Escalation of error catastrophe for enzymatic self-replicators

It is a long-standing question in origin-of-life research whether the information content of replicating molecules can be maintained in the presence of replication errors. Extending standard quasispecies models of non-enzymatic replication, we analyze highly specific enzymatic self-replication mediated through an otherwise neutral recognition region, which leads to frequency-dependent replication rates. We find a significant reduction of the maximally tolerable error rate, because the replication rate of the fittest molecules decreases with the fraction of functional enzymes. Our analysis is extended to hypercyclic couplings as an example for catalytic networks.Comment: 6 pages, 4 figures; accepted at Europhys. Let

Emergence of robust nucleosome patterns from an interplay of positioning mechanisms

Proper positioning of nucleosomes in eukaryotic cells is determined by a complex interplay of factors, including nucleosome-nucleosome interactions, DNA sequence, and active chromatin remodeling. Yet, characteristic features of nucleosome positioning, such as geneaveraged nucleosome patterns, are surprisingly robust across perturbations, conditions, and species. Here, we explore how this robustness arises despite the underlying complexity. We leverage mathematical models to show that a large class of positioning mechanisms merely affects the quantitative characteristics of qualitatively robust positioning patterns. We demonstrate how statistical positioning emerges as an effective description from the complex interplay of different positioning mechanisms, which ultimately only renormalize the model parameter quantifying the effective softness of nucleosomes. This renormalization can be species-specific, rationalizing a puzzling discrepancy between the effective nucleosome softness of S. pombe and S. cerevisiae. More generally, we establish a quantitative framework for dissecting the interplay of different nucleosome positioning determinants

Cellular Structures for Computation in the Quantum Regime

We present a new cellular data processing scheme, a hybrid of existing cellular automata (CA) and gate array architectures, which is optimized for realization at the quantum scale. For conventional computing, the CA-like external clocking avoids the time-scale problems associated with ground-state relaxation schemes. For quantum computing, the architecture constitutes a novel paradigm whereby the algorithm is embedded in spatial, as opposed to temporal, structure. The architecture can be exploited to produce highly efficient algorithms: for example, a list of length N can be searched in time of order cube root N.Comment: 11 pages (LaTeX), 3 figure

Asymptotic Level Density of the Elastic Net Self-Organizing Feature Map

Whileas the Kohonen Self Organizing Map shows an asymptotic level density following a power law with a magnification exponent 2/3, it would be desired to have an exponent 1 in order to provide optimal mapping in the sense of information theory. In this paper, we study analytically and numerically the magnification behaviour of the Elastic Net algorithm as a model for self-organizing feature maps. In contrast to the Kohonen map the Elastic Net shows no power law, but for onedimensional maps nevertheless the density follows an universal magnification law, i.e. depends on the local stimulus density only and is independent on position and decouples from the stimulus density at other positions.Comment: 8 pages, 10 figures. Link to publisher under http://link.springer.de/link/service/series/0558/bibs/2415/24150939.ht

Venn diagrams may indicate erroneous statistical reasoning in transcriptomics

A common application of differential expression analysis is finding genes that are differentially expressed upon treatment in only one out of several groups of samples. One of the approaches is to test for significant difference in expression between treatment and control separately in the two groups, and then select genes that show statistical significance in one group only. This approach is then often combined with a gene set enrichment analysis to find pathways and gene sets regulated by treatment in only this group. Here we show that this procedure is statistically incorrect and that the interaction between treatment and group should be tested instead. Moreover, we show that gene set enrichment analysis applied to such incorrectly defined genes group-specific genes may result in misleading artifacts. Due to the presence of false negatives, genes significant in one, but not the other group are enriched in gene sets which correspond to the overall effect of the treatment. Thus, the results appear related to the problem at hand, but do not reflect the group-specific effect of a treatment. A literature search revealed that more than a quarter of papers which used a Venn diagram to illustrate the results of separate differential analysis have also applied this incorrect reasoning