The Lorentz-Dirac and Landau-Lifshitz equations from the perspective of modern renormalization theory

This paper uses elementary techniques drawn from renormalization theory to derive the Lorentz-Dirac equation for the relativistic classical electron from the Maxwell-Lorentz equations for a classical charged particle coupled to the electromagnetic field. I show that the resulting effective theory, valid for electron motions that change over distances large compared to the classical electron radius, reduces naturally to the Landau-Lifshitz equation. No familiarity with renormalization or quantum field theory is assumed

Investigating the effectiveness of different forms of mineral resources governance in meeting the objectives of the UK petroleum fiscal regime

After 40 years of oil investments, the UK is now a mature oil province. During these 40 years or so, the UK Government has changed the type of governance it uses to manage its petroleum resources. This paper introduces the theoretical background to two models of mineral resource governance: the so-called proprietorial and nonproprietorial regimes. It investigates the adoption of these two models by the UK Government and their effect on the overall tax take from the UK`s petroleum resources. The analysis tracks the changes in the UK petroleum taxation system since establishment up until 2010. It assesses how these tax changes have affected the overall petroleum average tax rate (ATR). The study concludes that the UK Government adopted a proprietorial type of mineral governance during the period 1975-1982, before changing to a non-proprietorial regime in the period 1983-2000. Since 2000 it has begun to move back towards a proprietorial style of governance. This change is still in its early stages, however; the evidence shows that although there has been an increase in fiscal revenues, this increase has been small

Boosting forward-time population genetic simulators through genotype compression

Background: Forward-time population genetic simulations play a central role in deriving and testing evolutionary hypotheses. Such simulations may be data-intensive, depending on the settings to the various param- eters controlling them. In particular, for certain settings, the data footprint may quickly exceed the memory of a single compute node. Results: We develop a novel and general method for addressing the memory issue inherent in forward-time simulations by compressing and decompressing, in real-time, active and ancestral genotypes, while carefully accounting for the time overhead. We propose a general graph data structure for compressing the genotype space explored during a simulation run, along with efficient algorithms for constructing and updating compressed genotypes which support both mutation and recombination. We tested the performance of our method in very large-scale simulations. Results show that our method not only scales well, but that it also overcomes memory issues that would cripple existing tools. Conclusions: As evolutionary analyses are being increasingly performed on genomes, pathways, and networks, particularly in the era of systems biology, scaling population genetic simulators to handle large-scale simulations is crucial. We believe our method offers a significant step in that direction. Further, the techniques we provide are generic and can be integrated with existing population genetic simulators to boost their performance in terms of memory usage

ncDNA and drift drive binding site accumulation

Background: The amount of transcription factor binding sites (TFBS) in an organism's genome positively correlates with the complexity of the regulatory network of the organism. However, the manner by which TFBS arise and accumulate in genomes and the effects of regulatory network complexity on the organism's fitness are far from being known. The availability of TFBS data from many organisms provides an opportunity to explore these issues, particularly from an evolutionary perspective. Results: We analyzed TFBS data from five model organisms -- E. coli K12, S. cerevisiae, C. elegans, D. melanogaster, A. thaliana -- and found a positive correlation between the amount of non-coding DNA (ncDNA) in the organismﾒs genome and regulatory complexity. Based on this finding, we hypothesize that the amount of ncDNA, combined with the population size, can explain the patterns of regulatory complexity across organisms. To test this hypothesis, we devised a genome-based regulatory pathway model and subjected it to the forces of evolution through population genetic simulations. The results support our hypothesis, showing neutral evolutionary forces alone can explain TFBS patterns, and that selection on the regulatory network function does not alter this finding. Conclusions: The cis-regulome is not a clean functional network crafted by adaptive forces alone, but instead a data source filled with the noise of non-adaptive forces. From a regulatory perspective, this evolutionary noise manifests as complexity on both the binding site and pathway level, which has significant implications on many directions in microbiology, genetics, and synthetic biology

Convergent evolution of modularity in metabolic networks through different community structures

Background: It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of themodularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. Results: In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxomony. Conclusions: We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organismﾒs metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new measures of modularity and network communities that better correspond to functional categorizations