Are there laws of genome evolution?

Research in quantitative evolutionary genomics and systems biology led to the discovery of several universal regularities connecting genomic and molecular phenomic variables. These universals include the log-normal distribution of the evolutionary rates of orthologous genes; the power law-like distributions of paralogous family size and node degree in various biological networks; the negative correlation between a gene's sequence evolution rate and expression level; and differential scaling of functional classes of genes with genome size. The universals of genome evolution can be accounted for by simple mathematical models similar to those used in statistical physics, such as the birth-death-innovation model. These models do not explicitly incorporate selection, therefore the observed universal regularities do not appear to be shaped by selection but rather are emergent properties of gene ensembles. Although a complete physical theory of evolutionary biology is inconceivable, the universals of genome evolution might qualify as 'laws of evolutionary genomics' in the same sense 'law' is understood in modern physics.Comment: 17 pages, 2 figure

Calculation of exciton densities in SMMC

We develop a shell-model Monte Carlo (SMMC) method to calculate densities of states with varying exciton (particle-hole) number. We then apply this method to the doubly closed-shell nucleus 40Ca in a full 0s-1d-0f-1p shell-model space and compare our results to those found using approximate analytic expressions for the partial densities. We find that the effective one-body level density is reduced by approximately 22% when a residual two-body interaction is included in the shell model calculation.Comment: 10 pages, 4 figure

Monte Carlo Simulation of Quantum Computation

The many-body dynamics of a quantum computer can be reduced to the time evolution of non-interacting quantum bits in auxiliary fields by use of the Hubbard-Stratonovich representation of two-bit quantum gates in terms of one-bit gates. This makes it possible to perform the stochastic simulation of a quantum algorithm, based on the Monte Carlo evaluation of an integral of dimension polynomial in the number of quantum bits. As an example, the simulation of the quantum circuit for the Fast Fourier Transform is discussed.Comment: 12 pages Latex, 2 Postscript figures, to appear in Proceedings of the IMACS (International Association for Mathematics and Computers in Simulation) Conference on Monte Carlo Methods, Brussels, April 9