186,419 research outputs found
On the interaction of replication and transcriptional regulation
No full textDNA replication introduces a gradient of gene copy numbers, and in Bacteria it affects gene expression accordingly. In E. coli and other species, the slope of the gradient averaged over the population can be predicted on the basis of its relationship with growth rate. In this work we integrated this growth- and position-dependent gradient into a classical transcriptional regulation model, to highlight their interaction. The theoretical treatment of our model highlights that the sensitivity to transcription factor-mediated regulations acquires an additional dimension related to the position of a locus on the oriter axis and to division time. This reinforces the idea of replication as an additional layer in gene regulation. We highlight here that replication- and transcription factor-mediated regulations can in theory work in concert or counteract each other, and we discuss why this is important from an evolutionary point of view with respect to both steady state transcript abundance and its variance across conditions. Finally, we note that this treatment may improve the estimation of kinetic parameters for transcription factor activity using RNA-seq data, and the estimation of the dispersion factor in differential gene expression analysis when division time across conditions changes significantly
GEMO: grammatical evolution memory optimization system
No full textIn Grammatical Evolution (GE) individuals occupy more space than required, that is, the Actual Length of the individuals is longer than their Effective Length. This has major implications for scaling GE to complex problems that demand larger populations and complex individuals. We show how these two lengths vary for different sizes of population, demonstrating that Effective Length is relatively independent of population size, but that the Actual Length is proportional to it. We introduce Grammatical Evolution Memory Optimization (GEMO), a two-stage evolutionary system that uses a multi-objective approach to identify the optimal, or at least, near-optimal, genome length for the problem being examined. It uses a single run with a multi-objective fitness function defined to minimize the error for the problem being tackled along with maximizing the ratio of Effective to Actual Genome Length leading to better utilization of memory and hence, computational speedup. Then, in Stage 2, standard GE runs are performed restricting the genome length to the length obtained in Stage 1. We demonstrate this technique on different problem domains and show that in all cases, GEMO produces individuals with the same fitness as standard GE but significantly improves memory usage and reduces computation time. </p
A new selective force driving metabolic gene clustering
No full textThe evolution of operons has puzzled evolutionary biologists since their discovery, and many theories exist to explain their emergence, spreading, and evolutionary conservation. In this work, we suggest that DNA replication introduces a selective force for the clustering of functionally related genes on chromosomes, which we interpret as a preliminary and necessary step in operon formation. Our reasoning starts from the observation that DNA replication produces copy number variations of genomic regions, and we propose that such changes perturb metabolism. The formalization of this effect by exploiting concepts from metabolic control analysis suggests that the minimization of such perturbations during evolution could be achieved through the formation of gene clusters and operons. We support our theoretical derivations with simulations based on a realistic metabolic network, and we confirm that present-day genomes have a degree of compaction of functionally related genes, which is significantly correlated to the proposed perturbations introduced by replication. The formation of clusters of functionally related genes in microbial genomes has puzzled microbiologists since their first discovery. Here, we suggest that replication, and the copy number variations due to the replisome passage, might play a role in the process through a perturbation in metabolite homeostasis. We provide theoretical support to this hypothesis, and we found that both simulations and genomic analysis support our hypothesis. IMPORTANCE: The formation of clusters of functionally related genes in microbial genomes has puzzled microbiologists since their discovery. Here, we suggest that replication, and the copy number variations due to the replisome passage, might play a role in the process through a perturbation in metabolite homeostasis. We provide theoretical support to this hypothesis, and we found that both simulations and genomic analysis support our hypothesis
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
A new selective force driving metabolic gene clustering
No full textABSTRACT The evolution of operons has puzzled evolutionary biologists since their discovery, and many theories exist to explain their emergence, spreading, and evolutionary conservation. In this work, we suggest that DNA replication introduces a selective force for the clustering of functionally related genes on chromosomes, which we interpret as a preliminary and necessary step in operon formation. Our reasoning starts from the observation that DNA replication produces copy number variations of genomic regions, and we propose that such changes perturb metabolism. The formalization of this effect by exploiting concepts from metabolic control analysis suggests that the minimization of such perturbations during evolution could be achieved through the formation of gene clusters and operons. We support our theoretical derivations with simulations based on a realistic metabolic network, and we confirm that present-day genomes have a degree of compaction of functionally related genes, which is significantly correlated to the proposed perturbations introduced by replication. The formation of clusters of functionally related genes in microbial genomes has puzzled microbiologists since their first discovery. Here, we suggest that replication, and the copy number variations due to the replisome passage, might play a role in the process through a perturbation in metabolite homeostasis. We provide theoretical support to this hypothesis, and we found that both simulations and genomic analysis support our hypothesis.IMPORTANCEThe formation of clusters of functionally related genes in microbial genomes has puzzled microbiologists since their discovery. Here, we suggest that replication, and the copy number variations due to the replisome passage, might play a role in the process through a perturbation in metabolite homeostasis. We provide theoretical support to this hypothesis, and we found that both simulations and genomic analysis support our hypothesis
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Second workshop on Greenhouse construction and design
No full text[Notes_IRSTEA]graph., tabl., sch., bibl.International audienceTransmission radiative. Nouvelles structures et nouveaux matériels. Résistance et normalisation. Aération et microclimat
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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