evolution also finds new applications in biotechnology. Recent technological developments provide a path towards detailed understanding of the genomic and molecular basis of experimental evolutionary change, while new findings raise new questions that can be addressed with this approach. However, experimental evolution has important limitations, and the interpretation of results is subject to caveats resulting from small population sizes, limited timescales, the simplified nature of laboratory environments, and, in some cases, the potential to misinterpret the selective forces and other processes at work